JP5435101B2 - Vehicle driving support device and vehicle driving support method - Google Patents

Description

  The present invention relates to a vehicle driving support device that supports a driver's operation.

  A conventional vehicle driving support device learns a surplus time at the time when deceleration is started when approaching a preceding vehicle, and generates an alarm based on the learned value (see, for example, Patent Document 1). This device estimates the driver's predicted time from the margin time at the start of the deceleration operation, and issues a warning when the predicted value of the inter-vehicle distance after the estimated predicted time becomes smaller than the warning distance.

JP-A-7-159525 JP 2005-71184 A

  The conventional devices as described above detect the driving characteristics of the driver based on the allowance time at the start of the deceleration operation, and generate an alarm when an unsafe state is detected. If reliable detection is not performed due to noise generated due to a difference between the same drivers or variations within the same driver (intra-individual differences), there is a problem in that the driver's persuasiveness to alarms is reduced.

A vehicle driving support apparatus according to the present invention includes a driving state detection unit that detects a driving state of a vehicle, a driving operation detection unit that detects a driving operation by a driver, a driving state and a driving operation detected by the driving state detection unit. Based on the driving operation detected by the detection means, as a parameter for driving diagnosis, a parameter distribution representing the driver's safety awareness and driving preference characteristics is detected , and the driving for diagnosing the driver's driving from the detected index Evaluation criteria including a diagnostic means and a driving evaluation result obtained by the driving diagnostic means including a first evaluation standard based on the driving state of the driver and a second evaluation standard based on the normal driving state of the driver. Information setting means for setting the contents of information provision to the driver by evaluating according to the above, and information providing means for providing information to the driver with the contents set by the information setting means; Provided.
A vehicle driving support apparatus according to the present invention includes a driving state detection unit that detects a driving state of a vehicle, a driving operation detection unit that detects a driving operation by a driver, a driving state and a driving operation detected by the driving state detection unit. Driving diagnosis means for detecting the smoothness and / or stability of the driving operation as an index for driving diagnosis based on the driving operation detected by the detecting means, and diagnosing the driving of the driver from the detected index; Evaluating the driving diagnosis result by the driving diagnosis means according to an evaluation criterion including a first evaluation criterion based on the driving state of the driver of the day and a second evaluation criterion based on the normal driving state of the driver. Thus, the information setting means for setting the content of information provision to the driver and the information provision means for providing information to the driver with the content set by the information setting means are provided.

The vehicle driving support method according to the present invention detects a driving state of a vehicle, detects a driving operation by the driver, and based on the detected driving state and the driving operation, as an index for driving diagnosis , Detecting the distribution of parameters representing safety awareness and driving preference characteristics, diagnosing the driver's driving from the detected index , and driving evaluation results based on the first evaluation criteria and driving based on the driver's driving condition of the day The content of information provision to the driver is set by evaluating according to the evaluation criteria including the second evaluation criterion based on the driver's normal driving state, and the information is provided to the driver with the set content.
A vehicle driving support method according to the present invention detects a driving state of a vehicle, detects a driving operation by a driver, and uses the detected driving state as an index for driving diagnosis based on the detected driving state and driving operation. The smoothness and / or stability is detected, the driver's driving is diagnosed from the detected index, and the driving diagnosis result is determined based on the first evaluation standard based on the driving state of the driver and the driver's normal By performing evaluation according to the evaluation criteria including the second evaluation criteria based on the driving state, the content of information provision to the driver is set, and the information is provided to the driver with the set content.

  According to the present invention, since the information providing content is set by evaluating the driving diagnosis result according to the evaluation criteria, it is possible to provide information with high driver's satisfaction.

1 is a system diagram of a vehicle driving support apparatus according to a first embodiment of the present invention. The block diagram of the vehicle carrying the driving assistance device for vehicles shown in FIG. The flowchart which shows the process sequence of the driving assistance control program in 1st Embodiment. The figure explaining a data structure. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. (A)-(d) The figure explaining the calculation method of a deviation degree. (A) (b) The figure explaining the calculation method of the departure degree using margin time TTC. The figure which shows an example of visual information presentation. The figure which shows another example of visual information presentation. The system diagram of the driving assistance device for vehicles by a 2nd embodiment of the present invention. The flowchart which shows the process sequence of the driving assistance control program in 2nd Embodiment. The figure which shows an example of the time change of lane departure time. The figure which shows an example of frequency distribution of lane departure time. The figure which shows the example of a change of lane departure time. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The flowchart which shows the process sequence of the driving assistance control program in 3rd Embodiment. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The figure which shows an example of visual information presentation. The figure which shows another example of visual information presentation. (A) (b) The figure explaining the calculation method of the departure degree using a limit vehicle speed. The system diagram of the driving assistance device for vehicles by a 4th embodiment of the present invention. The block diagram of the vehicle carrying the vehicle driving assistance device shown in FIG. The flowchart which shows the process sequence of the driving assistance control program in 4th Embodiment. The figure explaining the structure of a traveling road database. The figure explaining a data structure. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. (A) (b) The figure explaining inter-vehicle time THW distribution and normal distribution. (A)-(d) The figure explaining the calculation method of a deviation degree. (A) (b) The figure explaining the calculation method of the deviation degree of "this time" with respect to "this day". The figure which shows an example of visual information presentation. The figure which shows another example of visual information presentation. The flowchart which shows the process sequence of the driving assistance control program in 5th Embodiment. The figure explaining the data structure in 5th Embodiment. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The figure which shows an example of visual information presentation. The flowchart which shows the process sequence of the driving assistance control program in 6th Embodiment. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The figure explaining the setting method of a bin. (A) (b) The figure explaining the setting method of the initial value put into a bin. The figure explaining the deviation degree calculation method using the mode value. The system diagram of the driving assistance device for vehicles by a 7th embodiment of the present invention. The flowchart which shows the process sequence of the driving assistance control program in 7th Embodiment. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The figure which shows an example of visual information presentation. The figure which shows another example of visual information presentation. The flowchart which shows the process sequence of the driving assistance control program in 8th Embodiment. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The flowchart which shows the process sequence of the driving assistance control program in 9th Embodiment. The flowchart which shows the process sequence of a driver driving | operation diagnosis process. The system diagram of the driving assistance device for vehicles by a 10th embodiment of the present invention. The flowchart which shows the process sequence of the driving assistance control program in 10th Embodiment. The figure explaining the symbol utilized for steering angle entropy calculation. The flowchart explaining the process sequence of the calculation process of steering angle entropy. The figure which shows the division of a steering angle. The figure which shows the example of a classification | category of a long-term steering angle entropy calculation result. The figure which shows the example of the relationship between long-term steering angle entropy classification | category and alerting | reporting content. The flowchart which shows the process sequence of the driving assistance control program in the modification 1 of 10th Embodiment. The flowchart which shows the process sequence of the driving assistance control program in the modification 1 of 10th Embodiment. The flowchart which shows the calculation process procedure of (alpha) value of a reference | standard state. The figure which shows the classification | category of a steering error. The figure which shows the frequency distribution of a steering error. The figure which shows the example of a classification | category of a middle period steering angle entropy calculation result. The figure which shows the example of the relationship between middle period steering angle entropy classification | category and alerting | reporting content. The figure which shows the example of a classification | category of the comparison result of the middle period steering angle entropy before and the measured middle period steering angle entropy. The figure which shows the relationship between the classification | category of the comparison result with the middle period steering angle entropy measured before and alerting | reporting content. The figure which shows the example of a classification | category of the calculation result of steering angle error distribution alpha value. The figure which shows the example of the relationship between the classification | category of steering angle error distribution alpha value, and alerting | reporting content. The flowchart which shows the process sequence of the driving assistance control program in the modification 2 of 10th Embodiment. The figure which shows the example of a classification | category of a short period steering angle entropy calculation result. The figure which shows the example of the relationship between a short period steering angle entropy classification | category and alerting | reporting content. The flowchart which shows the calculation process sequence of the steering angle entropy in 11th Embodiment. 7 is a flowchart for explaining a method of recursively classifying a probability using steering angle estimation error data. The flowchart which shows the calculation process procedure of (alpha) value of a reference | standard state. The flowchart explaining the method of calculating | requiring a frequency distribution recursively using steering angle estimation error data. The system figure of the driving assistance device for vehicles by a 12th embodiment of the present invention. The flowchart which shows the process sequence of the driving assistance control program in 12th Embodiment. The flowchart which shows the calculation process sequence of an accelerator pedal opening entropy. The figure which shows the example of classification | category of a long-term accelerator pedal opening entropy calculation result. The figure which shows the example of the relationship between long-time accelerator pedal opening entropy classification | category and alerting | reporting content. The flowchart which shows the process sequence of the driving assistance control program in the modification 1 of 12th Embodiment. The flowchart which shows the process sequence of the driving assistance control program in the modification 1 of 12th Embodiment. The flowchart explaining the calculation method of the αap value of the reference state. The figure which shows the example of a classification | category of the middle period accelerator pedal opening entropy calculation result. The figure which shows the example of the relationship between middle period accelerator pedal opening entropy classification | category and alerting | reporting content. The figure which shows the example of a classification | category of the comparison result of the middle period accelerator pedal opening entropy before and the measured middle period accelerator pedal opening entropy. The figure which shows the example of the relationship between the classification | category of the comparison result of the middle period accelerator pedal opening entropy measured previously, and alerting | reporting content. The figure which shows the example of a classification | category of an accelerator pedal opening error distribution alpha value calculation result. The figure which shows the example of the relationship between an accelerator pedal opening error distribution alpha value classification | category and alerting | reporting content. The flowchart which shows the process sequence of the driving assistance control program in the modification 2 of 12th Embodiment. The figure which shows the example of a classification | category of a short-term accelerator pedal opening entropy calculation result. The figure which shows the example of the relationship between a short period accelerator pedal opening entropy classification | category and alerting | reporting content. The system diagram of the driving assistance device for vehicles by a 13th embodiment of the present invention. The figure explaining the relationship between the separate parameter | index for driving diagnosis, and the aim of a detection. The flowchart which shows the process sequence of the driving assistance control program in 13th Embodiment.

<< First Embodiment >>
The vehicle driving support apparatus according to the first embodiment of the present invention will be described below. FIG. 1 is a system diagram showing a configuration of a vehicle driving support device 4 according to the first embodiment, and FIG. 2 is a configuration diagram of a vehicle on which the vehicle driving support device 4 is mounted.

First, the configuration of the vehicle driving support device 4 will be described.
The laser radar 10 is attached to a front grill part or a bumper part of the vehicle, and scans the front area of the host vehicle by irradiating infrared light pulses in the horizontal direction. The laser radar 10 measures the reflected wave of the infrared light pulse reflected by a plurality of reflectors in front (usually the rear end of the preceding vehicle), and determines the distance between the vehicles from the arrival time of the reflected wave to the plurality of obstacles. Detect distance and relative speed respectively. The detected inter-vehicle distance and relative speed are output to the controller 300. The forward area scanned by the laser radar 10 is about ± 6 deg with respect to the front of the host vehicle, and a forward object existing in this range is detected.

  The vehicle speed sensor 30 detects the vehicle speed of the host vehicle by measuring the number of rotations of the wheels and the number of rotations on the output side of the transmission, and outputs the detected host vehicle speed to the controller 300.

  The navigation system 50 includes a GPS receiver, a map database, a display monitor, and the like, and is a system that performs route search, route guidance, and the like. The navigation system 50 can acquire information such as the type of road on which the host vehicle is running and the road width based on the current position of the host vehicle obtained from the GPS receiver and the road information stored in the map database.

  The accelerator pedal stroke sensor 55 detects, for example, the stroke amount of the accelerator pedal (accelerator pedal operation amount) converted into the rotation angle of the servo motor via the link mechanism, and outputs it to the controller 300. The brake pedal stroke sensor 60 detects the amount of depression (the amount of brake pedal operation) when the driver depresses the brake pedal. The brake pedal stroke sensor 60 outputs the detected brake pedal operation amount to the controller 300. The winker switch 65 detects the presence or absence of a winker lever operation by the driver and outputs a detection signal to the controller 300.

  The controller 300 is an electronic control unit composed of a CPU and CPU peripheral components such as a ROM and a RAM, and controls the entire vehicle driving support apparatus 4. The controller 300 analyzes the driving characteristics of the driver based on signals input from the laser radar 10, the vehicle speed sensor 30, the navigation system 50, the accelerator pedal stroke sensor 55, the brake pedal stroke sensor 60, the blinker switch 65, and the like. Perform driving diagnosis. Then, information is provided to the driver based on the driving diagnosis result. Information provided to the driver includes warnings to the driver and suggestions for improving driving operations. Specific control contents in the controller 300 will be described later.

  The speaker 130 provides information to the driver by a buzzer sound or voice according to a signal from the controller 300. The display unit 180 performs display so as to give an alarm or improvement suggestion to the driver according to a signal from the controller 300. The display unit 180 can use, for example, a display monitor or a combiometer of the navigation system 50.

Next, the operation of the vehicle driving support apparatus 4 according to the first embodiment will be described. First, the outline will be described.
The controller 300 performs a driving diagnosis of the driver based on the traveling state of the host vehicle and the driving operation of the driver, and gives an alarm to the driver and suggests an improvement of the driving operation according to the driving diagnosis result. Specifically, a driving characteristic when the accelerator pedal is released from a state where the host vehicle is following the preceding vehicle is detected, and a driving diagnosis is performed using the detected driving characteristic as an index. And if the driver is driving at a higher risk than normal driving from the results of the driving diagnosis, that is, if the driver's driving deviates in the high risk direction, an alarm is given and a high risk is Notify the driver before falling into a bad state. On the other hand, if the driver's driving is better than the general standard from the driving diagnosis result, the information is presented, that is, the improvement is suggested, so that the driver's driving is given up so as to further improve the safety driving awareness.

  As described above, the vehicle driving support apparatus 4 according to the first embodiment has a function of detecting the driving of the driver by driving diagnosis, a function of giving an alarm to the driver according to the detection result, and a response of the detection result. The driver has three functions to give improvement suggestions to the driver. The driver knows his / her driving characteristics objectively and encourages the driver to introspect and presents advice according to the driving characteristics to the driver. Can learn how to drive lower risk. In the seventh embodiment, in particular, a driving diagnosis relating to the driving operation in the front-rear direction of the driver is performed.

  The operation of the vehicle driving support apparatus 4 according to the first embodiment will be described in detail with reference to FIG. FIG. 3 is a flowchart of a processing procedure of the driving support control process in the controller 300 according to the first embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S100, the traveling state of the host vehicle is detected. Here, as the running state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10 are acquired. In step S102, the operation state of the driver is detected. Here, as an operation state of the driver, an accelerator pedal operation amount detected by the accelerator pedal stroke sensor 55, a brake pedal operation amount detected by the brake pedal stroke sensor 60, and a winker lever operation amount detected by the winker switch 65. Get presence / absence.

In step S104, a margin time TTC and an inter-vehicle time THW between the host vehicle and the preceding vehicle are calculated in order to determine a travel scene of the host vehicle, which will be described later. The margin time TTC is a physical quantity indicating the current degree of proximity of the host vehicle with respect to the preceding vehicle. The margin time TTC indicates how many seconds later the inter-vehicle distance D becomes zero and the host vehicle and the preceding vehicle come into contact with each other when the current traveling state continues, that is, when the host vehicle speed V and the relative vehicle speed Vr are constant. It is a value and is calculated | required by the following (Formula 1).
TTC = D / Vr (Formula 1)

The inter-vehicle time THW is an effect when it is assumed that the degree of influence on the margin time TTC due to a change in the vehicle speed of the assumed vehicle ahead, that is, the relative vehicle speed Vr changes when the host vehicle is following the preceding vehicle. It is a physical quantity indicating the degree. The inter-vehicle time THW is obtained by dividing the inter-vehicle distance D by the own vehicle speed V, and indicates the time until the own vehicle reaches the current position of the preceding vehicle. The inter-vehicle time THW is obtained from the following (Equation 2).
THW = D / V (Formula 2)

  In step S106, it is determined whether or not an accelerator pedal off operation has been performed. For example, if the current accelerator pedal operation amount detected in step S102 is substantially zero, and it is detected that the accelerator pedal is released from the depressed state, the process proceeds to step S110. If the accelerator pedal is depressed, this process is terminated. In the following description, the operation of releasing the accelerator pedal that has been depressed is referred to as an accelerator pedal off operation, and the time when the accelerator pedal is released is referred to as an accelerator pedal off time.

  In step S110, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only in a driving scene in which the accelerator pedal is released from a state in which the host vehicle is stably following the same preceding vehicle.

An example of conditions for a stable follow-up driving scene is as follows.
(A) Following the same preceding vehicle (for example, the inter-vehicle distance has not changed by more than 4 m from the previous measured value)
(B) Not in a sudden approaching state (for example, margin time TTC exceeds 10 seconds)
(C) The inter-vehicle time THW is within a predetermined value (for example, the inter-vehicle time THW is less than 4 seconds)
(D) No brake operation by the driver (for example, the brake pedal operation amount is substantially 0)
(E) There is no blinker lever operation by the driver (for example, there is no ON signal input from the blinker switch 65).
(F) The states (a) to (e) are continuing (for example, continuing for 5 seconds or more).

  When all of the conditions (a) to (f) are satisfied, it is determined that the traveling scene of the host vehicle is a stable follow-up traveling scene, and the process proceeds to step S112 to perform driving diagnosis. On the other hand, if any of the conditions (a) to (f) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing driving diagnosis. The conditions for determining whether or not a stable follow-up driving scene is not limited to the above (a) to (f), and the presence or absence of a brake operation or the presence or absence of a blinker lever operation is detected separately. It is also possible to detect by means.

  In step S112, the travel location is determined. Specifically, an index number is labeled on the link ID described in the map information of the navigation system 50 based on the database. The link ID is an ID assigned to a link that connects nodes that are attribute change points at which road attributes change, and each link has data such as road type and link length (distance between nodes). doing. In step S114, the current time is recorded.

  In step S116, based on the labeling results of steps S112 and S114, data used for performing a driving diagnosis of the driver is stored. Here, for example, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the driving characteristic index in the link, the number of travels of the link, and the like are written in the structure format, and the travel road database is constructed. In the first embodiment, a surplus time TTC when the accelerator pedal is released is used as a physical quantity representing the driving characteristics of the driver. The calculation method of the driving characteristic and the driving characteristic index will be described in detail in the driving diagnosis process.

  In subsequent step S120, the driver is diagnosed for driving using the data stored in step S116. The driving diagnosis is performed based on the driving characteristics of the driver when the accelerator pedal is released from the state where the host vehicle is stably following the preceding vehicle. Driving characteristics during follow-up traveling include, for example, the inter-vehicle time THW between the host vehicle and the preceding vehicle, the reciprocal of the inter-vehicle time THW, the margin time TTC between the host vehicle and the preceding vehicle, the inter-vehicle distance, and the inverse of the inter-vehicle distance. In the seventh embodiment, the case where the margin time TTC at the time of releasing the accelerator pedal is used will be described as an example.

  The allowance time TTC when the accelerator pedal is released is robustly calculated from the data of the inter-vehicle distance D and the relative speed Vr before and after the accelerator pedal is turned off using a filter in both the front and rear directions.

  FIG. 4 shows the data structure of the vehicle driving support device 4. The A layer represents the data amount of “at this time” for a relatively short time indicating the current driving state of the driver. The B layer represents the data amount of “this day” indicating the driving state of the driver for the day for a longer time than “at this time”. The C layer represents a driver's normal driving state, that is, a “normal” data amount indicating personal characteristics, which is longer than “this day”. The D layer represents an amount of data indicating “general public” driving characteristics for diagnosing each driver's driving in comparison with a general driver.

  The amount of data increases as the layer moves from the A layer to the D layer. The amount of data included in each layer corresponds to the number of samples when calculating the average value of the inter-vehicle time THW at “this time”, “this day”, and “normal”, and by changing the number of samples, as shown in FIG. The data structure is realized. The numerical value of the data included in each layer is updated as needed by real-time calculation described below.

  In the driving diagnosis process, driving in different time spans, that is, driving at this time, “this day”, and “ordinary” is detected using the data of each of the A layer to D layer. The driving diagnosis process executed in step S120 will be described in detail using the flowchart of FIG.

  In step S122, in order to perform the driving diagnosis of “at this time” of the driver, the driving characteristic value of the driver at this time is calculated. As a driver's driving characteristic value, an average value Mean_x (n) and a standard deviation Stdev_x (n) of an allowance time TTC when the accelerator pedal is off within a predetermined time defining “at this time” are calculated. Here, the predetermined time for defining “at this time” is, for example, 60 seconds, and the data for 60 seconds from the past to the present detected when the accelerator pedal is turned off in the stable follow-up driving scene determined in step S110 is used. An average value Mean_x (n) and a standard deviation Stdev_x (n) of the margin time TTC are calculated. The following parameters are used to calculate the mean value Mean_x (n) and the standard deviation Stdev_x (n).

x (n): The data acquired this time, that is, the margin time TTC when the accelerator pedal is off calculated in step S106
K: Number of TTC data calculated within a predetermined time
M ₁ (n): Total value of TTC within the predetermined time calculated this time
M ₂ (n): Sum of squares of TTC within a predetermined time calculated this time
M ₁ (n-1): Total value of TTC within the predetermined time calculated last time
M ₂ (n-1): Sum of squares of TTC within the predetermined time calculated last time
Mean_x (n): Average value of this data, that is, average value of TTC
Var_x (n): Distribution of data this time, that is, distribution of TTC
Stdev_x (n): Standard deviation of this data, that is, standard deviation of TTC

  Here, the data number K is determined by the predetermined time × the number of samplings (samples) per second. That is, if the predetermined time “at this time” is 60 seconds and the sampling number is 5 Hz, the number of data is K = 300.

The total value M ₁ (n) and the square sum M ₂ (n) can be calculated from the following (Equation 3) and (Equation 4) using these parameters.
M ₁ (n) = M ₁ (n−1) + x (n) −M ₁ (n−1) / K (Equation 3)
M ₂ (n) = M ₂ (n−1) + (x (n)) ² −M ₂ (n−1) / K (Equation 4)

The mean value Mean_x (n), variance Var_x (n), and standard deviation Stdev_x (n) of the surplus time TTC when the accelerator pedal is off at this time are expressed by the following (formula 5) (formula 6) (formula 7) ).
Mean_x (n) = M ₁ (n) / K (Formula 5)
Var_x (n) = M ₂ (n) / K− (M ₁ (n)) ² / K ² (Formula 6)
Stdev_x (n) = √ (Var_x (n)) (Expression 7)

  In step S124, in order to perform the driving diagnosis of “this day” of the driver, the following characteristic value of the driver of “this day”, that is, a margin when the accelerator pedal is off within a predetermined time defining “this day” An average value Mean_x (n) and a standard deviation Stdev_x (n) of the time TTC are calculated. Here, the predetermined time for defining “this day” is, for example, 360 seconds, and the data for 360 seconds from the past to the present detected when the accelerator pedal is turned off in the stable follow-up driving scene determined in step S110 is used. An average value Mean_x (n) and a standard deviation Stdev_x (n) of the allowance time TTC when the accelerator pedal is off are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 1800 when the predetermined time of this day is 360 seconds and the sampling number is 5 Hz.

  In step S126, in order to perform the "normal" driving diagnosis of the driver, the follow-up characteristic value of the "normal" driver, that is, the allowance time TTC when the accelerator pedal is off within a predetermined time defining "normal". Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined time for defining “ordinary” is, for example, 2160 seconds, and the accelerator is used by using data for 2160 seconds from the past to the present detected when the accelerator pedal is turned off in the stable follow-up driving scene determined in step S110. An average value Mean_x (n) and a standard deviation Stdev_x (n) of the margin time TTC when the pedal is off are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 10800 when the predetermined time of “normal” is 2160 seconds and the sampling number is 5 Hz.

  In the subsequent processing after step S128, the driver is diagnosed using the driving characteristic values calculated in steps S122, S124, and S126. Here, the driving characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two are different. That is, in the data structure shown in FIG. 4, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

  First, in step S128, a deviation degree indicating how much the driving characteristics of the driver “at this time” deviate from the driving characteristics of the driver “this day” is calculated. Here, the deviation degree of “this time” with respect to “this day” is the distribution of the margin time TTC when the accelerator pedal is off on “this day” and the distribution of the margin time TTC when the accelerator pedal is off at this time. It shows the difference. In order to calculate the degree of deviation of “this time” with respect to “this day”, the distribution of the surplus time TTC when the accelerator pedal is off on this day is used as a reference distribution representing a long-time behavior distribution. The surplus time TTC distribution when the accelerator pedal is off is used as a comparison target distribution representing a short-time behavior distribution.

As a method of calculating the deviation degree, here, the position of “average value−standard deviation” (comparison value x _std ) of the action distribution for a long time (for example, “this day”) is used for a short time (for example, “at this time”). ) And a long time (for example, “this day”) distribution function is used.

  For calculating the deviation degree, the mean time TTC when the accelerator pedal is off is normalized using the mean value Mean_x (n) and the standard deviation Stdev_x (n) of the time TTC when the accelerator pedal is off calculated in steps S122 and S124. A probability density function is calculated assuming a distribution.

Therefore, as shown in FIGS. 6A and 6B, in the comparison target region set based on the predetermined value (comparison value x _std ), the comparison target is compared with the short normal distribution for a long time. How much the normal distribution of time deviates is calculated as a deviation degree Dist _diff . Specifically, the difference between the comparison distribution and the reference distribution in the region where the margin time TTC is shorter than the comparison value x _std (the area of the hatched portion in FIG. 6A, the length of the arrow in FIG. 6B) Is equivalent to the deviation degree Dist _diff . The calculation methods shown in FIGS. 6C and 6D will be described later.

  FIGS. 7A and 7B show probability density distributions and cumulative distributions calculated based on results obtained from actual public road experiments. In FIG. 7A, the probability density distribution in a case where the margin time TTC when the accelerator pedal is off is approximated by a normal distribution using the mean value “Mean_x (n)” and the standard deviation Stdev_x (n) of “at this time” is shown by a one-dot chain line. The probability density distribution is shown by a solid line when the margin time TTC when the accelerator pedal is off is approximated by a normal distribution using the average value Mean_x (n) and standard deviation Stdev_x (n) of “this day”. In FIG. 7B, the cumulative distribution of “At this time” is indicated by a one-dot chain line, and the cumulative distribution of “this day” is indicated by a solid line. 7 (a) and 7 (b), the mean value of the margin time TTC when the accelerator pedal is off at this time Mean_x (n) = 1.22, the standard deviation Stdev_x (n) = 0.80, The average value Mean_x (n) = 1.63 and the standard deviation Stdev_x (n) = 1.00 of the surplus time TTC when the accelerator pedal of “day” is off.

First, the comparison value x _std is calculated from the following (Expression 8) from the average value Mean_std and the standard deviation Stdev_std of the reference distribution.
x _std = Mean_std−Stdev_std (Equation 8)
The comparison value x _std is a value of the allowance time TTC indicating at which position the reference distribution and the comparison distribution are compared, and corresponds to a position indicated by a broken line in FIGS.

Next, the cumulative distribution value in the reference distribution comparison value x _std is calculated. The probability distribution function f (x) of the normal distribution can be calculated from the following (Equation 9) when the average value is μ and the standard deviation is σ (see FIG. 7A).
... (Formula 9)

When the probability density function f (x) calculated in (Expression 9) is integrated, a cumulative distribution function F (x) is obtained as shown in (Expression 10) below (see FIG. 7B).
... (Formula 10)

When the average value of the standard distribution is set to μ _std and the standard deviation is set to σ _std , the probability F _std (x) of the cumulative distribution in the comparison value x _std is calculated by the following (formula 11).
... (Formula 11)

Next, the value of the cumulative distribution in the comparison value x _std of the comparison distribution is calculated. When the average value of the comparison distribution is μ _comp and the standard deviation is σ _comp , the probability F _comp (x) of the cumulative distribution at the comparison value x _std is calculated by the following (formula 12).
... (Formula 12)

The difference between the probability F _std (x) of the reference cumulative distribution and the probability F _comp (x) of the cumulative distribution to be compared is defined as the following (formula 13) as the deviation degree Dist _diff of “at this time” with respect to “this day” ).
Dist _diff = F _comp (x) −F _std (x) (Formula 13)
As the deviation degree Dist _diff increases in the positive direction, the driver's driving at this time is biased toward a direction in which the margin time TTC is shorter than the driving on this day, that is, a high risk direction. As the deviation degree Dist _diff increases in the negative direction, the driver's driving at this time tends to have a longer margin time TTC than the driving on this day, that is, the risk tends to decrease. When the driver always operates with the same driving characteristics, the deviation degree Dist _diff is zero.

In step S128, the degree of deviation Dsit _diff of “at this time” with respect to “normal” is also calculated. In this case, the distribution of the TTC when the accelerator pedal is off as a “normal” is used as a reference distribution representing the long-term behavior distribution, and the distribution of the margin time TTC when the accelerator pedal is off at this time is used as a short-time behavior. This is used as a comparison target distribution representing the distribution. Then, by using the probability F _std (x) of the “normal” cumulative distribution and the probability F _comp (x) of the cumulative distribution of “at this time”, “at this time” with respect to “normal” from the above (formula 13). The deviation degree Dist _diff is calculated.

In this way, after calculating the deviation degree Dist _diff for “this time” for “this day” and “this time” for “normal” in step S128, the process proceeds to step S130. In step S130, as in the process in step S128, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the distribution of the allowance time TTC when the accelerator pedal is off at “normal” is used as a reference distribution representing the long-term behavior distribution, and the distribution of the allowance time TTC when the accelerator pedal is off on “this day” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time.

In the subsequent step S132, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as in the process in step S128. Note that here, the distribution of the allowance time TTC when the accelerator pedal is off in “general public” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the allowance time TTC when the accelerator pedal is off in “normal” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time. The driving value of “general public”, that is, the average value and the standard deviation of the allowance time TTC when the accelerator pedal is off are set in advance as fixed values.

As described above, after the driving diagnosis of the driver is performed using the data obtained at the plurality of different time spans in step S120, the process proceeds to step S140. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “normal” is Dist_1a, the deviation degree Dist _diff of “this time” for “this day” is Dist_1b, and “this day” for “normal” The departure degree Dist _diff is Dist_2, and the “ordinary” departure degree Dist _diff for “general public” is Dist_3.

  In step S140, it is determined whether or not to perform the alarm presentation process based on the driving diagnosis result in step S120. Here, the deviation degree Dist_1a of “at this time” with respect to “normal” calculated in step S128, the deviation degree Dist_1b of “at this time” with respect to “this day”, or “this day” with respect to “normal” calculated at step S130. It is determined whether or not the deviation degree Dist_2 is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1a, Dist_1b, or Dist_2 is larger than a threshold value, it progresses to Step S150 and gives a warning to a driver. After the alarm is presented, this process is terminated.

  For example, when the deviation level Dist_2 of “this day” with respect to “normal” is larger than the threshold value, the speaker 130 outputs a sound “prone to approach the vehicle ahead” together with a buzzer sound. The voice information is set so as to inform the driver that the driver tends to take a shorter inter-vehicle distance than usual or to take a longer inter-vehicle distance than the current inter-vehicle distance. The specific audio information is not limited to this. Even when the deviation degree Dist_1a of “at this time” with respect to “normal” and the deviation degree Dist_1b of “at this time” with respect to “this day” are larger than the threshold value, appropriate audio information set in advance is output.

  When a negative determination is made in step S140 and no alarm presentation is performed, the process proceeds to step S160, and it is determined whether to perform the guidance presentation process based on the driving diagnosis result of step S120. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S132 is smaller than a threshold (for example, 0.07) for determining whether to provide guidance (improvement suggestion) or not. Determine whether. Here, the threshold value for determining whether or not to present the instruction is appropriately set in advance as a value such that the deviation degree is almost always within the range for the same driver. The value of “0.07” described above is set based on the result of an actual vehicle experiment by 15 subjects. According to this experimental result, the degree of deviation calculated for each subject was always within 0.07.

When Dist_3 is smaller than the threshold value, the process proceeds to step S170, and guidance is presented to the driver (improvement suggestion). This process is complete | finished after giving instruction | indication presentation.
For example, the display of the content which abandons a driver | operator and the audio | voice are output as guidance presentation content. For example, the “ordinary” deviation degree Dist_3 is converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation. That is, the score of the driver who tends to increase the risk is 50 points or less, and the score of a good driver who tries safe driving is 50 points or more. The score is displayed within a range of 0 to 100. When the score obtained by converting the deviation degree Dist_3 is 100 or more, it is 100 points, and when it is 0 or less, 0 points.

  Further, as shown in FIG. 8, the distribution of the general inter-vehicle distance is schematically displayed on the display unit 180 so that the driver's “ordinary” inter-vehicle distance can be understood with respect to the general public. Display. FIG. 8 shows that the driver's inter-vehicle distance is longer by two steps than the average value of the general inter-vehicle distance, and that the driving characteristics have more margin than the general public. It is provided to the driver as visual information.

  In addition, the driver's usual distance between vehicles tends to be longer than that of a general driver, and the driver is informed by voice output from the speaker 130 that the driver is an excellent driver who performs a follow-up driving with a margin. For example, it outputs a voice saying "You are driving safely. Let's do our best in this way." In this way, it is possible to inform the driver that the driver's individual following driving characteristics are excellent for the general public driver, and to display or encourage the driver to maintain good driving or improve it. Output audio.

  In addition, in the case of display and voice output, the example of using the term “distance between vehicles” familiar to the driver has been explained, but of course it is also possible to perform display and voice output using the term “distance between vehicles” It is. The contents of the display and audio information convey the driving tendency to the driver in an easy-to-understand manner and provide an improvement so that a warning is given so as not to reach a high-risk state and good driving is further improved. If it can be performed, it will not be limited to the example mentioned above.

  FIG. 9 shows another example of display based on the driving diagnosis result. In FIG. 9, the vertical axis indicates the characteristics of the individual driver with respect to general driving characteristics, and the horizontal axis indicates the current state with respect to the normal driving characteristics of the driver. Here, as described above, the margin time TTC when the accelerator pedal is turned off from the follow-up running can be used as the driving characteristic. The greater the deviation of “this day” from “normal” calculated in step S130, the more the marker M in the figure is moved to the right, that is, the high risk direction. Further, as the degree of deviation from “general” with respect to “general public” calculated in step S132 is larger, the marker M in the figure is moved upward, that is, in a high risk direction.

  In this way, in the display example shown in FIG. 9, the driving diagnosis result based on “general public” and the “normal”, that is, the driving diagnosis result based on the personal characteristics of the driver are set from two axes. Displayed on a two-dimensional map. It should be noted that the horizontal axis can set the degree of departure at “this time” with respect to “this day” and the degree of departure at “at this time” with respect to “normal”. It is also possible to display the vertical axis and the horizontal axis interchanged.

  Also, as shown in FIG. 9, the two-dimensional map can be divided into a plurality of blocks, and each block can be color-coded so as to make it easy to understand where the driving characteristics of the driver are. For example, the upper right block in FIG. 9 may be displayed in red, the color may be lighter as it moves to the lower left, and the lower left block may be displayed in light blue.

In the first embodiment described above, the following operational effects can be obtained.
(1) The vehicle driving support device 4 calculates an index for driving diagnosis based on the driving state of the vehicle and the driving operation by the driver, and diagnoses the driving of the driver from the calculated index. Furthermore, by evaluating the driving diagnosis result according to the evaluation criteria, the content of information provision to the driver is set, and the information is provided to the driver with the set content. This makes it possible to provide driving diagnosis results that improve the driver's satisfaction.
(2) By evaluating the driving diagnosis result according to at least two evaluation criteria and setting the information providing content, it is possible to set the information providing content that is easy for the driver to understand.
(3) The vehicle driving support apparatus 4 performs driving diagnosis using a single index, compares the driving diagnosis result using the single index with at least two evaluation reference values, and determines the contents of information provision. Set. As a result, it is possible to set information provision contents that are easy for the driver to understand.
(4) At least two evaluation standard values are evaluated standard values based on the driving state of a general driver, evaluation standard values based on the normal driving state of the driver, and the driving state of the driver of the day. Set from the evaluation standard values based on. As described above, since the driving diagnosis result is evaluated based on the general driver or the driver himself, information providing contents that are easy for the driver to understand can be set.
(5) When the driving diagnosis result falls below a predetermined evaluation standard value, an alarm is set as the content of information provision, so the driver's attention can be alerted so that the driving of the driver does not reach a high risk state. .
(6) When the driving diagnosis result exceeds a predetermined evaluation standard value, driving improvement suggestion is set as the contents of information provision, so encourage the driver to maintain or improve good driving by giving up the driver Can do.
(7) Since the driving diagnosis result is evaluated according to two evaluation criteria, and an alarm or driving improvement suggestion is set as the content of information provision according to the evaluation result, the driver's driving does not reach a high risk state as appropriate. By attracting the driver's attention or giving up the driver, the driver can be encouraged to maintain good driving or improve it. In this case, when the driving diagnosis result falls below the first evaluation reference value, an alarm is set, and when it exceeds the second evaluation reference value, driving improvement suggestion is set.
(8) The risk recognition characteristic of the driver is detected as an index for driving diagnosis. Specifically, a margin time TTC between the host vehicle and the preceding vehicle when the accelerator pedal is released is calculated. The margin time TTC represents the time until the host vehicle and the preceding vehicle come into contact with each other when the host vehicle speed V and the relative vehicle speed Vr are constant. By using it as an index representing the characteristics, it is possible to perform an accurate driving diagnosis in the following traveling scene.

-Modification of the first embodiment-
Hereinafter, another method for calculating the deviation degree Dist _diff will be described. Here, the behavior distribution for a long time (for example, “this day”) and the behavior distribution for a short time (for example, “at this time”) are approximated by normal distributions, respectively, and the size of the area where the two normal distributions do not overlap is calculated. Deviation degree is calculated as Dist _diff .

Specifically, as shown in FIGS. 6C and 6D, the difference between the comparison distribution and the reference distribution in a region having a margin time TTC shorter than the intersection α between the reference distribution and the comparison distribution, that is, more than the reference distribution. The size of the comparison distribution area of the protruding portion is calculated as the deviation degree Dist _diff .

By substituting the average value μ _std and standard deviation σ _std of the reference distribution and the average value μ _comp and standard deviation σ _{comp of the} comparison distribution into the above (Equation 10), the probability density function f _std ( x) and the probability density function f _comp (x) of the comparative distribution are expressed by the following (formula 14) and (formula 15), respectively.
... (Formula 14)
... (Formula 15)

When these simultaneous equations of (Equation 14) and (Equation 15) are obtained, the frequencies of the distributions coincide at two points α and β (α <β). In order to obtain the area in which the two normal distributions do not overlap in the region where the margin time TTC is shorter than the intersection α, the above (Equation 11) for obtaining the cumulative distribution function of the reference distribution and the above for obtaining the cumulative distribution function of the comparative distribution. The intersection point α is substituted into (Expression 12). Then, the probability F _std (α) of the cumulative distribution of the reference distribution is subtracted from the probability F _comp (α) of the cumulative distribution of the comparative distribution at the intersection α.

The deviation degree Dist _diff can be calculated by the following (formula 16).
-Dist _diff = F _comp (α) −F _std (α) (Expression 16)
The area Dist _corr where the two normal distributions overlap can be obtained by the following (Equation 17).
-Dist _corr = 1--Dist _diff (Expression 17)

<< Second Embodiment >>
The vehicle driving support apparatus according to the second embodiment of the present invention will be described below. FIG. 10 is a system diagram showing the configuration of the vehicle driving support device 5 according to the second embodiment. In the second embodiment, portions having the same functions as those in the first embodiment described above are denoted by the same reference numerals and description thereof is omitted. Here, differences from the first embodiment will be mainly described.

  As shown in FIG. 10, the vehicle driving assistance device 5 includes a steering angle sensor 5, a laser radar 10, a front camera 15, a vehicle speed sensor 30, a navigation system 50, a brake pedal stroke sensor 60, a blinker switch 65, a controller 350, a speaker. 130 and a display unit 180.

  The rudder angle sensor 5 is an angle sensor attached, for example, in the vicinity of a steering column or a steering wheel (not shown), and detects a steering angle due to the turning of the driver from the rotation of the steering shaft. The detected steering angle is output to the controller 350.

  The front camera 15 is a small CCD camera, a CMOS camera, or the like attached to the upper part of the front window, and detects the state of the road ahead as an image. The controller 350 performs image processing on the image signal from the front camera 15 to detect a lane marker or the like existing in the front area of the host vehicle. The detection area by the front camera 15 is about ± 30 deg in the horizontal direction with respect to the center line in the front-rear direction of the vehicle, and the front road scenery included in this area is captured as an image.

  The controller 350 is an electronic control unit including a CPU and CPU peripheral components such as a ROM and a RAM, and controls the entire vehicle driving support device 5. The controller 350 analyzes the driving characteristics of the driver based on signals input from the rudder angle sensor 5, the laser radar 10, the front camera 15, the vehicle speed sensor 30, the navigation system 50, the brake pedal stroke sensor 60, the blinker switch 65, and the like. And perform driving diagnosis. Then, information is provided to the driver based on the driving diagnosis result. Information provided to the driver includes warnings to the driver and suggestions for improving driving operations. Specific control contents in the controller 350 will be described later.

  In the second embodiment, a driver's driving diagnosis is performed based on the traveling state of the host vehicle and the driver's driving operation, and an alarm to the driver or an improvement suggestion of the driving operation is given according to the driving diagnosis result. Do. Specifically, the time until the vehicle departs from the traveling lane in which the vehicle is traveling (lane departure time) is detected as the driving characteristics of the driver, and a driving diagnosis is performed using the detected driving characteristics as an index. That is, driving diagnosis related to the driving operation of the driver in the left-right direction is performed.

  The operation of the vehicle driving support apparatus 5 according to the second embodiment will be described in detail with reference to FIG. FIG. 11 is a flowchart of the processing procedure of the driving support control process in the controller 350 according to the second embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S200, the traveling state of the host vehicle is detected. Here, as the running state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10 are acquired. Furthermore, image processing is performed on the captured image of the front area of the host vehicle captured by the front camera 15 to detect the traveling lane of the host vehicle.

  In step S202, the operation state of the driver is detected. Here, the brake pedal operation amount detected by the brake pedal stroke sensor 60 and the presence / absence of the winker lever operation detected by the winker switch 65 are acquired as the operation state of the driver. Further, the steering angle θ detected by the steering angle sensor 5 is acquired.

In step S204, a lane departure time (Time to Lace Crossing) TLC used as a driving characteristic of the driver's own vehicle in the left-right direction is calculated in a driving diagnosis described later. The lane departure time TLC is a physical quantity that represents the time taken for the host vehicle to depart from the traveling lane in which the host vehicle is traveling. The lane departure time TLC is expressed as follows using the lateral distance DL and the host vehicle speed V from the current position of the host vehicle to the lane marker. It can be calculated from (Equation 18).
TLC = DL / V (Formula 18)

  In step S206, it is determined whether correction steering has been performed. Specifically, when the steering operation is performed at a steering angle equal to or greater than a predetermined value from the steering operation state maintained at a substantially constant steering angle, it is determined that the correction steering is performed. Here, the threshold value for determining the presence / absence of corrective steering is set in advance according to the width of the road, the number of lanes, and the like. When determining the presence or absence of corrective steering, a threshold is selected according to the vehicle width, the number of lanes, and the like of the traveling lane of the host vehicle obtained from the navigation system 50 and the front camera 15. If it is determined that there is corrective steering, the process proceeds to step S210. If there is no corrective steering, this process ends.

  In step S210, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only in a traveling scene in which the correction steering is performed from a state where the host vehicle is traveling stably.

An example of conditions for a stable running state is as follows.
(A) No brake operation by the driver (for example, the brake pedal operation amount is substantially 0)
(B) There is no blinker lever operation by the driver (for example, there is no ON signal input from the blinker switch 65).
(C) Less fluctuation in vehicle speed (for example, change in own vehicle speed V is ± 10 km / h or less)
(D) The states (a) to (c) are continuing (for example, continuing for 5 seconds or more).

  When all of the conditions (a) to (d) are satisfied, it is determined that the host vehicle is in a stable traveling state, and the process proceeds to step S212 to perform driving diagnosis. On the other hand, when any of the conditions (a) to (d) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing a driving diagnosis. The conditions for determining whether or not the vehicle is in a stable running state are not limited to the above (a) to (d), and another detection means for detecting whether or not the brake is operated and whether or not the winker lever is operated It is also possible to detect by.

  In step S212, the travel location is determined. In step S214, the current time is recorded. In step S216, based on the labeling results in steps S212 and S214, data used for performing a driving diagnosis of the driver is stored. Here, for example, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the driving characteristic index in the link, the number of travels of the link, and the like are written in the structure format, and the travel road database is constructed.

  In the second embodiment, the lane departure time TLC when the correction steering is performed is used as a physical quantity representing the driving characteristics of the driver. Specifically, the time until the host vehicle deviates from the lane marker present in the steering direction is used. The calculation method of the driving characteristic and the driving characteristic index will be described in detail in the driving diagnosis process.

  In subsequent step S220, the driver is diagnosed using the data stored in step S216. The driving diagnosis is performed based on the driving characteristics of the driver when the driver performs the correction steering from a stable traveling state. Here, a case where the lane departure time TLC when the correction steering is performed is used as an example of the driving characteristic at the time of the correction steering will be described.

  FIG. 12 shows an example of a time change of the lane departure time TLC, and FIG. 13 shows an example of a frequency distribution of the lane departure time TLC. As shown in FIG. 13, when the host vehicle is in a stable running state, the distribution of the lane departure time TLC normally peaks at about 2.0 seconds. FIG. 14 shows an example of a change in the lane departure time TLC during the correction steering. As shown in FIG. 14, when the driver is performing the driving operation loosely, the lane departure time TLC tends to be shortened because the corrected steering is delayed, and when the driver is operating with tension, the operation is quick. Since the correction steering is performed at the timing, the lane departure time TLC tends to be long.

  In the driving diagnosis process, each data of the A layer to D layer having the data structure shown in FIG. 4 is used to drive at different time spans, that is, “at this time”, “this day”, and “ordinary”. Determine. The driving diagnosis process executed in step S220 will be described in detail using the flowchart of FIG.

  In step S222, in order to perform the driving diagnosis of “at this time” of the driver, the driving characteristic value of the driver at this time is calculated. The average value Mean_x (n) and standard deviation Stdev_x (n) of the lane departure time TLC during the correction steering within a predetermined time defining “at this time” are calculated as the driving characteristic values of the driver. Here, the predetermined time for defining “at this time” is, for example, 60 seconds, and using the data for 60 seconds from the past to the present detected during the correction steering in the stable driving scene determined in Step S210, the lane An average value Mean_x (n) and a standard deviation Stdev_x (n) of the deviation time TLC are calculated. The average value Mean_x (n) and the standard deviation Stdev_x (n) can be calculated using (Expression 5) and (Expression 7), respectively, as in the first embodiment.

  In step S224, in order to perform the driving diagnosis of “this day” of the driver, the following characteristic value of the driver of “this day”, that is, the lane deviation at the time of correction steering within a predetermined time defining “this day” The average value Mean_x (n) and standard deviation Stdev_x (n) of the time TLC are calculated. Here, the predetermined time for defining “this day” is, for example, 360 seconds, and using the data for 360 seconds from the past to the present detected during the correction steering in the stable driving state determined in step S210, the lane An average value Mean_x (n) and a standard deviation Stdev_x (n) of the deviation time TLC are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 1800 when the predetermined time of this day is 360 seconds and the sampling number is 5 Hz.

  In step S226, in order to perform the “normal” driving diagnosis of the driver, the follow-up characteristic value of the “normal” driver, that is, the lane departure time TLC at the time of correction steering within a predetermined time defining “normal” is determined. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined time for defining “ordinary” is, for example, 2160 seconds, and the lane departure time is determined using data for 2160 seconds from the past to the present detected at the time of the correction steering in the stable traveling scene determined in step S210. The average value Mean_x (n) and standard deviation Stdev_x (n) of TLC are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 10800 when the predetermined time of “normal” is 2160 seconds and the sampling number is 5 Hz.

  In the subsequent processing from step S228, the driving diagnosis of the driver is performed using the driving characteristic values calculated in steps S222, S224, and S226. Here, the driving characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two are different. That is, in the data structure shown in FIG. 4, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

  First, in step S228, a deviation degree indicating how much the driving characteristics of the driver “at this time” deviate from the driving characteristics of the driver “this day” is calculated. Here, the deviation degree of “at this time” with respect to “this day” is the difference between the distribution of the lane departure time TLC at the time of correction steering of “this day” and the distribution of the deviation time TLC at the time of correction steering of “at this time”. Is shown. In order to calculate the degree of departure of “this time” with respect to “this day”, the distribution of the lane departure time TLC at the time of the correction steering of “this day” is used as a reference distribution representing a long-time behavior distribution. The lane departure time TLC distribution at the time of the correction steering is used as a comparison target distribution representing a short-time behavior distribution.

Here, as in the first embodiment, the deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above. In addition, the deviation degree Dsit _diff of “at this time” with respect to “normal” is also calculated. In this case, the distribution of TLC at the time of “normal” correction steering is used as a reference distribution representing the long-time behavior distribution, and the distribution of TLC at the time of correction steering at this time is compared to represent the short-time behavior distribution. Use as target distribution. Then, by using the probability F _std (x) of the “normal” cumulative distribution and the probability F _comp (x) of the cumulative distribution “at this time”, the “normal” from the above-described (Expression 13) or (Expression 16). The deviation degree “Dist _diff ” of “at this time” is calculated.

As described above, after calculating the deviation degree Dist _diff for “this time” for “this day” and “this time” for “usual” in step S228, the process proceeds to step S230. In step S230, similar to the processing in step S228, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the distribution of the lane departure time TLC at the time of the “normal” correction steering is used as a reference distribution representing the long-term behavior distribution, and the distribution of the lane departure time TLC at the time of the correction steering of “this day” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time.

In the subsequent step S232, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as the processing in step S228. In this case, the distribution of the lane departure time TLC at the time of the “general” correction steering is used as a reference distribution representing the long-time behavior distribution, and the distribution of the lane departure time TLC at the time of the “normal” correction steering is shortened. It is used as a comparison target distribution representing the behavioral distribution of time. As the driving characteristic value of “general public”, that is, the average value and the standard deviation of the lane departure time TLC during the correction steering, appropriate values are set in advance as fixed values.

As described above, after the driver's driving diagnosis is performed using the data obtained in the plurality of different time spans in step S220, the process proceeds to step S240. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “normal” is Dist_1a, the deviation degree Dist _diff of “this time” for “this day” is Dist_1b, and “this day” for “normal” The departure degree Dist _diff is Dist_2, and the “ordinary” departure degree Dist _diff for “general public” is Dist_3.

  In step S240, it is determined whether or not to perform the alarm presentation process based on the driving diagnosis result in step S220. Here, the deviation degree Dist_1a of “at this time” with respect to “normal” calculated at step S228, the deviation degree Dist_1b of “at this time” with respect to “this day”, or “this day” with respect to “normal” calculated at step S230. It is determined whether or not the deviation degree Dist_2 is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1a, Dist_1b, or Dist_2 is larger than a threshold value, it progresses to Step S250 and gives a warning to a driver. After the alarm is presented, this process is terminated.

  For example, when the deviation level Dist_2 of “this day” with respect to “normal” is larger than the threshold value, the speaker 130 outputs a sound “being careful about exceeding the lane” together with a buzzer sound. The voice information is set so as to notify the driver that the start of the correction steering tends to be slow or to promote the stability of the steering operation. The specific audio information is not limited to this. Even when the deviation degree Dist_1a of “at this time” with respect to “normal” and the deviation degree Dist_1b of “at this time” with respect to “this day” are larger than the threshold value, appropriate audio information set in advance is output.

  When a negative determination is made in step S240 and no alarm presentation is performed, the process proceeds to step S260, and it is determined whether to perform the guidance presentation process based on the driving diagnosis result in step S220. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S232 is smaller than a threshold value (for example, 0.07) for determining whether or not to provide guidance (improvement suggestion). Determine whether. Here, the threshold value for determining whether or not to present the instruction is appropriately set in advance as a value such that the deviation degree is almost always within the range for the same driver. The value of “0.07” described above is set based on the result of an actual vehicle experiment by 15 subjects. According to this experimental result, the degree of deviation calculated for each subject was always within 0.07.

When Dist_3 is smaller than the threshold value, the process proceeds to step S270, where guidance is presented to the driver (improvement suggestion). This process is complete | finished after giving instruction | indication presentation.
For example, the display of the content which abandons a driver | operator and the audio | voice are output as guidance presentation content. For example, the “ordinary” deviation degree Dist_3 is converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation. That is, the number of drivers who tend to be slow in corrective steering is 50 points or less, and the number of excellent drivers who perform early and stable steering operation is 50 points or more. The score is displayed within a range of 0 to 100. When the score obtained by converting the deviation degree Dist_3 is 100 or more, it is 100 points, and when it is 0 or less, 0 points.

  Improvement suggestions can also be made by voice. In this way, it is possible to inform the driver that the driver's individual following driving characteristics are excellent for the general public driver, and to display or encourage the driver to maintain good driving or improve it. Output audio. The driving diagnosis result can also be displayed using a two-dimensional map as shown in FIG.

In the second embodiment described above, the following operational effects can be obtained in addition to the effects of the first embodiment described above.
As an index for driving diagnosis, a risk perception characteristic of a driver is detected. Specifically, the lane departure time TLC until the host vehicle departs from the traveling lane when the correction steering is performed is calculated. The lane departure time TLC represents the time it takes for the vehicle to reach the lane boundary and deviate from the lane. Therefore, the lane departure time TLC is used as an index that represents the driver's characteristics, so that the driver is driving loosely or nervous. From the point of view of whether or not driving, accurate driving diagnosis can be performed.

<< Third Embodiment >>
The vehicle driving support apparatus according to the third embodiment of the present invention will be described below. The basic configuration of the vehicle driving support apparatus according to the third embodiment is the same as that of the first embodiment shown in FIG. Here, differences from the first embodiment will be mainly described.

  In the third embodiment, driving characteristics in a state where the host vehicle is performing a stable single traveling are detected, and driving diagnosis is performed using the detected driving characteristics as an index.

  The operation of the vehicle driving support apparatus 4 according to the third embodiment will be described in detail with reference to FIG. FIG. 16 is a flowchart of a processing procedure of driving support control processing in the controller 300 according to the third embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S300, the traveling state of the host vehicle is detected. Here, the host vehicle speed V detected by the vehicle speed sensor 30 and the presence / absence of a preceding vehicle detected by the laser radar 10 are acquired as the traveling state of the host vehicle. In step S305, the operation state of the driver is detected. Here, as an operation state of the driver, an accelerator pedal operation amount detected by the accelerator pedal stroke sensor 55, a brake pedal operation amount detected by the brake pedal stroke sensor 60, and a winker lever operation amount detected by the winker switch 65. Get presence / absence.

  In step S306, the state of the road on which the host vehicle travels is acquired. Specifically, the navigation system 50 includes information such as the road type (whether it is a high-standard trunk road or a general road) of the road on which the host vehicle is running, the current vehicle speed limit vehicle speed, and the like as parameters indicating the road condition. Get from.

  In step S310, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only in a driving scene in which the host vehicle is stably traveling independently.

An example of the condition of the stable single traveling state is as follows.
(A) There is no preceding vehicle in the detection area of the laser radar 10 (b) The vehicle is traveling on the same type of road (c) The vehicle is traveling under the same vehicle speed limit (d) The driver operates a large accelerator pedal and brakes No pedal operation (for example, the operation amount is less than 30% of the total operation amount)
(E) There is no blinker lever operation by the driver (for example, there is no ON signal input from the blinker switch 65).
(F) The states (a) to (e) are continuing (for example, continuing for 5 seconds or more).

  When all of the conditions (a) to (f) are satisfied, it is determined that the host vehicle is in a stable single traveling state, and the process proceeds to step S312 to perform driving diagnosis. On the other hand, if any of the conditions (a) to (f) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing driving diagnosis. Note that the conditions for determining whether or not the vehicle is in a stable single traveling state are not limited to the above (a) to (f), and the presence or absence of a brake operation or the presence or absence of a blinker lever operation is detected separately. It is also possible to detect by means.

  In step S312, the travel location is determined. In step S314, the current time is recorded. In step S316, based on the labeling results of steps S312 and S314, data used for performing a driving diagnosis of the driver is stored. Here, for example, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the driving characteristic index in the link, the number of travels of the link, and the like are written in the structure format, and the travel road database is constructed. In the third embodiment, the host vehicle speed V detected during independent traveling is used as a physical quantity representing the driving characteristics of the driver.

  In subsequent step S320, a driving diagnosis of the driver is performed using the data stored in step S316. The driving diagnosis is performed based on the vehicle speed characteristic in a stable single traveling state. In the driving diagnosis process, using the data of each of the A layer to D layer having the data structure shown in FIG. 66, driving at different time spans, that is, driving at this time, “this day”, and “ordinary” driver. Determine. The driving diagnosis process executed in step S320 will be described in detail using the flowchart of FIG.

  In step S322, in order to perform a driving diagnosis of “at this time” of the driver, an independent traveling vehicle speed characteristic value of the driver at this time is calculated. As a driver's vehicle speed characteristic value, an average value Mean_x (n) and a standard deviation Stdev_x (n) of the vehicle speed V within a predetermined time defining “at this time” are calculated. Here, the predetermined time for defining “at this time” is, for example, 60 seconds, and the vehicle speed V is determined using data for 60 seconds from the past to the present detected in the stable single traveling state determined in step S310. Mean value Mean_x (n) and standard deviation Stdev_x (n) are calculated. The average value Mean_x (n) and the standard deviation Stdev_x (n) can be calculated using (Equation 5) and (Equation 7), respectively, as in the seventh embodiment.

  In step S324, in order to perform the driving diagnosis of “this day” of the driver, the independent traveling vehicle speed characteristic value of the driver of “this day”, that is, the vehicle speed V within a predetermined time defining “this day”. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined time for defining “this day” is, for example, 360 seconds, and the vehicle speed V is determined using the data for 360 seconds from the past to the present detected in the stable single traveling state determined in step S310. Mean value Mean_x (n) and standard deviation Stdev_x (n) are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 1800 when the predetermined time of this day is 360 seconds and the sampling number is 5 Hz.

  In step S326, in order to perform the "normal" driving diagnosis of the driver, the "normal" driver's individual traveling vehicle speed characteristic value, that is, the average value Mean_x of the vehicle speed V within a predetermined time period defining "normal". (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined time for defining “usually” is, for example, 2160 seconds, and the average of the vehicle speed V is calculated using data for 2160 seconds from the past to the present detected in the stable single traveling state determined in step S310. The value Mean_x (n) and the standard deviation Stdev_x (n) are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 10800 when the predetermined time of “normal” is 2160 seconds and the sampling number is 5 Hz.

  In the subsequent processing after step S328, the driver's driving diagnosis is performed using the driving characteristic values calculated in steps S322, S324, and S326. Here, the driving characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two are different. That is, in the data structure shown in FIG. 4, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

First, in step S328, a deviation degree indicating how much the driving characteristics of the driver “at this time” deviate from the driving characteristics of the driver “this day” is calculated. Here, the deviation degree of “this time” with respect to “this day” indicates a difference between the distribution of the own vehicle speed V of “this day” and the distribution of the own vehicle speed V of “this time”. In order to calculate the degree of deviation of “this time” with respect to “this day”, the distribution of the vehicle speed V of “this day” is used as a reference distribution representing the long-time behavior distribution, and the vehicle speed V of “at this time”. Is used as a comparison target distribution representing a short-time behavior distribution. Here, as in the first embodiment, the deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above.

In this way, after calculating the deviation degrees Dist _diff for “this time” for “this day” and “this time” for “usual” in step S328, the process proceeds to step S330. In step S330, as in the process in step S328, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the distribution of the vehicle speed V of “normal” is used as a reference distribution representing the long-term behavior distribution, and the distribution of the vehicle speed V of “this day” is the distribution of the comparison target representing the short-time behavior distribution. Used as

In the subsequent step S332, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as in the process in step S328. Here, the distribution of the vehicle speed V of “general public” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the comparison target representing the behavior distribution of the short time is used as the distribution of the “normal” own vehicle speed V. Used as The “universal general” isolated vehicle speed characteristic value, that is, the average value and the standard deviation of the vehicle speed V during the isolated operation are set in advance as fixed values.

In step S333, the single vehicle speed characteristic of “this day” detected in the past and the single vehicle speed characteristic of this day “this day” are compared, and the deviation degree Dist _diff of “this time” with respect to “past” is calculated. Specifically, the distribution of the vehicle speed V of “this day” detected in the previous cycle (for example, the previous day) is used as a reference distribution representing the long-time behavior distribution, and the vehicle speed V of “this day” detected this time is detected. The deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above as a comparison target distribution representing a short-time behavior distribution.

As described above, after the driver's driving diagnosis is performed using the data obtained at the plurality of different time spans in step S320, the process proceeds to step S340. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “this day” is Dist_1, the deviation degree Dist _diff of “this day” for “normal” is Dist_2, and “normal” for “general” The departure degree Dist _diff is Dist_3, and the departure degree Dist _{diff of} “this day of this time” with respect to “this day of the past” is Dist_4.

  In step S340, it is determined whether or not to execute the alarm presentation process based on the driving diagnosis result in step S320. Here, the deviation degree Dist_1 of “this time” with respect to “this day” calculated in step S328, the deviation degree Dist_2 of “this day” with respect to “normal” calculated in step S330, or “the past this calculated in step S333” It is determined whether or not the deviation degree Dist_4 of “this day of this time” with respect to “day” is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1, Dist_2, or Dist_4 is larger than a threshold value, it progresses to step S350 and a warning is shown to a driver. After the alarm is presented, this process is terminated.

  FIG. 18 shows a display example when an alarm is presented by visual information. The display is such that the tendency of the independent traveling vehicle speed on this day (the acquired data result during the middle period) can be compared with the tendency of the independent traveling vehicle speed (the acquired data result for the long term). FIG. 18 shows that today's vehicle speed tends to be higher than usual.

  When an alarm is presented using auditory information, sound information corresponding to the degree of departure is output from the speaker 130. For example, when the deviation degree Dist_2 of “this day” with respect to “normal” is larger than the threshold value, a voice “the traveling vehicle speed is higher than usual” is output. When the deviation degree Dist_1 of “at this time” with respect to “this day” is larger than the threshold value, a voice “the traveling vehicle speed is high” is output. When the deviation of “this day of the past” with respect to “this past day” and Dist_4 are larger than the threshold value, a voice “the traveling vehicle speed is higher than before” is output.

  When a negative determination is made in step S340 and no alarm presentation is performed, the process proceeds to step S360, and it is determined whether to perform the guidance presentation process based on the driving diagnosis result of step S320. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S332 is smaller than a threshold (for example, 0.07) for determining whether or not to provide guidance (improvement suggestion). Determine whether.

  When Dist_3 is smaller than the threshold value, the process proceeds to step S370, and the driver is presented with guidance (improvement suggestion). This process is complete | finished after giving instruction | indication presentation. FIG. 19 shows a display example in the case of providing guidance with visual information. The display is made so that the vehicle speed characteristics of the driver's normal traveling alone can be compared with the vehicle speed characteristics of a general traveling alone. FIG. 19 shows that the vehicle speed of the driver is lower than that of the general public. In addition, the “ordinary” deviation degree Dist_3 can be converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation.

  When providing guidance and presentation based on auditory information, for example, a voice indicating that `` the result of analyzing your driving is lower than the general vehicle speed '' is output, and the driver tends to drive at a lower vehicle speed than usual. The voice of the content which gives up driving of a person is output. The driving diagnosis result can also be displayed using a two-dimensional map as shown in FIG.

As described above, in the third embodiment described above, the following operational effects can be obtained in addition to the effects of the first and second embodiments described above.
The driver's safety awareness and driving preference characteristics are detected as indicators for driving diagnosis. Specifically, since the own vehicle speed V at the time of independent traveling is used, there is no preceding vehicle, and accurate driving diagnosis can be performed in a traveling scene in which the vehicle speed can be set according to the driver's will.

-Modification of the third embodiment-
Hereinafter, another method for calculating the deviation degree Dist _diff will be described. Here, the difference from the distribution to be compared at the position of “restricted vehicle speed” in the reference distribution is calculated as the deviation degree Dist _diff . Specifically, as shown in FIGS. 20A and 20B, the difference between the comparison distribution and the reference distribution in the region where the vehicle speed V is lower than the limit vehicle speed Vlmt is calculated as the deviation degree Dist _diff .

When the average value of the reference distribution is set to μ _std and the standard deviation is set to σ _std , the probability F _std (x) of the cumulative distribution at the limit vehicle speed Vlmt is calculated by the following (Equation 19).
... (Formula 19)

Next, the value of the cumulative distribution at the limited vehicle speed Vlmt of the comparative distribution is calculated. When the average value of the comparison distribution is μ _comp and the standard deviation is σ _comp , the cumulative distribution probability F _comp (x) at the limit vehicle speed Vlmt is calculated by the following (Equation 20).
... (Formula 20)

The difference between the probability F _std (v _lmt ) of the reference cumulative distribution and the probability F _comp (v _lmt ) of the cumulative distribution to be compared is calculated as the deviation degree Dist _{diff from} the following (Equation 21).
Dist _diff = F _comp (v _lmt ) −F _std (v _lmt ) (Equation 21)

<< Fourth Embodiment >>
A vehicle driving support apparatus according to a fourth embodiment of the present invention will be described with reference to the drawings. FIG. 21 is a system diagram showing a configuration of the vehicle driving support device 1 according to the fourth embodiment of the present invention, and FIG. 22 is a configuration diagram of a vehicle on which the vehicle driving support device 1 is mounted.

First, the configuration of the vehicle driving support device 1 will be described.
The laser radar 10 is attached to a front grill part or a bumper part of the vehicle, and scans the front area of the host vehicle by irradiating infrared light pulses in the horizontal direction. The laser radar 10 measures the reflected wave of the infrared light pulse reflected by a plurality of reflectors in front (usually the rear end of the preceding vehicle), and determines the distance between the vehicles from the arrival time of the reflected wave to the plurality of obstacles. Detect distance and relative speed respectively. The detected inter-vehicle distance and relative speed are output to the controller 100. The forward area scanned by the laser radar 10 is about ± 6 deg with respect to the front of the host vehicle, and a forward object existing in this range is detected.

  The vehicle speed sensor 30 detects the vehicle speed of the host vehicle by measuring the number of wheel rotations and the number of rotations on the output side of the transmission, and outputs the detected host vehicle speed to the controller 100.

  The navigation system 50 includes a GPS receiver, a map database, a display monitor, and the like, and is a system that performs route search, route guidance, and the like. The navigation system 50 can acquire information such as the type of road on which the host vehicle is running and the road width based on the current position of the host vehicle obtained from the GPS receiver and the road information stored in the map database.

  The brake pedal stroke sensor 60 detects the amount of depression (the amount of brake pedal operation) when the driver depresses the brake pedal. The brake pedal stroke sensor 60 outputs the detected brake pedal operation amount to the controller 100. The blinker switch 65 detects the presence or absence of a blinker lever operation by the driver and outputs a detection signal to the controller 100.

  The controller 100 includes a CPU and CPU peripheral components such as a ROM and a RAM, and controls the entire vehicle driving support apparatus 1. The controller 100 analyzes driving characteristics of the driver based on signals input from the laser radar 10, the vehicle speed sensor 30, the brake pedal stroke sensor 60, the blinker switch 65, and the like, and performs driving diagnosis. Then, information is provided to the driver based on the driving diagnosis result. Information provided to the driver includes warnings to the driver and suggestions for improving driving operations. Specific control contents in the controller 100 will be described later.

  The speaker 130 provides information to the driver by a buzzer sound or voice according to a signal from the controller 100. The display unit 180 performs a display that gives a warning or improvement suggestion to the driver in accordance with a signal from the controller 100. The display unit 180 can use, for example, a display monitor or a combiometer of the navigation system 50.

Next, the operation of the vehicle driving support apparatus 1 according to the fourth embodiment will be described. First, the outline will be described.
The controller 100 performs a driving diagnosis of the driver based on the traveling state of the host vehicle and the driving operation of the driver, and gives an alarm to the driver and suggests an improvement of the driving operation according to the driving diagnosis result. Specifically, driving characteristics when the host vehicle is traveling following the preceding vehicle are detected, and driving diagnosis is performed using the detected driving characteristics as an index. And if the driver is driving at a higher risk than normal driving from the results of the driving diagnosis, that is, if the driver's driving deviates in the high risk direction, an alarm is given and a high risk is Notify the driver before falling into a bad state. On the other hand, if the driver's driving is better than the general standard from the driving diagnosis result, the information is presented, that is, the improvement is suggested, so that the driver's driving is given up so as to further improve the safety driving awareness.

  As described above, the vehicle driving support apparatus 1 according to the first embodiment has a function of detecting the driving of the driver by driving diagnosis, a function of giving an alarm to the driver according to the detection result, and a response of the detection result. The driver has three functions to give improvement suggestions to the driver. The driver knows his / her driving characteristics objectively and encourages the driver to introspect and presents advice according to the driving characteristics to the driver. Can learn how to drive lower risk.

  The operation of the vehicle driving support apparatus 1 according to the fourth embodiment will be described in detail with reference to FIG. FIG. 23 is a flowchart of the processing procedure of the driving support control process in the controller 100 according to the fourth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S400, the traveling state of the host vehicle is detected. Here, as the running state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10 are acquired. In step S405, the operation state of the driver is detected. Here, the brake pedal operation amount detected by the brake pedal stroke sensor 60 and the presence / absence of the winker lever operation detected by the winker switch 65 are acquired as the operation state of the driver.

In step S407, a margin time TTC and an inter-vehicle time THW between the host vehicle and the preceding vehicle are calculated in order to determine a travel scene of the host vehicle, which will be described later. The margin time TTC is a physical quantity indicating the current degree of proximity of the host vehicle with respect to the preceding vehicle. The margin time TTC indicates how many seconds later the inter-vehicle distance D becomes zero and the host vehicle and the preceding vehicle come into contact with each other when the current traveling state continues, that is, when the host vehicle speed V and the relative vehicle speed Vr are constant. It is a value and is obtained by the following (formula 22).
TTC = D / Vr (Formula 22)

The inter-vehicle time THW is an effect when it is assumed that the degree of influence on the margin time TTC due to a change in the vehicle speed of the assumed vehicle ahead, that is, the relative vehicle speed Vr changes when the host vehicle is following the preceding vehicle. It is a physical quantity indicating the degree. The inter-vehicle time THW is obtained by dividing the inter-vehicle distance D by the own vehicle speed V, and indicates the time until the own vehicle reaches the current position of the preceding vehicle. The inter-vehicle time THW is obtained from the following (Equation 23).
THW = D / V (Formula 23)

  In step S410, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only for a driving scene in which the host vehicle stably follows the same preceding vehicle.

An example of conditions for a stable follow-up driving scene is as follows.
(A) Following the same preceding vehicle (for example, the inter-vehicle distance has not changed by more than 4 m from the previous measured value)
(B) Not in a sudden approaching state (for example, margin time TTC exceeds 10 seconds)
(C) The inter-vehicle time THW is within a predetermined value (for example, the inter-vehicle time THW is less than 4 seconds)
(D) No brake operation by the driver (for example, the brake pedal operation amount is substantially 0)
(E) There is no blinker lever operation by the driver (for example, there is no ON signal input from the blinker switch 65).
(F) The states (a) to (e) are continuing (for example, continuing for 5 seconds or more).

  If all of the conditions (a) to (f) are satisfied, it is determined that the traveling scene of the host vehicle is a stable follow-up traveling scene, and the process proceeds to step S412 to perform driving diagnosis. On the other hand, if any of the conditions (a) to (f) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing driving diagnosis. The conditions for determining whether or not a stable follow-up driving scene is not limited to the above (a) to (f), and the presence or absence of a brake operation or the presence or absence of a blinker lever operation is detected separately. It is also possible to detect by means.

  In step S412, the travel location is determined. Specifically, an index number is labeled on the link ID described in the map information of the navigation system 50 based on the database. The link ID is an ID assigned to a link that connects nodes that are attribute change points at which road attributes change, and each link has data such as road type and link length (distance between nodes). doing. In step S414, the current time is recorded.

  In step S416, based on the labeling results of steps S412 and S414, data used for performing a driving diagnosis of the driver is stored. Here, for example, as shown in FIG. 24, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the tracking characteristic index within the link, the number of times the link traveled, and the like are written in the structure format Build a road database. In the fourth embodiment, the inter-vehicle time THW is used as a physical quantity representing the driver's follow-up characteristics. The method for calculating the tracking characteristic and the tracking characteristic index will be described in detail in the driving diagnosis process.

  In subsequent step S420, a driving diagnosis of the driver is performed using the data stored in step S416. The driving diagnosis is performed based on the driving characteristics of the driver in a driving scene in which the host vehicle stably follows the preceding vehicle. The driving characteristics during the follow-up traveling include, for example, the inter-vehicle time THW between the host vehicle and the preceding vehicle, the reciprocal of the inter-vehicle time THW, the inter-vehicle distance, the reciprocal of the inter-vehicle distance, and the like in the fourth embodiment. A case where THW is used will be described as an example.

  FIG. 25 shows a data structure of the vehicle driving support apparatus 1. The A layer represents the data amount of “at this time” for a relatively short time indicating the current driving state of the driver. The B layer represents the data amount of “this day” indicating the driving state of the driver for the day for a longer time than “at this time”. The C layer represents a driver's normal driving state, that is, a “normal” data amount indicating personal characteristics, which is longer than “this day”. The D layer represents an amount of data indicating “general public” driving characteristics for diagnosing each driver's driving in comparison with a general driver.

  The amount of data increases as the layer moves from the A layer to the D layer. The amount of data included in each layer corresponds to the number of samples when calculating the average value of the inter-vehicle time THW at “this time”, “this day”, and “normal”, and by changing the number of samples, as shown in FIG. The data structure is realized. The numerical value of the data included in each layer is updated as needed by real-time calculation described below.

  In the driving diagnosis process, driving in different time spans, that is, driving at this time, “this day”, and “ordinary” is detected using the data of each of the A layer to D layer. The driving diagnosis process executed in step S420 will be described in detail using the flowchart of FIG.

  In step S422, in order to perform the driving diagnosis of “at this time” of the driver, the following characteristic value of the driver at “at this time” is calculated. As the driver's following characteristic value, an average value Mean_x (n) and a standard deviation Stdev_x (n) of the inter-vehicle time THW within a predetermined time defining “at this time” are calculated. Here, the predetermined time defining “at this time” is, for example, 60 seconds, and the inter-vehicle time THW is determined using data for 60 seconds from the past to the present detected in the stable follow-up driving scene determined in step S410. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. The following parameters are used to calculate the mean value Mean_x (n) and the standard deviation Stdev_x (n).

x (n): data acquired this time, that is, the inter-vehicle time THW calculated in step S407
K: Number of THW data calculated within the specified time
M ₁ (n): Total value of THW within the predetermined time calculated this time
M ₂ (n): THW squared sum within the predetermined time calculated this time
M ₁ (n-1): Total value of THW calculated within the predetermined time previously calculated
M ₂ (n-1): The sum of squared THW values within the predetermined time previously calculated
Mean_x (n): Average value of this data, that is, average value of THW
Var_x (n): Distribution of data this time, that is, THW distribution
Stdev_x (n): Standard deviation of this data, that is, THW standard deviation

  Here, the data number K is determined by the predetermined time × the number of samplings (samples) per second. That is, if the predetermined time “at this time” is 60 seconds and the sampling number is 5 Hz, the number of data is K = 300.

The total value M ₁ (n) and the square sum M ₂ (n) can be calculated from the following (Equation 24) and (Equation 25) using these parameters.
M ₁ (n) = M ₁ (n−1) + x (n) −M ₁ (n−1) / K (Equation 24)
M ₂ (n) = M ₂ (n−1) + (x (n)) ² −M ₂ (n−1) / K (Equation 25)

The mean value Mean_x (n), variance Var_x (n), and standard deviation Stdev_x (n) of the inter-vehicle time THW at this time are calculated from the following (Equation 26), (Equation 27), and (Equation 28), respectively. Can do.
Mean_x (n) = M ₁ (n) / K (Equation 26)
Var_x (n) = M ₂ (n) / K− (M ₁ (n)) ² / K ² (Equation 27)
Stdev_x (n) = √ (Var_x (n)) (Equation 28)

  In step S424, in order to perform the driving diagnosis of “this day” of the driver, the following characteristic value of the driver of “this day”, that is, an average value of the inter-vehicle time THW within a predetermined time defining “this day” Calculate Mean_x (n) and standard deviation Stdev_x (n). Here, the predetermined time for defining “this day” is, for example, 360 seconds, and the data for 360 seconds from the past to the present detected in the stable follow-up traveling scene determined in step S410 is used. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 26) and (Formula 28) as in “At this time”. Here, the data number K is 1800 when the predetermined time of this day is 360 seconds and the sampling number is 5 Hz.

  In step S426, in order to perform the "normal" driving diagnosis of the driver, the follow-up characteristic value of the "normal" driver, that is, the average value Mean_x (n) of the inter-vehicle time THW within a predetermined time defining "normal". ) And standard deviation Stdev_x (n). Here, the predetermined time for defining “ordinary” is, for example, 2160 seconds, and the average of the inter-vehicle time THW using data for 2160 seconds from the past to the present detected in the stable follow-up traveling scene determined in step S410. The value Mean_x (n) and the standard deviation Stdev_x (n) are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 26) and (Formula 28) as in “At this time”. Here, the data number K is 10800 when the predetermined time of “normal” is 2160 seconds and the sampling number is 5 Hz.

  In the subsequent processing after step S428, the driver is diagnosed for driving using the following characteristic values calculated in steps S422, S424, and S426. Here, the following characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two deviate from each other. That is, in the data structure shown in FIG. 25, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

  First, in step S428, a deviation degree indicating how much the driver's following characteristic at this time is different from the following characteristic of the driver on this day is calculated. Here, the deviation degree of “this time” with respect to “this day” indicates a difference between the distribution of the inter-vehicle time THW of “this day” and the distribution of the inter-vehicle time THW of “at this time”. In order to calculate the deviation degree of “this time” with respect to “this day”, the distribution of the inter-vehicle time THW of “this day” is used as a reference distribution representing the long-time behavior distribution, and the inter-vehicle time THW of “at this time”. Is used as a comparison target distribution representing a short-time behavior distribution.

As a method of calculating the deviation degree, here, the position of “average value−standard deviation” (comparison value x _std ) of the action distribution for a long time (for example, “this day”) is used for a short time (for example, “at this time”). ) And a long time (for example, “this day”) distribution function is used.

  For calculating the deviation degree, the probability density function is calculated using the mean value Mean_x (n) and standard deviation Stdev_x (n) of the inter-vehicle time THW calculated in steps S422 and S424, assuming the inter-vehicle time THW as a normal distribution. To do. FIG. 27A shows the frequency distribution of the inter-vehicle time THW actually calculated and the probability density distribution (represented by a solid line) of the inter-vehicle time THW approximated by a normal distribution, and FIG. 27B shows these cumulative distributions. Show. As shown in FIGS. 27A and 27B, it can be seen that the distribution when the inter-vehicle time THW is assumed to be a normal distribution and the actual distribution agree well.

Therefore, as shown in FIGS. _{28A and 28B,} in the comparison target region set based on the predetermined value (comparison value x _std ), the comparison target is compared with the short normal distribution for a long time. How much the normal distribution of time deviates is calculated as a deviation degree Dist _diff . Specifically, the difference between the comparison distribution and the reference distribution in the region where the inter-vehicle time THW is shorter than the comparison value x _std (the area of the hatched portion in FIG. 28A, the length of the arrow in FIG. 28B) Is equivalent to the deviation degree Dist _diff . The calculation methods shown in FIGS. 28C and 28D will be described later.

  29 (a) and 29 (b) show the probability density distribution and cumulative distribution calculated based on the results obtained from actual public road experiments. In FIG. 29 (a), the probability density distribution when the inter-vehicle time THW is approximated by a normal distribution using the mean value Mean_x (n) and the standard deviation Stdev_x (n) of “at this time” is indicated by a one-dot chain line. The probability density distribution when the inter-vehicle time THW is approximated by a normal distribution using the mean value Mean_x (n) and standard deviation Stdev_x (n) of “day” is shown by a solid line. In FIG. 29B, the cumulative distribution of “At this time” is indicated by a one-dot chain line, and the cumulative distribution of “this day” is indicated by a solid line. 29 (a) and 29 (b), the mean value Mean_x (n) = 1.22 of the inter-vehicle time THW at this time and the standard deviation Stdev_x (n) = 0.80, and the inter-vehicle time of “this day” The average value of THW is Mean_x (n) = 1.63, and standard deviation Stdev_x (n) = 1.00.

First, the comparison value x _std is calculated from the following (Expression 29) from the average value Mean_std and the standard deviation Stdev_std of the reference distribution.
x _std = Mean_std−Stdev_std (Equation 29)
The comparison value x _std is a value of the inter-vehicle time THW that indicates at which position the reference distribution and the comparison distribution are compared, and corresponds to a position indicated by a broken line in FIGS.

Next, the cumulative distribution value in the reference distribution comparison value x _std is calculated. The probability density function f (x) of the normal distribution can be calculated from the following (Equation 30) where the average value is μ and the standard deviation is σ (see FIG. 28A).
... (Formula 30)

When the probability density function f (x) calculated in (Expression 30) is integrated, a cumulative distribution function F (x) is obtained as shown in (Expression 31) below (see FIG. 28B).
... (Formula 31)

When the average value of the standard distribution is set to μ _std and the standard deviation is set to σ _std , the probability F _std (x) of the cumulative distribution in the comparison value x _std is calculated by the following (formula 32).
... (Formula 32)

Next, the value of the cumulative distribution in the comparison value x _std of the comparison distribution is calculated. When the average value of the comparison distribution is μ _comp and the standard deviation is σ _comp , the probability F _comp (x) of the cumulative distribution at the comparison value x _std is calculated by the following (Equation 33).
... (Formula 33)

The difference between the probability F _std (x) of the reference cumulative distribution and the probability F _comp (x) of the cumulative distribution to be compared is defined as the following (formula 34) as the deviation degree Dist _diff of “at this time” with respect to “this day” ).
Dist _diff = F _comp (x) −F _std (x) (Formula 34)
As the deviation degree Dist _diff increases in the positive direction, it means that the driving of the driver at this time is biased toward the direction in which the inter-vehicle time THW is shorter than the driving on the day, that is, the high risk direction. As the deviation degree Dist _diff increases in the negative direction, the driver's driving at this time tends to have a longer inter-vehicle time THW than the driving on this day, that is, the risk tends to decrease. When the driver always drives with the same following characteristic, the deviation degree Dist _diff is zero.

Further, in step S428, the deviation degree Dsit _diff of “at this time” with respect to “normal” is also calculated. In this case, the “ordinary” inter-vehicle time THW distribution is used as a reference distribution representing a long-time behavior distribution, and the “inter-vehicle” time THW distribution is used as a comparison target distribution representing a short-time behavior distribution. Use. Then, by using the probability F _std (x) of the “normal” cumulative distribution and the probability F _comp (x) of the cumulative distribution “at this time”, “at this time” with respect to “normal” from the above-described (Equation 34). The deviation degree Dist _diff is calculated.

As described above, after calculating the deviation degree Dist _diff for “this time” for “this day” and “this time” for “normal” in step S428, the process proceeds to step S430. In step S430, similar to the processing in step S428, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the distribution of the “normal” inter-vehicle time THW is used as a reference distribution representing the long-time behavior distribution, and the distribution of the inter-vehicle time THW of “this day” is the comparison target distribution representing the short-time behavior distribution. Used as

In the subsequent step S432, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as the processing in step S428. Here, the distribution of the inter-vehicle time THW of “general public” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the comparison target representing the short-time behavior distribution is used as the distribution of the “normal” inter-vehicle time THW. Used as As the follow-up characteristic values of “general public”, that is, the average value and the standard deviation of the inter-vehicle time THW, appropriate values are set in advance as fixed values.

As described above, after the driver's driving diagnosis is performed using the data obtained at the plurality of different time spans in step S420, the process proceeds to step S440. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “normal” is Dist_1a, the deviation degree Dist _diff of “this time” for “this day” is Dist_1b, and “this day” for “normal” The departure degree Dist _diff is Dist_2, and the “ordinary” departure degree Dist _diff for “general public” is Dist_3.

  In step S440, it is determined whether or not to execute the alarm presentation process based on the driving diagnosis result in step S420. Here, the deviation degree Dist_1a of “at this time” with respect to “normal” calculated in step S428, the deviation degree Dist_1b of “at this time” with respect to “this day”, or “this day” with respect to “normal” calculated in step S430. It is determined whether or not the deviation degree Dist_2 is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1a, Dist_1b, or Dist_2 is larger than a threshold value, it progresses to Step S450 and gives a warning to a driver. After the alarm is presented, this process is terminated.

  For example, if the deviation Dist_2 of “this day” relative to “normal” is greater than the threshold, the speaker 130 will sound a buzzer sound “Today, the distance between the vehicles is shorter than usual. Output. The voice information is set so as to inform the driver that the driver tends to take a shorter inter-vehicle distance than usual or to take a longer inter-vehicle distance than the current inter-vehicle distance. When the deviation degree Dist_1a of “at this time” with respect to “normal” is larger than the threshold value, a sound such as “Hurry? Specific audio information is not limited to these. Even when the deviation degree Dist_1b of “at this time” with respect to “this day” is larger than the threshold, appropriate audio information set in advance is output.

  When a negative determination is made in step S440 and no alarm presentation is performed, the process proceeds to step S460, and it is determined whether or not the guidance presentation process is executed based on the driving diagnosis result in step S420. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S432 is smaller than a threshold (for example, 0.07) for determining whether to provide guidance (improvement suggestion) or not. Determine whether. Here, the threshold value for determining whether or not to present the instruction is appropriately set in advance as a value such that the deviation degree is almost always within the range for the same driver. The value of “0.07” described above is set based on the result of an actual vehicle experiment by 15 subjects. According to this experimental result, the degree of deviation calculated for each subject was always within 0.07.

When Dist_3 is smaller than the threshold value, the process proceeds to step S470, and guidance is presented to the driver. This process is complete | finished after giving instruction | indication presentation.
For example, the display of the content which abandons a driver | operator and the audio | voice are output as guidance presentation content. For example, the “ordinary” deviation degree Dist_3 is converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation. That is, the score of the driver who tends to increase the risk is 50 points or less, and the score of a good driver who tries safe driving is 50 points or more. The score is displayed within a range of 0 to 100. When the score obtained by converting the deviation degree Dist_3 is 100 or more, it is 100 points, and when it is 0 or less, 0 points.

  Further, as shown in FIG. 30, the distribution of general inter-vehicle distances is schematically displayed on the display unit 180 so that the driver's “ordinary” inter-vehicle distance can be understood with respect to the general public. Display. FIG. 30 shows that the driver's inter-vehicle distance is longer by two stages than the average value of the general inter-vehicle distance, and that it has a tracking characteristic with a margin more than the general public. It is provided to the driver as visual information.

  In addition, the driver's usual distance between vehicles tends to be longer than that of a general driver, and the driver is informed by voice output from the speaker 130 that the driver is an excellent driver who performs a follow-up driving with a margin. For example, it outputs a voice saying "You are driving safely. Let's do our best in this way." In this way, it is possible to inform the driver that the driver's individual following driving characteristics are excellent for the general public driver, and to display or encourage the driver to maintain good driving or improve it. Output audio.

  In addition, in the case of display and voice output, the example of using the term “distance between vehicles” familiar to the driver has been explained, but of course it is also possible to perform display and voice output using the term “distance between vehicles” It is. The contents of the display and audio information convey the driving tendency to the driver in an easy-to-understand manner and provide an improvement so that a warning is given so as not to reach a high-risk state and good driving is further improved. If it can be performed, it will not be limited to the example mentioned above.

  Another example of the display based on the driving diagnosis result is shown in FIG. In FIG. 31, the vertical axis indicates the characteristics of the individual driver with respect to the general tracking characteristics, and the horizontal axis indicates the current state with respect to the driver's normal tracking characteristics. As the tracking characteristic, for example, the inter-vehicle time THW and the inter-vehicle distance can be used as described above. The greater the deviation of “this day” from “ordinary” calculated in step S430, the more the marker M in the figure is moved to the right, that is, the high risk direction. Further, as the degree of deviation from “general” with respect to “general public” calculated in step S432 is larger, the marker M in the figure is moved upward, that is, in a high risk direction.

  In this way, in the display example shown in FIG. 31, the driving diagnosis result based on “general public” and the driving diagnosis result based on “ordinary”, that is, the personal characteristics of the driver, are set from two axes. Displayed on a two-dimensional map. It should be noted that the horizontal axis can set the degree of departure at “this time” with respect to “this day” and the degree of departure at “at this time” with respect to “normal”. It is also possible to display the vertical axis and the horizontal axis interchanged.

  In addition, as shown in FIG. 31, the two-dimensional map can be divided into a plurality of blocks, and each block can be color-coded so as to make it easy to understand where the driver's following characteristic is. For example, the upper right block in FIG. 31 may be displayed in red, the color may be lighter as it moves to the lower left, and the lower left block may be displayed in light blue.

In the fourth embodiment described above, the following operational effects can be obtained in addition to the effects of the first to third embodiments described above.
The driver's safety awareness and driving preference characteristics are detected as indicators for driving diagnosis. Specifically, the inter-vehicle time THW between the host vehicle and the preceding vehicle when the host vehicle travels following the preceding vehicle is calculated. The inter-vehicle time THW represents the time until the host vehicle reaches the current position of the preceding vehicle. Therefore, the inter-vehicle time THW is used as an index that represents the characteristics of the driver when the host vehicle follows the preceding vehicle. Accurate driving diagnosis can be performed in the driving scene.

-Modification of the fourth embodiment-
Hereinafter, another method for calculating the deviation degree Dist _diff will be described. Here, the behavior distribution for a long time (for example, “this day”) and the behavior distribution for a short time (for example, “at this time”) are approximated by normal distributions, respectively, and the size of the area where the two normal distributions do not overlap is calculated. Deviation degree is calculated as Dist _diff .

Specifically, as shown in FIGS. 28C and 28D, the difference between the comparison distribution and the reference distribution in a region where the inter-vehicle time THW is shorter than the intersection α between the reference distribution and the comparison distribution, that is, more than the reference distribution. The size of the comparison distribution area of the protruding portion is calculated as the deviation degree Dist _diff .

When the average value μ _std and standard deviation σ _std of the reference distribution and the average value μ _comp and standard deviation σ _{comp of the} comparison distribution are substituted into the above (Equation 30) and calculated, the probability density function f _std ( x) and the probability density function f _comp (x) of the comparative distribution are expressed by the following (Equation 35) and (Equation 36), respectively.
... (Formula 35)
... (Formula 36)

When these simultaneous equations of (Equation 35) and (Equation 36) are obtained, the frequencies of the respective distributions coincide at two points α and β (α <β). In order to obtain the area in which the two normal distributions do not overlap in the region where the inter-vehicle time THW is shorter than the intersection α, the above (formula 32) for obtaining the cumulative distribution function of the reference distribution and the above for obtaining the cumulative distribution function of the comparative distribution The intersection point α is substituted into (Expression 33). Then, the probability F _std (α) of the cumulative distribution of the reference distribution is subtracted from the probability F _comp (α) of the cumulative distribution of the comparative distribution at the intersection α.

The deviation degree Dist _diff can be calculated by the following (formula 37).
-Dist _diff = F _comp (α) −F _std (α) (Expression 37)
In addition, the area Dist _corr where two normal distributions overlap is obtained by the following (formula 38).
-Dist _corr = 1--Dist _diff (Equation 38)
In this way, it is possible to compare the distributions of a plurality of data having different time ranges, calculate the degree of coincidence of distributions, that is, calculate the overlapping area of the distributions, and perform driving diagnosis.

<< Fifth Embodiment >>
The vehicle driving support apparatus according to the fifth embodiment of the present invention will be described below. The basic configuration of the vehicle driving support apparatus according to the fifth embodiment is the same as that of the fourth embodiment shown in FIG. Here, differences from the above-described fourth embodiment will be mainly described.

  In the vehicle driving support device 1 according to the fifth embodiment, when driving conditions such as a time zone and a place in a day are matched, the past driving data is compared with the current driving data and the driver's driving data is compared. Perform driving diagnosis.

  The operation of the vehicle driving support apparatus 1 according to the fifth embodiment will be described in detail with reference to FIG. FIG. 32 is a flowchart of the process procedure of the driving support control process in the controller 100 according to the fifth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S500, as the running state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10 are acquired. In step S502, the brake pedal operation amount detected by the brake pedal stroke sensor 60 and the presence / absence of the winker lever operation detected by the winker switch 65 are acquired as the driver's operation state. In step S503, a margin time TTC and an inter-vehicle time THW between the host vehicle and the preceding vehicle are calculated in order to determine a travel scene of the host vehicle, which will be described later.

  In step S504, the travel location is determined, and in step S506, the travel time is recorded. In step S508, based on the labeling results in steps S504 and S506, data used for driving diagnosis of the driver is stored. The processing up to data storage is basically the same as in the fourth embodiment described above. However, the travel scene is determined after the travel history is determined.

  In the subsequent step S510, it is determined whether or not there is a database of roads on which the host vehicle is currently traveling based on the travel road database created in step S508. That is, it is determined whether or not past data having the same spatial position and range corresponding to the current position of the host vehicle has been stored, since the host vehicle has traveled on the currently traveling road. If there is a database, the process proceeds to step S515. If there is no database, this process ends.

  In step S515, the traveling scene of the host vehicle is determined. When the above-described conditions (a) to (f) are all satisfied, it is determined that the traveling scene of the host vehicle is a stable follow-up traveling scene, and the process proceeds to step S520 to perform driving diagnosis. On the other hand, if any of the conditions (a) to (f) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing driving diagnosis.

  In subsequent step S520, the driving diagnosis of the driver is performed using the data saved in step S508. The driving diagnosis is performed based on the driving characteristics of the driver in a driving scene in which the host vehicle stably follows the preceding vehicle in the same driving condition, that is, in the same spatial position and range. The driving characteristics during the follow-up traveling include, for example, the inter-vehicle time THW between the host vehicle and the preceding vehicle, the reciprocal of the inter-vehicle time THW, the inter-vehicle distance, the reciprocal of the inter-vehicle distance, etc. In the fifth embodiment, the inter-vehicle time A case where THW is used will be described as an example.

  FIG. 33 shows a data structure of the vehicle driving support apparatus 1 according to the fifth embodiment. The A layer represents the data amount of “at this time” for a relatively short time indicating the current driving state of the driver. The B layer represents the data amount of “this day” indicating the driving state of the driver for the day for a longer time than “at this time”. The C layer represents a driver's normal driving state, that is, a “normal” data amount indicating personal characteristics, which is longer than “this day”. The D layer represents an amount of data indicating “general public” driving characteristics for diagnosing each driver's driving in comparison with a general driver.

  The amount of data increases as the layer moves from the A layer to the D layer. The amount of data included in each layer corresponds to the number of samples when calculating the average value of the inter-vehicle time THW at “this time”, “this day”, and “normal”, and by changing the number of samples, as shown in FIG. The data structure is realized. The numerical value of the data included in each layer is updated as needed by the real-time calculation described in the fourth embodiment.

  In the fifth embodiment, data on the following characteristics when traveling on the same road in the past is stored, and the data at the time of the previous travel, or all the data when traveled in the past, and the current data are stored. By comparing, the driver's driving of “this time” and “this day” is determined. The driving diagnosis process executed in step S520 will be described in detail using the flowchart of FIG.

  In step S522, in order to perform the driving diagnosis of the driver at this time, the following characteristic value of the driver at the time, that is, the average value Mean_x of the inter-vehicle time THW within a predetermined time that defines the time is determined. (n) and the standard deviation Stdev_x (n) are calculated using (Equation 26) and (Equation 28) described above. In step S524, in order to perform the driving diagnosis of “this day” of the driver, the following characteristic value of the driver of “this day”, that is, the average value of the inter-vehicle time THW within the predetermined time defining “this day” Mean_x (n) and standard deviation Stdev_x (n) are calculated using (Equation 26) and (Equation 28).

  In step S526, past data serving as a reference for comparison with the current follow-up characteristic is read. Here, the following characteristic data at the time of the previous travel on the same road, specifically, the mean value Mean_x (n) and standard deviation Stdev_x (n) of the inter-vehicle time THW are read from the database.

  In the subsequent steps S528 and S530, the driver is diagnosed for driving using the following characteristic values calculated in steps S522 and S524. Here, the current driver's follow-up characteristics are compared with the driver's follow-up characteristics based on the data obtained when traveling on the same road last time, and the driver's driving is diagnosed based on how far the two differ. To do. That is, in the data structure shown in FIG. 33, for example, driving diagnosis is performed by comparing the current A layer with the previous A layer.

  First, in step S528, a deviation degree is calculated that indicates how far the driver's following characteristic at this time has deviated from the following characteristic of the driver at this time when traveling the previous time. Here, the degree of deviation of “this time” from “this time” at the time of the previous run is the distribution of the inter-vehicle time THW of “at this time” at the time of the previous run and the inter-vehicle time THW of this time “at this time”. It shows the difference in the distribution of. The distribution of the inter-vehicle time THW at this time during the previous run is used as a reference distribution, and the distribution of the inter-vehicle time THW at the present time is used as a comparison target distribution.

Specifically, using the calculation method shown in FIGS. 28A and 28B described in the fourth embodiment, or the calculation method shown in FIGS. Alternatively, the deviation degree Dist _diff of “this time” of this time with respect to “this time” of the previous run is calculated from (Expression 37).

In step S530, as in step S528, the deviation degree Dist _diff of “this day” of this time with respect to “this day” of the previous run is calculated. Here, the distribution of the inter-vehicle time THW of “this day” when traveling on the same road last time is used as a reference distribution, and the distribution of the inter-vehicle time THW of “this day” of this time is used as a comparison target distribution.

As described above, after the driver's driving diagnosis is performed using the data when traveling on the same road in the past and the data when traveling this time, the process proceeds to step S540. In the following, for convenience of explanation, the deviation degree Dist _diff of this time with respect to “this time” during the previous run is Dist_10, and the deviation degree Dist of this day with respect to “this day” during the previous run. Let _diff be Dist_20.

  In step S540, it is determined whether or not to execute the alarm presentation process based on the driving diagnosis result in step S520. Here, the deviation degree Dist_10 of the current “this time” with respect to the previous “this time” calculated in step S528, or the deviation degree Dist_20 of the current “this day” with respect to the previous “this day” calculated in step S530. Then, it is determined whether or not it is larger than a threshold value (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_10 or Dist_20 is larger than a threshold value, it progresses to step S550 and a warning is shown to a driver. After the alarm is presented, this process is terminated.

  For example, when the deviation Dist_20 of “this day” is larger than the threshold value than the previous time, the speaker 130 outputs a sound with a buzzer sound “Today, the distance between vehicles is shorter than usual. To do. The voice information is set so as to notify that there is a tendency to take a shorter intervehicular distance than the previous time or to take a longer intervehicular distance than the current intervehicular distance. In addition, when the deviation degree Dist_20 of “this day” with respect to the previous time is larger than the threshold value, when the deviation degree Dist_10 of “at this time” with respect to the previous time is larger than the threshold value, both of the deviation degrees Dist_10 and Dist_20 are larger than the threshold value. In some cases, the contents of the audio information can be changed.

  Furthermore, as shown in FIG. 35, the current tendency of the inter-vehicle distance can be provided to the driver as visual information displayed on the display unit 180. In the display example shown in FIG. 35, the reference distribution (for example, the distribution of the inter-vehicle distance at the time of the previous run, the distribution of the general inter-vehicle distance, etc.) is schematically displayed, and how much the driver's current inter-vehicle distance is. Display to see if there is. FIG. 35 shows that the driver's inter-vehicle distance is shorter by two steps than the average value in the standard distribution, and visual information indicates that the current driver's method of taking the inter-vehicle distance tends to be short. Presented to the driver.

  When a negative determination is made in step S540 and no alarm presentation is performed, the process proceeds to step S560, and it is determined whether or not the guidance presentation process is to be executed based on the driving diagnosis result in step S520. Here, the deviation degree Dist_10 of the current “this time” with respect to the previous “this time” calculated in step S528, or the deviation degree Dist_20 of the current “this day” with respect to the previous “this day” calculated in step S530. It is determined whether the value is negative.

  When Dist_10 or Dist_20 is a negative value and the driving operation of the driver is improved as compared to the previous time, the process proceeds to step S570, and the driver is presented with guidance. This process is complete | finished after giving instruction | indication presentation. For example, the display of the content which abandons a driver | operator and the audio | voice are output as guidance presentation content. For example, it outputs a voice saying "You are driving safely. Let's do our best in this way."

  In addition, in the case of display and voice output, the example of using the term “distance between vehicles” familiar to the driver has been explained, but of course it is also possible to perform display and voice output using the term “distance between vehicles” It is. Moreover, it can also be comprised so that the display and audio | voice output similar to 4th Embodiment mentioned above may be performed.

  In the said 5th Embodiment, when driving | running | working on the same road as driving conditions, it comprised so that a driver | operator's driving | operation diagnosis might be performed. That is, it was determined whether the spatial range of the driving operation of the driver matched as the driving condition. Here, it is also possible to determine whether or not the time range for the driving operation, such as the traveling time zone, is matched as the traveling condition. It can also be determined as a traveling condition whether the spatial range and the temporal range of the driving operation match, such as traveling on the same road in the same time zone.

  In the fifth embodiment described above, the driver's safety consciousness / driving preference characteristics are detected as an index for driving diagnosis. Specifically, the inter-vehicle time THW between the host vehicle and the preceding vehicle when the host vehicle travels following the preceding vehicle is calculated. The inter-vehicle time THW represents the time until the host vehicle reaches the current position of the preceding vehicle. Therefore, the inter-vehicle time THW is used as an index that represents the characteristics of the driver when the host vehicle follows the preceding vehicle. Accurate driving diagnosis can be performed in the driving scene.

-Modification of the fifth embodiment-
A plurality of data calculated / accumulated when traveling on the same road in the past can also be used as past data serving as a reference for comparison with the current tracking characteristic. Specifically, data of all past tracking characteristic values when traveling on the same road stored in the database is read and used.

When handling data of a plurality of following characteristic values, these data are replaced with the representative values. For example, when the mean value Mean_x (n) and the standard deviation Stdev_x (n) of the inter-vehicle time THW are used as the follow-up characteristic value, the representative value is calculated using the addition theorem. For example, when using two sets of data acquired in the past, N ₁ (μ ₁ , σ ₁ ² ) and N ₂ (μ ₂ , σ ₂ ) are expressed as N for distribution, μ for mean value, and σ for standard deviation. The representative value of ² ) is N (μ ₁ + μ ₂ , σ ₁ ² + σ ₂ ² ).

<< Sixth Embodiment >>
The vehicle driving support apparatus according to the sixth embodiment of the present invention will be described below. The basic configuration of the vehicle driving support apparatus according to the sixth embodiment is the same as that of the fourth embodiment shown in FIG. Here, differences from the above-described fourth embodiment will be mainly described.

In the vehicle driving support apparatus 1 according to the sixth embodiment, as in the fourth embodiment described above, the driver's tracking characteristic when following the preceding vehicle is calculated as a probability density function, and different times are calculated. The driving diagnosis is performed by calculating a deviation degree Dist _diff representing a difference between a plurality of probability density distributions acquired by bread. Here, in the sixth embodiment, a probability density function representing the following characteristic is calculated recursively using the vehicle traveling state data and the driver's operation state data during the following traveling. To do.

  The operation of the vehicle driving support apparatus 1 according to the sixth embodiment will be described in detail with reference to FIG. FIG. 36 is a flowchart of the process procedure of the driving support control process in the controller 100 according to the sixth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec. The processing in steps S600 to S616 is the same as the processing in steps S400 to S416 in the flowchart shown in FIG.

  In step S620, a driving diagnosis of the driver is performed using the data stored in step S616. The driving diagnosis is performed based on the driving characteristics of the driver in a driving scene in which the host vehicle stably follows the preceding vehicle. The driving characteristics during the follow-up traveling include, for example, the inter-vehicle time THW between the host vehicle and the preceding vehicle, the reciprocal of the inter-vehicle time THW, the inter-vehicle distance, the reciprocal of the inter-vehicle distance, etc. In the sixth embodiment, the inter-vehicle time A case where THW is used will be described as an example.

  The data structure of the vehicle driving support apparatus 1 is the same as that of the fourth embodiment shown in FIG. Layer A shows a relatively short amount of data “at this time” indicating the current driving state of the driver, and layer B shows the driving state of the driver that day for a longer time than “at this time”. It represents the amount of data for this day. Layer C represents the normal driving state of the driver for a longer time than “this day”, that is, “normal” data amount indicating personal characteristics, and layer D represents the driving of each driver in general. It represents the amount of data indicating the driving characteristics of “general public” for diagnosis compared with the driver.

  The amount of data increases as the layer moves from the A layer to the D layer. The amount of data included in each layer corresponds to the number of samples when calculating the average value of the inter-vehicle time THW at “this time”, “this day”, and “normal”, and by changing the number of samples, as shown in FIG. The data structure is realized. The numerical value of the data included in each layer is updated as needed by real-time calculation described below.

  In the driving diagnosis process, the data of the A layer to D layer are used to determine driving at different time spans, that is, driving at this time, “this day”, and “ordinary”. The driving diagnosis process executed in step S620 will be described in detail using the flowchart of FIG.

  In step S622, the driver's follow-up characteristic value at this time is calculated in order to perform the driver's driving diagnosis at this time. As the driver's following characteristic value, the distribution of the inter-vehicle time THW within a predetermined time defining “at this time” is calculated. Here, the predetermined time (time window) defining “at this time” is, for example, 60 seconds, and the data for 60 seconds from the past to the present detected in the stable follow-up traveling scene determined in step S610 is used. The distribution of the inter-vehicle time THW is calculated.

  Specifically, in order to calculate the distribution of the inter-vehicle time THW, the probability density of the inter-vehicle time THW within a predetermined time defining “at this time” is calculated recursively. Therefore, first, as shown in FIG. 38, a plurality of bins for storing the inter-vehicle time THW data are prepared. Each bin is set in increments of 0.2 from THW = 0 to THW = 4, and a total of 21 bins Bin (array) are prepared. In the example shown in FIG. 38, it is set so that THW data from 0.0 to 0.1 is placed in the leftmost bin of THW0.0 (Bin_1), and the second bin from the left of THW0.2 (Bin_2). It is set so that data of 0.1 to 0.3 is entered.

Next, a predetermined value is entered as an initial value in each of the bins Bin_1 to Bin_21.
The predetermined value is set such that the sum of the predetermined values placed in all the bins is 1. For example, as shown in FIG. 39A, a predetermined value (= 1 / total number of bins) can be set so that all bins Bin_1 to Bin_21 have the same value (1/21). However, in the case of the inter-vehicle time THW or the like that shows a suspension-like distribution having a certain value in the center, it is desirable to set the previously calculated distribution to a predetermined value as shown in FIG. That is, the probability density of the previously calculated distribution is set as the initial value of each bin Bin_1 to Bin_21. As a result, the distribution can be calculated with higher accuracy. 39A and 39B, the horizontal axis indicates the inter-vehicle time THW (s), and the vertical axis indicates the probability density.

  The predetermined time (time window) for defining “at this time” is, for example, 60 seconds as described above. The number of data in the set time window is defined as N.

Next, the probability density is calculated in real time.
Each time new data of the inter-vehicle time THW is acquired, it is determined to which bin the new inter-vehicle time THW corresponds. The method of calculating the probability density is different from the bin that contains new data and the bin that does not. The probability density Bin_x (n) of a bin containing new data is calculated using (Equation 39) below.
Bin_x (n) = {Bin_x (n−1) + 1 / N} ÷ (1 + 1 / N) (Equation 39)
The probability density Bin_x (n) of a bin that does not contain new data is calculated using the following (Equation 40).
Bin_x (n) = {Bin_x (n−1)} ÷ (1 + 1 / N) (Equation 40)

  Thus, by calculating the probability density of the inter-vehicle time THW at this time in real time, a distribution of the inter-vehicle time THW that closely matches the actual distribution can be obtained. FIG. 40 shows an example of the distribution of the inter-vehicle time THW. In the distribution shown in FIG. 40, mode values (mode values) appear at Bin_5. For ease of viewing, only 10 bins Bin_1 to Bin_10 are shown.

  In step S624, in order to perform a driving diagnosis of “this day” of the driver, the following characteristic value of the driver of “this day”, that is, the distribution of the inter-vehicle time THW within a predetermined time defining “this day” is calculated. calculate. Here, the predetermined time (time window) defining “this day” is, for example, 360 seconds, and the data for 360 seconds from the past to the present detected in the stable follow-up traveling scene determined in step S610 is used. The distribution of the inter-vehicle time THW is calculated in the same manner as “at this time” described above.

  In step S626, in order to perform the “normal” driving diagnosis of the driver, the follow-up characteristic value of the “normal” driver, that is, the distribution of the inter-vehicle time THW within a predetermined time defining “normal” is calculated. Here, the predetermined time (time window) for defining “usually” is, for example, 2160 seconds, and using data for 2160 seconds from the past to the present detected in the stable follow-up traveling scene determined in step S610, The distribution of the inter-vehicle time THW is calculated in the same manner as “at this time” described above.

  In the subsequent processing after step S628, the driver's following characteristics based on the data obtained in different time spans calculated in steps S622, S624, and S626 are respectively compared, and the driver is determined based on how far the two differ. Diagnose your driving. That is, in the data structure shown in FIG. 25, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

  First, in step S628, a deviation degree indicating how much the driver's following characteristic at “this time” is different from the following characteristic of the driver on this day is calculated. Here, the deviation degree of “this time” with respect to “this day” indicates a difference between the distribution of the inter-vehicle time THW of “this day” and the distribution of the inter-vehicle time THW of “at this time”.

Therefore, the mode value of the distribution of the inter-vehicle time THW of “this day” that is the reference distribution and the mode value of the distribution of the inter-vehicle time THW of “at this time” that is the distribution to be compared are calculated. Then, the probability of the inter-vehicle time THW distributed on the side where the inter-vehicle time THW is shorter than each mode value, that is, in a high-risk direction is obtained. The probability F _std of the reference distribution on the high risk side and the probability F _{comp of the} comparison distribution can be calculated from the following (Equation 41) and (Equation 42), respectively.
... (Formula 41)
... (Formula 42)

The deviation degree Dist _diff of “at this time” with respect to “this day” can be calculated from the following (formula 43).
Dist _diff = F _comp -F _std (Equation 43)
As the deviation degree Dist _diff increases in the positive direction, it means that the driving of the driver at this time is biased toward the direction in which the inter-vehicle time THW is shorter than the driving on the day, that is, the high risk direction. As the deviation degree Dist _diff increases in the negative direction, the driver's driving at this time tends to have a longer inter-vehicle time THW than the driving on this day, that is, the risk tends to decrease. When the driver always drives with the same following characteristic, the deviation degree Dist _diff is zero.

Further, in step S628, the deviation degree Dsit _diff of “at this time” with respect to “normal” is also calculated. In this case, the “ordinary” inter-vehicle time THW distribution is used as a reference distribution representing a long-time behavior distribution, and the “inter-vehicle” time THW distribution is used as a comparison target distribution representing a short-time behavior distribution. Use. Then, by using the “normal” probability F _std on the high-risk side and the “in this case” probability F _comp , the deviation degree “Dist _diff ” in “at this time” with respect to “normal” is calculated from the above (formula 43). .

As described above, after calculating the deviation degree Dist _diff of “this time” with respect to “this day” in step S628, the process proceeds to step S630. In step S630, similar to the processing in step S628, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the distribution of the “normal” inter-vehicle time THW is used as a reference distribution, and the distribution of the inter-vehicle time THW of “this day” is used as a comparison target distribution.

In the subsequent step S632, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as the processing in step S628. Here, the “general” inter-vehicle time THW distribution is used as a reference distribution, and the “normal” inter-vehicle time THW distribution is used as a comparison target distribution. The follow-up characteristic value of “general public”, that is, the probability F _std on the higher risk side than the mode value is set in advance as a fixed value.

As described above, after the driver's driving diagnosis is performed using the data obtained at the plurality of different time spans in step S620, the process proceeds to step S640. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “normal” is Dist_1a, the deviation degree Dist _diff of “this time” for “this day” is Dist_1b, and “this day” for “normal” The departure degree Dist _diff is Dist_2, and the “ordinary” departure degree Dist _diff for “general public” is Dist_3.

In step S640, it is determined whether or not to execute the alarm presentation process based on the driving diagnosis result in step S620. Here, Dist_1a a deviance Dist _diff of "this moment" relative to "usual" calculated in step S628, the deviation degree Dist _diff of "this moment" relative to "this day" Dist_1b or "Normally" calculated in step S630, It is determined whether or not the deviation degree Dist_2 of “this day” is greater than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1a, Dist_1b, or Dist_2 is larger than a threshold value, it progresses to Step S650 and gives a warning to a driver. The contents of the alarm presentation are the same as in the fourth embodiment described above. After the alarm is presented, this process is terminated.

  When a negative determination is made in step S640 and no alarm presentation is performed, the process proceeds to step S660, and it is determined whether or not to perform the guidance presentation process based on the driving diagnosis result in step S620. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S632 is smaller than a threshold (for example, 0.07) for determining whether to provide guidance (improvement suggestion) or not. Determine whether. Here, the threshold value for determining whether or not to present the instruction is appropriately set in advance as a value such that the deviation degree is almost always within the range for the same driver.

  If Dist_3 is smaller than the threshold value, the process proceeds to step S670, and the driver is presented with guidance. The contents of the instruction presentation are the same as in the first embodiment described above. This process is complete | finished after giving instruction | indication presentation.

  Thus, in the sixth embodiment described above, the driver's safety consciousness / driving preference characteristics are detected as indicators for driving diagnosis. Specifically, the inter-vehicle time THW between the host vehicle and the preceding vehicle when the host vehicle travels following the preceding vehicle is calculated. The inter-vehicle time THW represents the time until the host vehicle reaches the current position of the preceding vehicle. Therefore, the inter-vehicle time THW is used as an index that represents the characteristics of the driver when the host vehicle follows the preceding vehicle. Accurate driving diagnosis can be performed in the driving scene.

<< Seventh Embodiment >>
The vehicle driving support apparatus according to the seventh embodiment of the present invention will be described below. FIG. 41 shows a system diagram of the vehicle driving support apparatus 6 according to the seventh embodiment. In FIG. 41, portions having the same functions as those of the first embodiment shown in FIG. Here, differences from the first embodiment will be mainly described.

  The vehicle driving support apparatus 6 according to the seventh embodiment includes a laser radar 10, a front camera 15, a vehicle speed sensor 30, an acceleration sensor 35, a navigation system 50, an accelerator pedal stroke sensor 55, a controller 400, a speaker 130, and a display unit. 180 and so on.

  The front camera 15 is a small CCD camera, a CMOS camera, or the like attached to the upper part of the front window, and detects the state of the road ahead as an image. The controller 400 performs image processing on the image signal from the front camera 15 and detects a lane marker or the like existing in the front area of the host vehicle. The detection area by the front camera 15 is about ± 30 deg in the horizontal direction with respect to the center line in the front-rear direction of the vehicle, and the front road scenery included in this area is captured as an image. The acceleration sensor 35 is a sensor that detects the longitudinal acceleration of the host vehicle, and outputs the detected longitudinal acceleration to the controller 400.

  In the seventh embodiment, the driving characteristics when the host vehicle starts are detected, and driving diagnosis is performed using the detected driving characteristics as an index.

  The operation of the vehicle driving support apparatus 6 according to the seventh embodiment will be described in detail with reference to FIG. FIG. 42 is a flowchart of the process procedure of the driving support control process in the controller 400 according to the seventh embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

First, in step S700, the traveling state of the host vehicle is detected. Here, the host vehicle speed V detected by the vehicle speed sensor 30, the presence / absence of a preceding vehicle detected by the laser radar 10, and the longitudinal acceleration xg detected by the acceleration sensor 35 are acquired as the traveling state of the host vehicle. Further, the G value is calculated by dividing the longitudinal acceleration xg by 9.8 m / s ² . Further, the jerk value is calculated by differentiating the longitudinal acceleration xg with respect to time. The longitudinal acceleration xg and the jerk value are main indexes representing the ride feeling and uncomfortable feeling when the vehicle starts.

  In step S705, the operation state of the driver is detected. Here, the accelerator pedal operation amount detected by the accelerator pedal stroke sensor 55 is acquired as the operation state of the driver. Further, the accelerator pedal operation speed is calculated by differentiating the accelerator pedal operation amount with respect to time.

  In step S706, the state of the road where the host vehicle exists is acquired. Specifically, the shape of the road where the host vehicle is present is acquired from the captured images of the navigation system 50 and the front camera 15.

  In step S710, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only in a driving scene where the vehicle starts from a stop with no obstacle ahead.

An example of the conditions of the driving scene of a single start is as follows.
(A) There is no preceding vehicle in the detection area of the laser radar 10 (b) The road on which the host vehicle is present is a substantially straight road (R ≧ 800 m) (c) Stopped state (own vehicle speed 0 km / h) Immediately after starting from (for example, within 10 seconds after starting)

  When all of the conditions (a) to (c) are satisfied, it is determined that the host vehicle is a traveling scene where the vehicle starts alone, and the process proceeds to step S712 to perform driving diagnosis. On the other hand, when any of the conditions (a) to (c) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing a driving diagnosis.

  In step S712, the travel location is determined. In step S714, the current time is recorded. In step S715, the maximum value of the starting acceleration characteristic index within 10 seconds after starting from the stop state is calculated. Examples of the index representing the acceleration characteristics at the time of starting include longitudinal acceleration xg, G value, jerk value, accelerator pedal operation amount, accelerator pedal operation speed, and the like. Here, a case where longitudinal acceleration xg is used will be described as an example. To do.

  In step S716, based on the labeling results in steps S712 and S714, data used for performing a driving diagnosis of the driver is stored. Here, for example, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the acceleration characteristic index at start of the link, the number of travels of the link, etc. are written in the structure format, and the travel road database is constructed To do.

  In subsequent step S720, the driving diagnosis of the driver is performed using the data stored in step S716. The driving diagnosis is performed based on the acceleration characteristics in the traveling scene of the single start. In the driving diagnosis process, each data of the A layer to D layer having the data structure shown in FIG. 4 is used to drive at different time spans, that is, “at this time”, “this day”, and “ordinary”. Determine. The driving diagnosis process executed in step S720 will be described in detail using the flowchart of FIG.

  In step S722, in order to perform the driving diagnosis of “at this time” of the driver, the acceleration characteristic value at the time of start of the driver at this time is calculated. The average value Mean_x (n) and standard deviation Stdev_x (n) of the longitudinal acceleration xg within a predetermined period defining “at this time” are calculated as the acceleration characteristic value at the time of start of the driver. Here, the predetermined period defining “at this time” is, for example, the last three start times, and using the data detected at the last three start times (data number K = 3), the average value of the longitudinal acceleration xg Calculate Mean_x (n) and standard deviation Stdev_x (n). The average value Mean_x (n) and the standard deviation Stdev_x (n) can be calculated using (Expression 5) and (Expression 7), respectively, as in the first embodiment.

  In step S724, in order to perform the driving diagnosis of “this day” of the driver, the acceleration characteristic value at the time of start of the driver of “this day”, that is, the longitudinal acceleration xg within a predetermined period defining “this day”. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined period defining “this day” is, for example, the latest 18 times, and using the data detected at the time of the latest 18 starts (data number K = 18), the average value Mean_x (n ) And standard deviation Stdev_x (n).

  In step S726, in order to perform the “normal” driving diagnosis of the driver, the acceleration characteristic value at the start of the “normal” driver, that is, the average value Mean_x of the longitudinal acceleration xg within a predetermined period defining “normal”. (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined period for defining “normal” is, for example, the latest 108 times, and using the data detected at the time of the 108 most recent start (data number K = 108), the average value Mean_x (n) of the longitudinal acceleration xg And the standard deviation Stdev_x (n) is calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”.

  In the subsequent processing from step S728, driving diagnosis of the driver is performed using the driving characteristic values calculated in steps S722, S724, and S726. Here, the driving characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two are different. That is, in the data structure shown in FIG. 4, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

First, in step S728, a deviation degree indicating how much the driving characteristics of the driver “at this time” deviate from the driving characteristics of the driver “this day” is calculated. Here, the deviation degree of “this time” with respect to “this day” indicates the difference between the distribution of acceleration xg of “this day” and the distribution of acceleration xg of “at this time”. In order to calculate the deviation degree of “this time” with respect to “this day”, the distribution of the acceleration xg of “this day” is used as the reference distribution representing the long-term behavior distribution, and the distribution of the acceleration xg of “at this time”. Is used as a comparison target distribution representing a short-time behavior distribution. Here, as in the first embodiment, the deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above.

In this way, after calculating the deviation degree Dist _diff for “this time” for “this day” and “this time” for “normal” in step S728, the process proceeds to step S730. In step S730, as in the process in step S728, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the “normal” acceleration xg distribution is used as a reference distribution representing a long-time behavior distribution, and the “this day” acceleration xg distribution is used as a comparison target distribution representing a short-time behavior distribution. .

In the subsequent step S732, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as the processing in step S728. Here, the “general public” acceleration xg distribution is used as a reference distribution representing a long-time behavior distribution, and the “normal” acceleration xg distribution is used as a comparison target distribution representing a short-time behavior distribution. . The acceleration characteristics value at the time of starting “general public”, that is, the average value and standard deviation of the acceleration xg at the time of independent starting are set in advance as fixed values.

In step S733, the departure acceleration characteristic of “this day” detected in the past is compared with the acceleration characteristic at the start of this “this day”, and the deviation degree “Dist _diff ” of “this time” relative to “past” is calculated. To do. Specifically, the distribution of the acceleration xg of “this day” detected in the previous cycle (for example, the previous day) is used as a reference distribution representing the long-time behavior distribution, and the distribution of the acceleration xg of “this day” detected this time is used. The deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above as a comparison target distribution representing a short-time behavior distribution.

As described above, after the driver's driving diagnosis is performed using the data obtained at the plurality of different time spans in step S720, the process proceeds to step S740. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “this day” is Dist_1, the deviation degree Dist _diff of “this day” for “normal” is Dist_2, and “normal” for “general” The departure degree Dist _diff is Dist_3, and the departure degree Dist _{diff of} “this day of this time” with respect to “this day of the past” is Dist_4.

  In step S740, it is determined whether or not to execute the alarm presentation process based on the driving diagnosis result in step S720. Here, the deviation degree Dist_1 of “this time” with respect to “this day” calculated in step S728, the deviation degree Dist_2 of “this day” with respect to “normal” calculated in step S730, or “the past this calculated in step S733” It is determined whether or not the deviation degree Dist_4 of “this day of this time” with respect to “day” is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1, Dist_2, or Dist_4 is larger than a threshold value, it progresses to step S750 and a warning is shown to a driver. After the alarm is presented, this process is terminated.

  FIG. 44 shows a display example when an alarm is presented by visual information. The display is such that the tendency of the acceleration at the start of the day (the acquired data result during the middle period) can be compared with the normal tendency of the acceleration at the start (the acquired data result for the long term). FIG. 44 shows that today's acceleration tends to be higher than usual.

  When an alarm is presented using auditory information, sound information corresponding to the degree of departure is output from the speaker 130. For example, when the deviation degree Dist_2 of “this day” with respect to “normal” is larger than the threshold value, a voice “starting acceleration is higher than usual” is output. When the deviation degree Dist_1 of “at this time” with respect to “this day” is larger than the threshold value, a voice “starting acceleration is high” is output. When the deviation degree Dist_4 of “this day of the present” with respect to “this day of the past” is larger than the threshold value, a voice “starting acceleration is higher than before” is output.

  When a negative determination is made in step S740 and no alarm presentation is performed, the process proceeds to step S760, and it is determined whether or not to perform the guidance presentation process based on the driving diagnosis result in step S720. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S732 is smaller than a threshold (for example, 0.07) for determining whether to provide guidance (improvement suggestion) or not. Determine whether.

  If Dist_3 is smaller than the threshold value, the process proceeds to step S770, and the driver is presented with guidance. This process is complete | finished after giving instruction | indication presentation. FIG. 45 shows a display example in the case of providing guidance with visual information. The display is such that the driver's normal start acceleration can be compared with the general start acceleration. In FIG. 45, it is shown that the acceleration at the time of start of the driver is lower than the general public. In addition, the “ordinary” deviation degree Dist_3 can be converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation.

  For example, when providing guidance based on auditory information, the driver tends to start at low vehicle speeds on a daily basis by outputting a voice saying "The result of analyzing your driving is lower than the general start acceleration." Outputs a sound that gives up the driver's driving. The driving diagnosis result can also be displayed using a two-dimensional map as shown in FIG.

As described above, in the seventh embodiment described above, in addition to the effects of the first to eighth embodiments described above, the following operational effects can be achieved.
The driver's safety awareness and driving preference characteristics are detected as indicators for driving diagnosis. Specifically, since the maximum value of the longitudinal acceleration xg at the start of the vehicle is used, there is no preceding vehicle, and an accurate driving diagnosis can be performed at the start of the vehicle.

<< Eighth Embodiment >>
The vehicle driving support apparatus according to the eighth embodiment of the present invention will be described below. The basic configuration of the vehicle driving support apparatus according to the eighth embodiment is the same as that of the first embodiment shown in FIG. Here, differences from the first embodiment will be mainly described. In the vehicle driving support device of the eighth embodiment, the accelerator pedal stroke sensor 55 can be omitted.

  In the eighth embodiment, the driving characteristic when the brake pedal is depressed from the state where the host vehicle is following the preceding vehicle is detected, and the driving diagnosis is performed using the detected driving characteristic as an index.

  The operation of the vehicle driving support apparatus 4 according to the eighth embodiment will be described in detail with reference to FIG. FIG. 46 is a flowchart of the process procedure of the driving support control process in the controller 300 according to the eighth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S800, the traveling state of the host vehicle is detected. Here, as the running state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10 are acquired. In step S802, the operation state of the driver is detected. Here, the brake pedal operation amount detected by the brake pedal stroke sensor 60 and the presence / absence of the winker lever operation detected by the winker switch 65 are acquired as the operation state of the driver. In step S804, the margin time TTC and the inter-vehicle time THW between the host vehicle and the preceding vehicle are calculated using the above-described (Formula 1) and (Formula 2) in order to determine the travel scene of the host vehicle described later.

  In step S806, it is determined whether or not the brake pedal is depressed. For example, when the brake pedal operation amount is larger than 0, it is determined that the brake pedal is depressed. If the brake pedal is depressed, the process proceeds to step S810. If the brake pedal is not depressed, this process is terminated. In the following description, the state where the brake pedal is depressed is referred to as a brake operation.

  In step S810, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only for a traveling scene in which the brake pedal is operated in a state where the host vehicle is stably following the same preceding vehicle.

An example of conditions for a stable follow-up driving scene is as follows.
(A) Following the same preceding vehicle (for example, the inter-vehicle distance has not changed by more than 4 m from the previous measured value)
(B) Not in a sudden approaching state (for example, margin time TTC exceeds 10 seconds)
(C) The inter-vehicle time THW is within a predetermined value (for example, the inter-vehicle time THW is less than 4 seconds)
(D) There is no blinker lever operation by the driver (for example, there is no ON signal input from the blinker switch 65).
(E) The states (a) to (d) above are continuing (for example, continuing for 5 seconds or more).

  When all of the conditions (a) to (e) are satisfied, it is determined that the traveling scene of the host vehicle is a stable follow-up traveling scene, and the process proceeds to step S812 to perform driving diagnosis. On the other hand, if any of the conditions (a) to (e) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing driving diagnosis. The conditions for determining whether or not a stable follow-up driving scene is not limited to the above (a) to (e), and the presence or absence of a brake operation or the presence or absence of a blinker lever operation is detected separately. It is also possible to detect by means.

  In step S812, the travel location is determined. In step S814, the current time is recorded. In step S816, based on the labeling results of steps S812 and S814, data used for performing a driving diagnosis of the driver is stored. Here, for example, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the driving characteristic index in the link, the number of travels of the link, and the like are written in the structure format, and the travel road database is constructed. In the eighth embodiment, the minimum value (minimum margin time) of the margin time TTC calculated during the brake operation is used as a physical quantity representing the driving characteristics of the driver.

  In subsequent step S820, the driver is diagnosed using the data stored in step S816. The driving diagnosis is performed based on the driving characteristics of the driver when the driver depresses the brake pedal in stable follow-up traveling. Driving characteristics at the time of brake operation include the inter-vehicle time THW, the reciprocal 1 / THW of the inter-vehicle time, the inter-vehicle distance D, the reciprocal of the inter-vehicle distance, etc. A case where the minimum margin time TTC is used will be described as an example.

  In the driving diagnosis process, each data of the A layer to D layer having the data structure shown in FIG. 4 is used to drive at different time spans, that is, “at this time”, “this day”, and “ordinary”. Determine. The driving diagnosis process executed in step S820 will be described in detail with reference to the flowchart of FIG.

  In step S822, in order to perform the driving diagnosis of “at this time” of the driver, the driving characteristic value of the driver at this time is calculated. As the driver's driving characteristic value, an average value Mean_x (n) and a standard deviation Stdev_x (n) of the minimum margin time TTC during the brake operation within a predetermined time defining “at this time” are calculated. Here, the predetermined time for defining “at this time” is, for example, 60 seconds, and using the data for 60 seconds from the past to the present detected during the brake operation in the stable follow-up traveling state determined in step S810, An average value Mean_x (n) and standard deviation Stdev_x (n) of the minimum margin time TTC are calculated. The average value Mean_x (n) and the standard deviation Stdev_x (n) can be calculated using (Expression 5) and (Expression 7), respectively, as in the first embodiment.

  In step S824, in order to perform the driving diagnosis of “this day” of the driver, the driving characteristic value of the driver of “this day”, that is, the minimum margin at the time of brake operation within a predetermined time defining “this day” An average value Mean_x (n) and a standard deviation Stdev_x (n) of the time TTC are calculated. Here, the predetermined time for defining “this day” is, for example, 360 seconds, and using the data for 360 seconds from the past to the present detected during the brake operation in the stable follow-up traveling state determined in step S810, An average value Mean_x (n) and standard deviation Stdev_x (n) of the minimum margin time TTC are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 1800 when the predetermined time of this day is 360 seconds and the sampling number is 5 Hz.

  In step S826, in order to perform the "normal" driving diagnosis of the driver, the "normal" driver's follow-up characteristic value, that is, the minimum margin time TTC during brake operation within a predetermined time defining "normal" is set. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined time for defining “usually” is, for example, 2160 seconds, and the minimum margin is obtained using data for 2160 seconds from the past to the present detected during the brake operation in the stable follow-up driving scene determined in step S810. An average value Mean_x (n) and a standard deviation Stdev_x (n) of the time TTC are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 10800 when the predetermined time of “normal” is 2160 seconds and the sampling number is 5 Hz.

  In the processing subsequent to step S828, the driver is diagnosed for driving using the driving characteristic values calculated in steps S822, S824, and S826. Here, the driving characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two are different. That is, in the data structure shown in FIG. 4, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

  First, in step S828, a deviation degree indicating how much the driving characteristics of the driver “at this time” deviate from the driving characteristics of the driver “this day” is calculated. Here, the degree of deviation of “this time” with respect to “this day” is the distribution of the minimum margin time TTC during the brake operation of “this day” and the distribution of the minimum margin time TTC during the brake operation of “this time”. It shows the difference. In order to calculate the degree of deviation of “this time” with respect to “this day”, the distribution of the minimum margin time TTC at the time of brake operation on “this day” is used as a reference distribution representing a long-time behavior distribution. The distribution of the minimum margin time TTC at the time of brake operation is used as a comparison target distribution representing a short-time action distribution.

Here, as in the first embodiment, the deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above. In addition, the deviation degree Dsit _diff of “at this time” with respect to “normal” is also calculated. In this case, the distribution of the minimum margin time TTC at the time of brake operation of “ordinary” is used as a reference distribution representing the long-term behavior distribution, and the distribution of the minimum margin time TTC at the time of brake operation of “At this time” is shortened. It is used as the distribution of the comparison target representing the behavior distribution. Then, by using the probability F _std (x) of the “normal” cumulative distribution and the probability F _comp (x) of the cumulative distribution “at this time”, the “normal” from the above-described (Expression 13) or (Expression 16). The deviation degree “Dist _diff ” of “at this time” is calculated.

In this way, after calculating the deviation degrees Dist _diff for “this time” for “this day” and “this time” for “normal” in step S828, the process proceeds to step S830. In step S830, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated in the same manner as the processing in step S828. Here, the distribution of the minimum margin time TTC at the time of the “normal” brake operation is used as a reference distribution representing the long-time behavior distribution, and the distribution of the minimum margin time TTC at the time of the brake operation of “this day” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time.

In subsequent step S832, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as in the process in step S828. Here, the distribution of the minimum margin time TTC at the time of brake operation of “general public” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the minimum margin time TTC at the time of brake operation of “ordinary” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time. The driving characteristic value of “general public”, that is, the average value and the standard deviation of the minimum margin time TTC at the time of the brake operation are set in advance as fixed values.

As described above, after the driver's driving diagnosis is performed using the data obtained at the plurality of different time spans in step S820, the process proceeds to step S840. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “normal” is Dist_1a, the deviation degree Dist _diff of “this time” for “this day” is Dist_1b, and “this day” for “normal” The departure degree Dist _diff is Dist_2, and the “ordinary” departure degree Dist _diff for “general public” is Dist_3.

  In step S840, it is determined whether or not to perform the alarm presentation process based on the driving diagnosis result in step S820. Here, the deviation degree Dist_1a of “at this time” with respect to “normal” calculated at step S828, the deviation degree Dist_1b of “at this time” with respect to “this day”, or “this day” with respect to “normal” calculated at step S830. It is determined whether or not the deviation degree Dist_2 is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1 or Dist_2 is larger than a threshold value, it progresses to step S850 and a warning is shown to a driver. After the alarm is presented, this process is terminated.

  For example, when the deviation level Dist_2 of “this day” with respect to “normal” is larger than the threshold, the speaker 130 outputs a sound “the brake operation tends to be slow” together with a buzzer sound. The voice information is set so as to notify that the driver's brake pedal operation tends to be slow or to prompt an early brake operation. The specific audio information is not limited to this. Even when the deviation degree Dist_1a of “at this time” with respect to “normal” and the deviation degree Dist_1b of “at this time” with respect to “this day” are larger than the threshold value, appropriate audio information set in advance is output.

  When a negative determination is made in step S840 and no alarm presentation is performed, the process proceeds to step S860, and it is determined whether or not the guidance presentation process is to be executed based on the driving diagnosis result in step S820. Here, whether or not the “ordinary” departure degree Dist_3 for “general public” calculated in step S832 is smaller than a threshold value (for example, 0.07) for determining whether to provide guidance (improvement suggestion) or not. Determine whether.

  When Dist_3 is smaller than the threshold value, the process proceeds to step S870, and the driver is presented with guidance. This process is complete | finished after giving instruction | indication presentation. For example, the display of the content which abandons a driver | operator and the audio | voice are output as guidance presentation content. For example, the “ordinary” deviation degree Dist_3 is converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation. That is, the score of the driver who tends to be slow in the brake operation is 50 points or less, and the score of an excellent driver who performs the early and stable brake operation is 50 points or more. The score is displayed within a range of 0 to 100. When the score obtained by converting the deviation degree Dist_3 is 100 or more, it is 100 points, and when it is 0 or less, 0 points.

  Improvement suggestions can also be made by voice. In this way, it is possible to inform the driver that the driver's individual following driving characteristics are excellent for the general public driver, and to display or encourage the driver to maintain good driving or improve it. Output audio. The driving diagnosis result can also be displayed using a two-dimensional map as shown in FIG.

As described above, in the eighth embodiment described above, in addition to the effects of the first to seventh embodiments described above, the following operational effects can be achieved.
The risk tolerance of the driver is detected as an index for driving diagnosis. Specifically, the minimum value of the margin time TTC between the host vehicle and the preceding vehicle when the brake operation is performed is used. The allowance time TTC represents the time until the host vehicle and the preceding vehicle come into contact when the host vehicle speed V and the relative vehicle speed Vr are constant. When the brake operation is performed from the state in which the host vehicle is following the preceding vehicle, it is used as an index that represents the characteristics of the driver by grasping how much the margin time TTC is reduced. Accurate driving diagnosis can be performed when a brake operation is performed in a scene.

<< Ninth embodiment >>
The vehicle driving support apparatus according to the ninth embodiment of the present invention will be described below. The basic configuration of the vehicle driving support apparatus according to the ninth embodiment is the same as that of the first embodiment shown in FIG. Here, differences from the first embodiment will be mainly described. In the vehicle driving support device of the ninth embodiment, the accelerator pedal stroke sensor 55 can be omitted.

  In the ninth embodiment, the driving characteristic when the host vehicle overtakes the preceding vehicle is detected, and the driving diagnosis is performed using the detected driving characteristic as an index.

  The operation of the vehicle driving support apparatus 4 according to the ninth embodiment will be described in detail with reference to FIG. FIG. 48 is a flowchart of the process procedure of the driving support control process in the controller 300 according to the ninth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S900, the traveling state of the host vehicle is detected. Here, as the running state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10 are acquired. Further, information on the road on which the host vehicle travels is acquired from the navigation system 50. In step S902, the operation state of the driver is detected. Here, the brake pedal operation amount detected by the brake pedal stroke sensor 60 and the presence / absence of the winker lever operation detected by the winker switch 65 are acquired as the operation state of the driver. In step S904, the margin time TTC and the inter-vehicle time THW between the host vehicle and the preceding vehicle are calculated using the above-described (Formula 1) and (Formula 2) in order to determine the travel scene of the host vehicle described later.

  In step S906, it is determined whether the own vehicle is performing an overtaking operation. Specifically, it is determined whether or not the host vehicle is overtaking a front obstacle while accelerating by operating the blinker lever while traveling on a road of two or more lanes on one side. For example, when it is detected from the road information acquired from the navigation system 50 that the vehicle is traveling on two or more lanes on one side and the winker lever is operated, it is determined that the vehicle is overtaking. If the overtaking operation is in progress, the process proceeds to step S910. If the overtaking operation is not in progress, the process ends.

  In step S910, the traveling scene of the host vehicle is determined. In order to improve the accuracy of driving diagnosis by limiting conditions such as the vehicle running state and the driver's operation state, and to reduce the uncomfortable feeling given to the driver when providing information according to the driving diagnosis result, A driving scene is determined, and driving diagnosis is performed only when the driving scene is a specific driving scene. Specifically, the driving diagnosis is performed only in a traveling scene in which the own vehicle is stably following the same preceding vehicle and the passing operation is performed.

An example of conditions for a stable follow-up driving scene is as follows.
(A) Following the same preceding vehicle (for example, the inter-vehicle distance has not changed by more than 4 m from the previous measured value)
(B) Not in a sudden approaching state (for example, margin time TTC exceeds 10 seconds)
(C) The inter-vehicle time THW is within a predetermined value (for example, the inter-vehicle time THW is less than 4 seconds)
(D) No brake pedal operation by the driver (for example, the brake pedal operation amount is 0)
(E) The states (a) to (d) above are continuing (for example, continuing for 5 seconds or more).

  If all of the conditions (a) to (e) are satisfied, it is determined that the traveling scene of the host vehicle is a stable follow-up traveling scene, and the process proceeds to step S912 to perform driving diagnosis. On the other hand, if any of the conditions (a) to (e) is not satisfied, it is determined that the vehicle does not correspond to a specific traveling scene, and this process is terminated without performing driving diagnosis. The conditions for determining whether or not a stable follow-up driving scene is not limited to the above (a) to (e), and the presence or absence of a brake operation or the presence or absence of a blinker lever operation is detected separately. It is also possible to detect by means.

  In step S912, the travel location is determined. In step S914, the current time is recorded. In step S916, based on the labeling results of steps S912 and S914, data used for performing a driving diagnosis of the driver is stored. Here, for example, the current time for each link ID, that is, the time when the link was traveled, the travel distance, the driving characteristic index in the link, the number of travels of the link, and the like are written in the structure format, and the travel road database is constructed. In the ninth embodiment, the minimum value (minimum inter-vehicle distance) of the inter-vehicle distance D detected during the overtaking operation is used as a physical quantity representing the driving characteristics of the driver.

  In subsequent step S920, the driving diagnosis of the driver is performed using the data stored in step S916. The driving diagnosis is performed based on the driving characteristics of the driver when the driver overtakes the preceding vehicle in stable follow-up traveling. The driving characteristics at the time of overtaking include the inter-vehicle time THW between the host vehicle and the preceding vehicle, the reciprocal 1 / THW of the inter-vehicle time, the margin time TTC, the reciprocal of the inter-vehicle distance, etc. A case where the inter-vehicle distance D is used will be described as an example. Specifically, the inter-vehicle distance between the preceding vehicle and the host vehicle existing in the traveling lane before the host vehicle changes the lane is used. It is also possible to use the inter-vehicle distance between the vehicle traveling in front of the host vehicle and the host vehicle in the travel lane after the lane change.

  In the driving diagnosis process, each data of the A layer to D layer having the data structure shown in FIG. 4 is used to drive at different time spans, that is, “at this time”, “this day”, and “ordinary”. Determine. The driving diagnosis process executed in step S920 will be described in detail using the flowchart of FIG.

  In step S922, in order to perform the driving diagnosis of “at this time” of the driver, the driving characteristic value of the driver at this time is calculated. As a driver's driving characteristic value, an average value Mean_x (n) and a standard deviation Stdev_x (n) of the minimum inter-vehicle distance D at the time of overtaking within a predetermined time defining “at this time” are calculated. Here, the predetermined time for defining “at this time” is, for example, 60 seconds, and using the data for 60 seconds from the past to the present detected during overtaking in the stable following traveling state determined in step S910, An average value Mean_x (n) and a standard deviation Stdev_x (n) of the minimum inter-vehicle distance D are calculated. The average value Mean_x (n) and the standard deviation Stdev_x (n) can be calculated using (Expression 5) and (Expression 7), respectively, as in the first embodiment.

  In step S924, in order to perform the driving diagnosis of “this day” of the driver, the driving characteristic value of the driver of “this day”, that is, the minimum inter-vehicle distance at the time of overtaking within a predetermined time defining “this day” The average value Mean_x (n) and standard deviation Stdev_x (n) of the distance D are calculated. Here, the predetermined time for defining “this day” is, for example, 360 seconds, and using the data for 360 seconds from the past to the present detected at the time of overtaking in the stable following traveling state determined in step S910, An average value Mean_x (n) and a standard deviation Stdev_x (n) of the minimum inter-vehicle distance D are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 1800 when the predetermined time of this day is 360 seconds and the sampling number is 5 Hz.

  In step S926, in order to perform the "normal" driving diagnosis of the driver, the "following" driver's follow-up characteristic value, that is, the minimum inter-vehicle distance D during overtaking within a predetermined time defining "normal" is determined. Average value Mean_x (n) and standard deviation Stdev_x (n) are calculated. Here, the predetermined time for defining “ordinary” is, for example, 2160 seconds, and the minimum distance between vehicles is determined using data for 2160 seconds from the past to the present detected in the stable follow-up driving scene determined in step S910. The average value Mean_x (n) and standard deviation Stdev_x (n) of the distance D are calculated.

  Specifically, the average value Mean_x (n) and the standard deviation Stdev_x (n) are calculated using the above (Formula 5) and (Formula 7) as in “At this time”. Here, the data number K is 10800 when the predetermined time of “normal” is 2160 seconds and the sampling number is 5 Hz.

  In the subsequent processing from step S928, the driving diagnosis of the driver is performed using the driving characteristic values calculated in steps S922, S924, and S926. Here, the driving characteristics of the driver based on the data obtained in different time spans are compared, and the driving of the driver is diagnosed based on how much the two are different. That is, in the data structure shown in FIG. 4, driving diagnosis is performed by comparing an upper layer (for example, A layer) with a lower layer (for example, B layer).

  First, in step S928, the degree of deviation indicating how much the driving characteristics of the driver “at this time” deviate from the driving characteristics of the driver “this day” is calculated. Here, the deviation degree of “at this time” with respect to “this day” is the distribution of the minimum inter-vehicle distance D at the time of overtaking of “this day” and the distribution of the minimum inter-vehicle distance D at the time of overtaking of “at this time”. It shows the difference. In order to calculate the deviation degree of “this time” with respect to “this day”, the distribution of the minimum inter-vehicle distance D at the time of overtaking of “this day” is used as a reference distribution representing a long-time behavior distribution. The distribution of the minimum inter-vehicle distance D at the time of overtaking is used as a comparison target distribution representing a short-time behavior distribution.

Here, as in the first embodiment, the deviation degree Dist _diff is calculated using (Equation 13) or (Equation 16) described above. In addition, the deviation degree Dsit _diff of “at this time” with respect to “normal” is also calculated. In this case, the distribution of the minimum inter-vehicle distance D at the time of overtaking in “ordinary” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the minimum inter-vehicle distance D at the time of overtaking in “At this time” is used for a short time. It is used as the distribution of the comparison target representing the behavior distribution. Then, by using the probability F _std (x) of the “normal” cumulative distribution and the probability F _comp (x) of the cumulative distribution “at this time”, the “normal” from the above-described (Expression 13) or (Expression 16). The deviation degree “Dist _diff ” of “at this time” is calculated.

In this way, after calculating the deviation degrees Dist _diff for “this time” for “this day” and “this time” for “normal” in step S928, the process proceeds to step S930. In step S930, similar to the processing in step S928, the deviation degree Dist _diff of “this day” with respect to “normal” is calculated. Here, the distribution of the minimum inter-vehicle distance D at the time of overtaking in “ordinary” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the minimum inter-vehicle distance D at the time of overtaking of “this day” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time.

In the subsequent step S932, the “ordinary” departure degree Dist _diff for “general public” is calculated in the same manner as the processing in step S928. Here, the distribution of the minimum inter-vehicle distance D at the time of overtaking in “general public” is used as a reference distribution representing the long-time behavior distribution, and the distribution of the minimum inter-vehicle distance D at the time of overtaking in “normal” is shortened. It is used as a comparison target distribution representing the behavioral distribution of time. The driving characteristic value of “general public”, that is, the average value and the standard deviation of the minimum inter-vehicle distance D during overtaking are set in advance as fixed values.

As described above, after the driver's driving diagnosis is performed using the data obtained in the plurality of different time spans in step S920, the process proceeds to step S940. In the following, for convenience of explanation, the deviation degree Dist _diff of “this time” for “normal” is Dist_1a, the deviation degree Dist _diff of “this time” for “this day” is Dist_1b, and “this day” for “normal” The departure degree Dist _diff is Dist_2, and the “ordinary” departure degree Dist _diff for “general public” is Dist_3.

  In step S940, it is determined whether or not to execute the alarm presentation process based on the driving diagnosis result in step S920. Here, the deviation degree Dist_1a of “at this time” with respect to “normal” calculated in step S928, the deviation degree Dist_1b of “at this time” with respect to “this day”, or “this day” with respect to “normal” calculated in step S930. It is determined whether or not the deviation degree Dist_2 is larger than a threshold (for example, 0.30) for determining whether or not to present an alarm. When deviation degree Dist_1a, Dist_1b, or Dist_2 is larger than a threshold value, it progresses to step S950 and a warning is shown to a driver. After the alarm is presented, this process is terminated.

  For example, when the deviation degree Dist_2 of “this day” with respect to “normal” is larger than the threshold value, the speaker 130 outputs a sound “there is a tendency to get too close to the vehicle ahead” along with a buzzer sound. The audio information informs that there is a tendency to approach too much when overtaking the vehicle ahead, and sets the content that prompts the user to take a longer inter-vehicle distance during overtaking. The specific audio information is not limited to this. Even when the deviation degree Dist_1a of “at this time” with respect to “normal” and the deviation degree Dist_1b of “at this time” with respect to “this day” are larger than the threshold value, appropriate audio information set in advance is output.

  When a negative determination is made in step S940 and no alarm presentation is performed, the process proceeds to step S960, and it is determined whether to perform the guidance presentation process based on the driving diagnosis result of step S920. Here, whether or not the “ordinary” deviation degree Dist_3 for “general public” calculated in step S932 is smaller than a threshold (for example, 0.07) for determining whether to provide guidance (improvement suggestion) or not. Determine whether.

  When Dist_3 is smaller than the threshold value, the process proceeds to step S970, and the guidance is presented to the driver. This process is complete | finished after giving instruction | indication presentation. For example, the display of the content which abandons a driver | operator and the audio | voice are output as guidance presentation content. For example, the “ordinary” deviation degree Dist_3 is converted into a score and displayed. Specifically, after inverting the sign of the deviation degree Dist_3, a value obtained by adding 50 is displayed on the display unit 180 as the score of the driver's normal driving operation. That is, the number of drivers who tend to be too close to the vehicle ahead during overtaking is 50 points or less, and the number of excellent drivers who pass with a margin is 50 points or more. The score is displayed within a range of 0 to 100. When the score obtained by converting the deviation degree Dist_3 is 100 or more, it is 100 points, and when it is 0 or less, 0 points.

  Improvement suggestions can also be made by voice. In this way, it is possible to inform the driver that the driver's individual following driving characteristics are excellent for the general public driver, and to display or encourage the driver to maintain good driving or improve it. Output audio. The driving diagnosis result can also be displayed using a two-dimensional map as shown in FIG.

In the ninth embodiment described above, the following operational effects can be obtained in addition to the effects of the first to eighth embodiments described above.
The risk tolerance of the driver is detected as an index for driving diagnosis. Specifically, the minimum value of the inter-vehicle distance D between the own vehicle being overtaken and the preceding vehicle is used. When the driver deliberately overtakes the preceding vehicle by operating the blinker, the degree of proximity to the preceding vehicle is used as an indicator to express the driver's characteristics, so that the driver can drive accurately in the overtaking scene. Diagnosis can be made.

<< Tenth Embodiment >>
The vehicle driving support apparatus according to the tenth embodiment of the present invention will be described below. FIG. 50 is a system diagram showing the configuration of the vehicle driving support apparatus 2 according to the tenth embodiment.

First, the configuration of the vehicle driving support device 2 will be described.
The rudder angle sensor 5 is an angle sensor attached, for example, in the vicinity of a steering column or a steering wheel (not shown), and detects a steering angle due to the turning of the driver from the rotation of the steering shaft. The detected steering angle is output to the controller 200.

The front camera 15 is a small CCD camera, a CMOS camera, or the like attached to the upper part of the front window, and detects the state of the road ahead as an image. The controller 200 performs image processing on the image signal from the front camera 15 to detect a lane marker or the like existing in the front area of the host vehicle. The detection area by the front camera 15 is about ± 30 deg in the horizontal direction with respect to the center line in the front-rear direction of the vehicle.
The vehicle speed sensor 30 detects the vehicle speed of the host vehicle by measuring the number of wheel rotations and the number of rotations on the output side of the transmission, and outputs the detected host vehicle speed to the controller 200.

  The navigation system 50 includes a GPS receiver, a map database, a display monitor, and the like, and is a system that performs route search, route guidance, and the like. The navigation system 50 can acquire information such as the type of road on which the host vehicle is running and the road width based on the current position of the host vehicle obtained from the GPS receiver and the road information stored in the map database.

  The controller 200 is an electronic control unit including a CPU and CPU peripheral components such as a ROM and a RAM, and controls the entire vehicle driving support apparatus 2. The controller 200 analyzes the driving characteristics of the driver based on signals input from the steering angle sensor 5, the front camera 15, the vehicle speed sensor 30, the navigation system 50, etc., and performs driving diagnosis. Then, information is provided to the driver based on the driving diagnosis result. Information provided to the driver includes warnings to the driver and suggestions for improving driving operations. Specific control contents in the controller 200 will be described later.

  The speaker 130 provides information to the driver by a buzzer sound or voice according to a signal from the controller 200. The display unit 180 performs a display that gives a warning or an improvement suggestion to the driver according to a signal from the controller 200. The display unit 180 can use, for example, a display monitor or a combiometer of the navigation system 50.

Next, the operation of the vehicle driving support apparatus 2 according to the tenth embodiment will be described. First, the outline will be described.
The controller 200 of the vehicle driving support apparatus 2 performs a driving diagnosis of the driver based on the traveling state of the host vehicle and the driving operation of the driver, and an alarm to the driver and improvement of the driving operation according to the driving diagnosis result. Make suggestions. Specifically, an unstable state of the driving operation is detected using the steering angle signal, and the driving diagnosis of the driver is performed. In order to detect an unstable state of the driving operation, the steering entropy method is used in the fourth embodiment. This is a method of calculating the degree of instability of the driving operation of the driver from the smoothness of the steering operation (steering angle).

  And if the driver is driving at a higher risk than normal driving from the results of the driving diagnosis, that is, if the driver's driving deviates in the high risk direction, an alarm is given and a high risk is Notify the driver before falling into a bad state. On the other hand, if the driver's driving is better than the general standard from the driving diagnosis result, the information is presented, that is, the improvement is suggested, so that the driver's driving is given up so as to further improve the safety driving awareness.

  As described above, the vehicle driving support apparatus 2 according to the tenth embodiment has a function of detecting the driving of the driver by driving diagnosis, a function of giving an alarm to the driver according to the detection result, and a response of the detection result. The driver has three functions to give improvement suggestions to the driver. The driver knows his / her driving characteristics objectively and encourages the driver to introspect and presents advice according to the driving characteristics to the driver. Can learn how to drive lower risk.

Here, the steering entropy method will be described.
In general, in a state where the driver's attention is not concentrated on driving, the time during which steering is not performed becomes longer than in normal driving where the driving is concentrated, and a large steering angle error is accumulated. Therefore, the corrected steering amount when the driver's attention returns to driving increases. The steering entropy method focuses on this characteristic, and uses an α value as a characteristic value and a steering angle entropy Hp calculated on the basis of the α value. By comparing the reference steering angle entropy with the steering angle entropy calculated based on the measured steering angle, an unstable state of the driving operation with respect to the reference is detected.

  The α value is a steering error within a fixed time based on the time series data of the steering angle, that is, the difference between the estimated value of the steering angle when the steering is operated smoothly and the actual steering angle, A steering error distribution (variation) is measured to calculate a 90 percent tile value (a distribution range including 90% of the steering error).

  The steering entropy value, that is, the Hp value represents the ambiguity (uncertainty) of the steering error distribution. Similar to the α value, the Hp value decreases when the steering operation is smooth and stable, and increases when it is unstable and unstable. The Hp value is corrected by the α value, and can be used as a driver instability that is not affected by the skill or habit of the driver.

  In the tenth embodiment, the steering entropy calculated using the steering error distribution of the general driver group is set as the reference state. Then, the steering angle entropy is calculated using the measured steering error distribution of the driver, and the driving diagnosis is performed by detecting the unstable state of the driving operation that the driver basically has.

  Since the steering error is affected by the road alignment and the load state of the driver, it is necessary to measure a reference steering error distribution when the driver is in an unloaded state. Therefore, in the tenth embodiment, in order to be less susceptible to these effects, the steering angle data at the time of road linear traveling that can be regarded as a straight road is used, and the steering error distribution is based on the steering angle data measured for a long time. Ask for.

  The operation of the vehicle driving support apparatus 2 according to the tenth embodiment will be described in detail with reference to FIG. FIG. 51 is a flowchart of the process procedure of the driving support control process in the controller 200 according to the tenth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  In step S1011, the steering angle signal θ detected by the steering angle sensor 5 is read. In step S1013, the road curvature signal ρ of the road on which the host vehicle runs is read. Here, the road curvature signal ρ of the traveling road of the host vehicle can be acquired by using road curvature data and road linear information included in the road map database of the navigation system 50, for example. Alternatively, image processing can be performed on the front image of the host vehicle photographed by the front camera 15 to detect a lane marker, and the road curvature can be calculated from the detected lane marker. In step S1015, the vehicle speed signal V of the host vehicle detected by the vehicle speed sensor 30 is read.

  In subsequent steps S1017 and S1019, it is determined whether or not to calculate the steering angle entropy. Specifically, in step S1017, it is determined whether or not the road curvature signal ρ read in step S1013 is smaller than a road curvature predetermined value ρo for determining that the vehicle is traveling on a straight road. If the road curvature signal ρ is less than or equal to the predetermined value ρo, it is determined that the host vehicle is traveling on a straight road, and the process proceeds to step S1019. If ρ> ρo, this process ends.

  In step S1019, it is determined whether the vehicle speed signal V read in step S1015 is greater than a predetermined value Vo. The predetermined value Vo is a threshold value for determining whether or not the vehicle is traveling at a stable vehicle speed on a road other than a city road with many obstacles, and is set to, for example, Vo = 60 km / h. . If the vehicle speed signal V is greater than the predetermined value Vo, the process proceeds to step S1011. If the vehicle speed signal V is equal to or less than the predetermined Vo, this process ends.

  In step S1021, whether the sample number n of the steering angle signal θ measured under the condition that the road curvature signal ρ is equal to or less than the predetermined value ρo and the host vehicle speed V is greater than the predetermined value Vo is greater than the predetermined value N1. Determine whether. Here, the predetermined value N1 is a threshold value for determining whether data for a long period of time sufficient to make it less susceptible to the influence of the driver's load is obtained. For example, N1 = 100000. . Note that 100,000 pieces of data are expected to be obtained in a traveling period of about three weeks. If the number of samples n is greater than the predetermined value N1, the process proceeds to step S1023. If the number of samples is less than the predetermined value N1, this process is terminated.

  In step S1023, the steering angle entropy Hp1 (hereinafter referred to as long-term steering angle entropy) is calculated using the steering angle signal θ measured over a long period. The long-term steering angle entropy Hp1 is a value that represents an unstable state of the driving operation that the driver basically has, and can be said to be a normal degree of instability of the driver.

Here, FIG. 52 shows special symbols and their names used for calculating the steering angle entropy. The steering angle smooth value θn-tilde is a steering angle in which the influence of quantization noise is reduced. The estimated value θn-hat of the steering angle is a value obtained by estimating the steering angle at the sampling time on the assumption that the steering is operated smoothly. The estimated steering angle θn-hat is obtained by performing a second-order Taylor expansion on the steering angle smooth value θn-tilde as shown in the following (formula 44).

In (Equation 44), tn is the sampling time of the steering angle θn.

The steering angle smooth value θn-tilde is calculated from the following (Equation 45) as an average value of the three adjacent steering angles θn in order to reduce the influence of quantization noise.

In (Equation 45), l is the steering angle included in 150 msec when the calculation time interval of the steering angle smooth value θn-tilde is 150 msec, that is, the minimum time interval that can be intermittently operated by humans in manual operation. This represents the number of samples of θn.

When the sampling interval of the steering angle θn is Ts, the number of samples 1 is expressed by the following (Equation 46).
l = round (0.15 / Ts) (Formula 46)
In (Equation 45), k = 1, 2, and 3 are taken, and the smooth value θn-tilde is calculated based on the steering angle at intervals of 150 msec and a total of three steering angles θn adjacent thereto according to (k * 1). Can be sought. Therefore, the estimated value θn-hat calculated based on the smooth value θn-tilde is calculated based on the steering angle θ substantially obtained at intervals of 150 msec.

The steering error en at the time of sampling can be calculated from the following (formula 47) as the difference between the estimated steering angle θn-hat and the actual steering angle θn when it is assumed that the steering operation is performed smoothly.

However, the steering error en is calculated only for the minimum time interval at which a human can operate intermittently and the steering angle θn every 150 msec.

With reference to these terms, the long-term steering angle entropy Hp1 calculation process will be described with reference to the flowchart of FIG.
In step S1031, time series data of n steering angle signals θn measured at the sampling interval Ts under the above-described road curvature and vehicle speed conditions are collected. The sampling interval Ts is, for example, 50 msec.

In step S1032, three steering angle smooth values θn-tilde are calculated from (Equation 45) using three adjacent steering angles θn at intervals of 150 msec. The three steering angle smooth values θn-tilde are expressed by the following (formula 48).

In step S1033, using the three steering angle smooth values θn-tilde calculated in step 1032, the estimated steering angle value θn-hat is calculated from the above (Equation 44). The estimated value θn-hat is expressed by the following (formula 49).

  In step S1034, the steering error en is calculated from the above (Equation 47) using the steering angle estimated value θn-hat calculated in step S1033 and the actual steering angle signal θn.

  In step S1035, the steering error en calculated in step S1034 is classified into nine sections b1 to b9 based on the α value (= αo) in the reference state, as shown in FIG. 54, and the steering error en included in each section bi. The probability Pi for all frequencies is obtained. Here, the α value (= αo) in the reference state is set in advance based on the steering angle signal of the general driver group (general drivers), and is stored in the memory of the controller 200. Then, the category bi is set when the program shown in FIG. 51 is executed.

In the subsequent step S1036, the long-term steering angle entropy Hp1 is calculated from the following (Equation 50) using the probability Pi calculated in step S1035.

Note that “p” in the long-term steering angle entropy Hp1 indicates that the steering angle entropy follows the probability distribution P = {Pi}.

  The long-term steering angle entropy Hp1 value represents the steepness of the distribution of the steering error en, and when the steering error en is equally included in each section bi, the logarithm of the bottom is 9 so that the Hp1 value is 1. Calculate by In addition, since the sections are set so that 90% of the total frequencies are included in the three sections b4 to b6 in the center of the distribution of the steering error en, the Hp1 value does not become 1 in the reference state.

  The steeper degree of the distribution of the steering error en becomes larger as the steering angle entropy Hp1 becomes longer, and the distribution of the steering error en falls within a certain range. That is, the steering operation is performed smoothly and the driving is in a stable state. On the contrary, the steeper degree of the distribution of the steering error en becomes smaller and the distribution of the steering error en varies as the long-term steering angle entropy Hp1 value increases. That is, the steering operation is jerky and the driving is unstable.

  Thus, after calculating the long-term steering angle entropy Hp1 in step S1023, the process proceeds to step S1025. In step S1025, a warning or guidance (improvement suggestion) is presented to the driver according to the long-term steering angle entropy Hp1. It can be considered that the greater the long-term steering angle entropy Hp1, the greater the instability of the driving operation compared to the average steering angle entropy of the general driver group (general public).

  Therefore, for example, as shown in FIG. 55, the long-term steering angle entropy Hp1 is classified into five levels (SSS, SS, SM, SL, SLL), and the calculated long-term steering angle entropy Hp1 level. To the driver. In the classification shown in FIG. 55, five levels are appropriately set so that the average steering angle entropy of the general driver group (general public) falls within the central level SM.

  FIG. 56 shows an example of notification contents according to the classification result of the long-term steering angle entropy Hp1. When the classification result of the long-term steering angle entropy Hp1 is the level SLL, the text indicating that “the driving is unstable is a type” is displayed on the display monitor of the display unit 180. When the classification result is level SL, the text of the content “the degree of driving instability is a slightly large type” is displayed. When the classification result is level SM and is equivalent to the average steering angle entropy of the general driver group, a text indicating that the degree of driving instability is normal is displayed. When the classification result is level SS, the text of the content “the degree of driving instability is slightly small” is displayed. When the classification result is level SSS, the text of the content “the degree of driving instability is small” is displayed. Note that the notification content shown in FIG. 56 can also be provided to the driver by voice output from the speaker 130.

Thus, in the tenth embodiment described above, the following operational effects can be achieved in addition to the effects of the first to ninth embodiments described above.
As an index for driving diagnosis, a control result of driving operation by the driver is detected. Specifically, the steering angle entropy Hp representing the degree of instability of the steering operation by the driver is calculated using the steering angle. Thereby, a driving diagnosis can be performed with high accuracy with respect to the driving operation in the left-right direction of the host vehicle.

-Modification 1 of the tenth embodiment-
Here, the long-term steering angle entropy Hp1 calculated using the driver's steering angle error distribution measured over a long period is set as the reference state. Then, the steering angle entropy is calculated using the driver's steering error distribution measured in the middle period, and the driving diagnosis is performed by detecting the deviation from the unstable driving operation that the driver basically has. Do.

  The operation of the vehicle driving support apparatus 2 according to the first modification of the tenth embodiment will be described in detail with reference to FIGS. 57 and 58 are flowcharts of the processing procedure of the driving support control process in the controller 200 of the first modification of the tenth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec. The processing in steps S1041 to S1053 is the same as the processing in steps S1011 to S1023 in the flowchart shown in FIG. However, if it is determined in step S1051 that the number of samples n is equal to or less than the predetermined value N1, the process proceeds to step S1057 in FIG. 58 without terminating the process.

  After calculating the long-term steering angle entropy Hp1 representing the reference state in step S1053, in step S1055, an α value used for calculating the steering angle entropy in the middle period is calculated. Here, the α value (hereinafter referred to as α1 value) of the driver is calculated using the steering angle signal θ measured over a long period of time. This process will be described with reference to the flowchart of FIG.

  In step S1081, time series data of n steering angle signals θn measured at the sampling interval Ts under the above-described road curvature and vehicle speed conditions are collected. The sampling interval Ts is, for example, 50 msec. In step S1082, three steering angle smooth values θn-tilde are calculated from (Equation 45) using three adjacent steering angles θn at intervals of 150 msec.

  In step S1083, using the three steering angle smooth values θn-tilde calculated in step 1082, the estimated value θn-hat of the steering angle is calculated from the above (Equation 44). In step S1084, the steering error en is calculated from the above (formula 47) using the estimated steering angle value θn-hat calculated in step S1083 and the actual steering angle signal θn.

  In the subsequent step S1085, the frequency of the steering error en is counted for each predetermined steering error. Here, the predetermined steering error is determined in consideration of the resolution of the steering angle sensor 5. Here, for example, as shown in FIG. 60, the steering error en is classified every 0.001 rad. FIG. 61 shows an example of the frequency distribution of the steering error en. In step S1086, 0 is set to i indicating the steering error category.

  In step S1087, it is determined whether the probability P of the frequency T0.000 at the steering error en = 0.000 rad is 90% or more with respect to all the frequencies of all the steering errors. When the probability P of the frequency T0.000 is 90% or more, the process proceeds to step S1090. In this case, since i = 0, the α1 value is α1 = 0.000 rad. If a negative determination is made in step S1087, the process proceeds to step S1088. In step S1088, i is incremented by 1 and set to (i + 1).

  In step S1089, the steering error classification is expanded, and the probability P of the steering error en frequency (T0.000 + T0.001 + T−0.001) from −0.001 rad to +0.001 rad with respect to the total frequency of all the steering errors. Is determined to be 90% or more. When the probability P is 90% or more, the process proceeds to step S1090. In this case, since i = 1, the α1 value is α1 = 0.001. If a negative determination is made in step S1089, the process returns to step S1088, i is incremented again, and the determination in step S1089 is repeated.

  After calculating the α1 value in step S1055 as described above, the process proceeds to step S1057 in FIG. In step S1057, whether the sample number n of the steering angle signal θ measured under the condition that the road curvature signal ρ is equal to or less than the predetermined value ρo and the host vehicle speed V is greater than the predetermined value Vo is greater than the predetermined value N2. Determine whether. Here, the predetermined value N2 is a threshold value for determining whether data of a sufficient intermediate period (for example, a traveling period of about one day) is acquired to determine a deviation from the normal driving operation of the driver. For example, N2 = 7200. If the number of samples n is greater than the predetermined value N2, the process proceeds to step S1059. If the number of samples is less than the predetermined value N2, this process is terminated.

  In step S1059, the middle period steering angle entropy Hp2 is calculated using the steering angle signal θ measured in the middle period. The middle period steering angle entropy Hp2 is a value calculated using the steering angle signal θ measured in the middle period with the steering angle signal θ measured over a long period as a reference state. It can be said that the value represents a deviation (deviation) from the unstable state. The calculation method of the medium period steering angle entropy Hp2 is the same as the long period steering angle entropy Hp1 described with reference to the flowchart of FIG. However, the driver's individual α1 value calculated in step S1055 is used as the α value.

  In step S1061, a difference ΔHp2 (= Hp2−Hp1) between the long-term steering angle entropy Hp1 calculated in step S1053 and the intermediate period steering angle entropy Hp2 calculated in step S1059 is calculated. Then, the driving operation unstable state is determined using the calculated difference ΔHp2. It may be considered that the greater the middle-period steering angle entropy Hp2 is relative to the long-term steering angle entropy Hp1, the greater the instability of the driving operation compared to the driving instability that the driver basically has. it can. Therefore, for example, when the difference ΔHp2 in the steering angle entropy is larger than the predetermined value TH2, it is determined that the driving operation is in an unstable state, and the process proceeds to step S1063.

  In step S1063, it is notified that the driving operation of the driver is in an unstable state. Here, for example, as shown in FIG. 62, in a region equal to or greater than a predetermined value TH2, the level is classified into two levels (D2P, D2PP) according to the magnitude of the steering angle entropy difference ΔHp2. Then, the driver is notified of the level corresponding to the difference ΔHp2 in the steering angle entropy calculated in step S1061.

  FIG. 63 shows an example of the notification content corresponding to the classification result of the steering angle entropy difference ΔHp2. When the classification result of the difference ΔHp2 is a level D2PP larger than the predetermined value TH2L (> TH2), the text “Unstable driving is greater than usual” is displayed on the display monitor of the display unit 180. indicate. When the classification result is a level D2P larger than the predetermined value TH2, a text indicating that “the degree of driving instability is slightly larger than usual” is displayed. Note that the notification content shown in FIG. 63 can be provided to the driver by voice output from the speaker 130.

  If a negative determination is made in step S1061, the unstable state notification process in step S1063 is skipped. As described above, after detecting the deviation from the unstable state of the normal driving operation and informing the driver, in the processing after step S1065, to the driver based on the comparison result of the past and present middle period steering angle entropy. Is notified. Specifically, the change in the instability of the driving operation in the middle period is compared by comparing the middle period steering angle entropy (hereinafter referred to as Hp2p) calculated in the past with the latest middle period steering angle entropy Hp2. Detect and notify the driver.

  First, in step S1065, the difference ΔHp2p between the past medium period steering angle entropy Hp2p calculated and stored in the past for the same driver and the latest medium period steering angle entropy Hp2 calculated in step S1059 is calculated. . Then, a change in instability of the driving operation is determined using the calculated difference ΔHp2p. The smaller the latest intermediate period steering angle entropy Hp2p with respect to the past intermediate period steering angle entropy Hp2p, that is, the more negative the sign of the difference ΔHp2p and the larger the value, the more unstable the driving operation than before. It can be considered that it has changed in the direction of decreasing. Therefore, for example, when the difference ΔHp2p in the steering angle entropy is smaller than the predetermined value TH2p, it is determined that the degree of instability of the driving operation is small, and the process proceeds to step S1067.

  In step S1067, it is notified that the instability of the driving operation of the driver is small and the steering operation is smoothing (driving improvement suggestion). Here, for example, as shown in FIG. 64, in a region smaller than a predetermined value TH2P, the level is classified into two levels (D2PM, D2PMM) according to the magnitude of the steering angle entropy difference ΔHp2p. Then, the driver is notified of the level corresponding to the difference ΔHp2p in the steering angle entropy calculated in step S1065.

  FIG. 65 shows an example of notification contents corresponding to the classification result of the steering angle entropy difference ΔHp2p. When the classification result of the difference ΔHp2p is a level D2PM that is smaller than the predetermined value TH2P, the text that indicates “the degree of unstable driving is slightly smaller than before” is displayed on the display monitor of the display unit 180. When the classification result is the level D2PMM that is smaller than the predetermined value TH2PL (<TH2P), the text indicating that the degree of instability of driving is smaller than before is displayed. The notification content shown in FIG. 65 can also be provided to the driver by voice output from the speaker 130.

  If a negative determination is made in step S1065, the driving improvement suggestion process in step S1067 is skipped and the process proceeds to step S1069. In step S1069, the intermediate period steering angle entropy Hp2 calculated in step S1059 is set as the past intermediate period steering angle entropy Hp2p in preparation for the next processing.

  In the processing after step S1071, the driver's personal α1 value calculated based on the long-term steering angle signal θ of the driver is used to suggest driving improvement to the driver. As described above, the α1 value is obtained by calculating the 90% tile value in the steering error distribution calculated based on the long-term steering angle signal data of the driver. Therefore, it can be said that the α1 value is a factor representing the distribution of the driver's long-term steering error.

  In step S1071, the α1 value calculated in step S1055 is compared with a predetermined value THα1. When the long-term α1 value of the driver is smaller than the predetermined value THα1, it can be considered that the degree of instability of the driving operation of the driver is small. Therefore, if the α1 value is smaller than the predetermined value THα1, the process proceeds to step S1073.

  In step S1073, it is notified that the instability of the driving operation of the driver is small and the steering operation is smooth (driving improvement suggestion). Here, for example, as shown in FIG. 66, in a region smaller than the predetermined value THα1, the two levels (α1S, α1SS) are classified according to the α1 value. Then, the driver is notified of the level corresponding to the α1 value calculated in step S1055.

  FIG. 67 shows an example of the notification content corresponding to the α1 value classification result. When the result of classifying the α1 value is a level α1S smaller than the predetermined value THα1, a text indicating that “the degree of driving instability is slightly small” is displayed on the display monitor of the display unit 180. When the classification result is a level α1SS that is smaller than a predetermined value THα1S (<THα1), a text indicating that “the degree of driving instability is small” is displayed. Note that the notification content shown in FIG. 67 can be provided to the driver by voice output from the speaker 130.

  Thus, the current process is terminated. If a negative determination is made in step S1071, the driving improvement suggestion process in step S1073 is skipped and the current process ends.

-Modification of the tenth embodiment-
Here, the long-term steering angle entropy Hp1 calculated using the driver's steering angle error distribution measured over a long period is set as the reference state. And, by calculating the steering angle entropy using the driver's steering error distribution measured in a short period of time, by detecting the short-term deviation from the unstable driving operation that the driver basically has, Perform driving diagnosis.

  The operation of the vehicle driving support apparatus 2 according to the second modification of the tenth embodiment will be described in detail with reference to FIG. FIG. 68 is a flowchart of the process procedure of the driving support control process in the controller 200 of the second modification of the tenth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec. The processing in steps S1111 to S1125 is the same as the processing in steps S1041 to S1055 of the flowchart shown in FIG.

  After the α1 value is calculated in step S1125, the process proceeds to step S1127. In step S1127, whether the sample number n of the steering angle signal θ measured under the condition that the road curvature signal ρ is equal to or less than the predetermined value ρo and the vehicle speed V is greater than the predetermined value Vo is greater than the predetermined value N3. Determine whether. Here, the predetermined value N3 is a threshold for determining whether data for a short period (for example, a traveling period of about 5 minutes) for determining a short-term deviation from the normal driving operation of the driver is acquired. For example, N3 = 1200. If the number of samples n is greater than the predetermined value N3, the process proceeds to step S1129, and if it is equal to or smaller than the predetermined value N3, this process ends.

  In step S1129, the short-term steering angle entropy Hp3 is calculated using the steering angle signal θ measured in a short time. The short-term steering angle entropy Hp3 is a value calculated using the steering angle signal θ measured for a short period with the steering angle signal θ measured for a long period as a reference state. It can be said that the value represents a short-term deviation (deviation) from the unstable state. The calculation method of the short-term steering angle entropy Hp3 is the same as the long-term steering angle entropy Hp1 described with reference to the flowchart of FIG. However, the driver's individual α1 value calculated in step S1125 is used as the α value.

  In step S1131, a difference ΔHp3 (= Hp3−Hp1) between the long-term steering angle entropy Hp1 calculated in step S1123 and the short-term steering angle entropy Hp3 calculated in step S1129 is calculated. Then, the driving operation unstable state is determined using the calculated difference ΔHp3. It is considered that as the short-term steering angle entropy Hp3 is larger than the long-term steering angle entropy Hp1, the instability of the current driving operation is larger than the driving instability basically possessed by the driver. be able to. Therefore, for example, when the difference ΔHp3 in the steering angle entropy is larger than the predetermined value TH3, it is determined that the driving operation is unstable, and the process proceeds to step S1133.

  In step S1133, it is notified that the current driving operation of the driver is in an unstable state. Here, for example, as shown in FIG. 69, in a region equal to or greater than a predetermined value TH3, the level is classified into two levels (D3P, D3PP) according to the magnitude of the steering angle entropy difference ΔHp3. Then, the driver is notified of the level corresponding to the difference ΔHp3 in the steering angle entropy calculated in step S1131.

  FIG. 70 shows an example of notification contents according to the classification result of the steering angle entropy difference ΔHp3. When the classification result of the difference ΔHp3 is a level D3PP that is larger than the predetermined value TH3L (> TH3), the text of the content “the state of driving instability is large” is displayed on the display monitor of the display unit 180. When the classification result is a level D3P that is greater than the predetermined value TH3, a text that indicates “the degree of driving instability is slightly high” is displayed. Note that the notification content shown in FIG. 70 can also be provided to the driver by voice output from the speaker 130.

  Thus, the current process is terminated. If the determination in step S1131 is negative, the unstable state notification process in step S1133 is skipped, and the current process ends.

<< Eleventh embodiment >>
The vehicle driving support apparatus according to the eleventh embodiment of the present invention will be described below. The basic configuration of the vehicle driving support apparatus according to the eleventh embodiment is the same as that of the tenth embodiment shown in FIG. Here, differences from the above-described tenth embodiment and its first and second modifications will be mainly described.

  In the vehicle driving support apparatus 2 according to the eleventh embodiment, an unstable driving operation state is detected by calculating the steering angle entropy using the steering angle signal, as in the tenth embodiment described above. Then, the driver is diagnosed for driving. And if the driver is driving at a higher risk than normal driving from the results of the driving diagnosis, that is, if the driver's driving deviates in the high risk direction, an alarm is given and a high risk is Notify the driver before falling into a bad state. On the other hand, if the driver's driving is better than the general standard from the driving diagnosis result, information presentation that suggests that the driver's driving is given up, that is, driving improvement suggestion, is performed so as to further improve the safety driving awareness.

  Here, when obtaining the steering angle entropy, it is necessary to determine which section of the reference distribution into which the estimation error of the steering angle belongs is divided into nine and calculate the probability of each section. Further, in order to obtain the α value (α1 described above), it is necessary to obtain the frequency distribution of the steering error. Since these processes are performed on all data of the number of samples, it is necessary to prepare buffer memories for the number of data in advance.

  Therefore, in the eleventh embodiment, even when it is necessary to temporarily store the most recent long-term steering angle data, the probability of each section can be recursively calculated with a small amount of memory. Calculate the probability of each category in (Recursive).

  The processing procedure of the long-term steering angle entropy Hp1 calculation process in the vehicle driving support apparatus 2 according to the eleventh embodiment will be described below with reference to the flowchart of FIG. This process is executed in step S1023 of the flowchart of FIG. 51 described in the tenth embodiment. The processing in steps S1231 to S1234 is the same as the processing in steps S1031 to S1034 in FIG.

  In step S1235, as shown in FIG. 54, the steering error en calculated in step S1234 is classified into nine sections b1 to b9 based on the α value (= αo) in the reference state, and the steering error en included in each section bi. The probability Pi for all frequencies is determined recursively. Here, the α value (= αo) in the reference state is set in advance based on the steering angle signal of the general driver group (general drivers), and is stored in the memory of the controller 200. The determination of the division to which the steering error en is allocated and a method for recursively obtaining the probability Pi of each division will be described with reference to the flowchart of FIG.

  First, in step S1241, 1 is set to i indicating a steering error category. In step S1242, it is determined whether i is greater than 9. If i> 9, it is determined that the calculation of the probability Pi for each of the nine sections has been completed, and this process ends. In the case of i ≦ 9, the process proceeds to step S1243 in order to calculate the probability Pi in each section.

In step S1243, it is determined whether the steering error en calculated in step S1234 corresponds to the target category bi. If the steering error corresponds to the category bi, the process proceeds to step S1244. In step S1244, the probability Pi (n) of the steering error en included in the section bi is calculated from the following (Equation 51). Here, the number of data is N.
Pi (n) = {Pi (n−1) + 1 / N} ÷ (1 + 1 / N) (Formula 51)

On the other hand, if the steering error does not fall into the category bi, the process proceeds to step S1245, and the probability Pi (n) of the steering error en included in the category bi is calculated from the following (formula 52).
Pi (n) = {Pi (n−1)} ÷ (1 + 1 / N) (Formula 52)

  In step S1246, (i + 1) is set as i. Thereafter, the process returns to step S1242, and the processes of steps S1243 to S1246 are repeated until the probability Pi of all nine sections is calculated.

  As described above, after the probability Pi of the steering error en included in each section bi is recursively calculated in step S1235, the process proceeds to step S1236. In step S1236, the long-term steering angle entropy Hp1 is calculated from (Equation 50) described above using the probability Pi calculated in step S1235. Thus, the long-term steering angle entropy Hp1 calculation process is terminated.

  Next, a method for recursively calculating the frequency in order to calculate the α1 value described in the first modification of the tenth embodiment will be described. The processing procedure of α1 value calculation processing in the vehicle driving support apparatus 2 according to the eleventh embodiment will be described below with reference to the flowchart of FIG. This process is executed in step S1055 of the flowchart of FIG. 57 described in the first modification of the tenth embodiment. The processing in steps S1251 to S1254 is the same as the processing in steps S1081 to S1084 in FIG.

  In step S1255, the frequency of the steering error en for each predetermined steering error is recursively calculated. This process will be described with reference to the flowchart of FIG.

  First, in step S1271, 1 is set to T that indicates the division of the frequency distribution of the steering error. In step S1272, it is determined whether T is larger than the total number of sections Tn. The total number of sections Tn is 40, for example, and the increment of each section is set to 0.001. When T> Tn and frequency calculation is completed in all sections, this process is terminated. In the case of T ≦ Tn, the process proceeds to step S1273 in order to calculate the frequency Ti (n) of each section.

In step S1273, it is determined whether or not the steering error en calculated in step S1254 corresponds to the target category Ti. If the steering error corresponds to the category Ti, the process proceeds to step S1274. In step S1274, the frequency Ti (n) of the steering error en included in the section Ti is calculated from the following (formula 53). Here, the number of data is N.
Ti (n) = {Ti (n−1) + 1 / N} ÷ (1 + 1 / N) (Formula 53)

On the other hand, if the steering error does not correspond to the section Ti, the process proceeds to step S1275, and the frequency Ti (n) of the steering error en included in the section Ti is calculated from the following (formula 54).
Ti (n) = {Ti (n−1)} ÷ (1 + 1 / N) (Formula 54)

  In step S1276, (i + 1) is set as i. Then, it returns to step S1272 and repeats the process of step S1273-S1276 until the frequency Ti of all the divisions Tn is calculated.

  As described above, after the frequency distribution is recursively calculated in step S1255, the process proceeds to step S1256. In the processing after step S1256, the α1 value is calculated using the frequency distribution calculated in step S1255, as in the first modification of the tenth embodiment described above.

  As described above, by recursively obtaining the steering angle entropy Hp, the memory capacity for data storage can be reduced, and the calculation process can be simplified because the calculation steps are reduced. Further, the steering angle entropy Hp can be calculated in real time.

<< Twelfth Embodiment >>
The vehicle driving support apparatus according to the twelfth embodiment of the present invention will be described below. FIG. 75 is a system diagram showing the configuration of the vehicle driving support apparatus 3 according to the twelfth embodiment. In the twelfth embodiment, portions having the same functions as those in the tenth embodiment described above are denoted by the same reference numerals, and description thereof is omitted. Here, differences from the tenth embodiment will be mainly described.

  The vehicle driving support apparatus 3 includes a laser radar 10, a front camera 15, a vehicle speed sensor 30, an acceleration sensor 35, a navigation system 50, an accelerator pedal opening sensor 55, a controller 250, a speaker 130, and a display unit 180.

  The laser radar 10 is attached to a front grill part or a bumper part of the vehicle, and scans the front area of the host vehicle by irradiating infrared light pulses in the horizontal direction. The laser radar 10 measures the reflected wave of the infrared light pulse reflected by a plurality of reflectors in front (usually the rear end of the preceding vehicle), and determines the distance between the vehicles from the arrival time of the reflected wave to the plurality of obstacles. Detect distance and relative speed respectively. The detected inter-vehicle distance and relative speed are output to the controller 250. The forward area scanned by the laser radar 10 is about ± 6 deg with respect to the front of the host vehicle, and a forward object existing in this range is detected.

  The acceleration sensor 35 is a sensor that detects the longitudinal acceleration of the host vehicle, and outputs the detected longitudinal acceleration to the controller 250. The accelerator pedal opening sensor 55 is a sensor that detects an operation amount (accelerator pedal opening) of an accelerator pedal (not shown). The accelerator pedal opening sensor 55 detects the accelerator pedal opening converted into the rotation angle of the servo motor via a link mechanism, for example, and outputs it to the controller 250.

  The controller 250 is an electronic control unit including a CPU and CPU peripheral components such as a ROM and a RAM, and controls the entire vehicle driving support device 3. The controller 250 analyzes the driving characteristics of the driver based on signals input from the laser radar 10, the front camera 15, the vehicle speed sensor 30, the acceleration sensor 35, the navigation system 50, the accelerator pedal opening sensor 55, and the like. Make a diagnosis. Then, information is provided to the driver based on the driving diagnosis result. Information provided to the driver includes warnings to the driver and suggestions for improving driving operations. Specific control contents in the controller 250 will be described later.

Next, the operation of the vehicle driving support apparatus 3 according to the twelfth embodiment will be described. First, the outline will be described.
The controller 250 of the vehicle driving support device 3 performs a driving diagnosis of the driver based on the traveling state of the host vehicle and the driving operation of the driver, and provides an alarm to the driver and improvement of the driving operation according to the driving diagnosis result. Make suggestions. Specifically, an unstable operation state is detected using the accelerator pedal opening signal, and the driver is diagnosed for driving. In the twelfth embodiment, the steering entropy method used in the tenth and eleventh embodiments is applied to the accelerator pedal operation in order to detect an unstable state of the driving operation. The steering entropy method is a method using a steering angle related to the lateral control of the host vehicle. In the twelfth embodiment, an accelerator pedal opening signal related to the longitudinal control of the host vehicle is used. Then, an unstable state of the driving operation in the front-rear direction of the host vehicle is detected.

  And if the driver is driving at a higher risk than normal driving from the results of the driving diagnosis, that is, if the driver's driving deviates in the high risk direction, an alarm is given and a high risk is Notify the driver before falling into a bad state. On the other hand, if the driver's driving is better than the general standard from the driving diagnosis result, the information is presented, that is, the improvement is suggested, so that the driver's driving is given up so as to further improve the safety driving awareness.

  The α value used in the twelfth embodiment (hereinafter referred to as αap value) is assumed to be a pedal operation error within a certain time based on the time-series data of the accelerator pedal opening, that is, the accelerator pedal is operated smoothly. The difference between the estimated value of the accelerator pedal opening and the actual accelerator pedal opening is obtained, the distribution (variation) of the accelerator pedal opening error is measured, and the 90% tile value (90% of the accelerator pedal opening error is The range of distribution included) is calculated.

  The accelerator pedal opening entropy value (hereinafter referred to as Hp_ap) represents the ambiguity (uncertainty) of the accelerator pedal opening error distribution. Similar to the αap value, the Hp_ap value decreases when the accelerator pedal operation is smooth and stable, and increases when it is unstable and unstable. The Hp_ap value is corrected by the αap value, and can be used as a driver instability that is not affected by the skill or habit of the driver.

  In the twelfth embodiment, the accelerator pedal opening entropy calculated using the accelerator pedal opening error distribution of the general driver group is set as the reference state. Then, the driver's accelerator pedal opening entropy is calculated using the measured driver's accelerator pedal opening error distribution, and a driving diagnosis is performed by detecting an unstable state of the driving operation that the driver basically has. .

  The accelerator pedal opening error is affected by the road alignment, the relationship with the preceding vehicle, the driver's load condition, etc., so the reference accelerator pedal opening error distribution when the driver is unloaded It is necessary to measure. Therefore, in the twelfth embodiment, in order to be less susceptible to these effects, accelerator pedal opening data is used in a road linear traveling that can be regarded as a straight road and at a constant vehicle speed. An accelerator pedal opening distribution is obtained based on the time-measured accelerator pedal opening data.

  The operation of the vehicle driving support apparatus 3 according to the twelfth embodiment will be described in detail with reference to FIG. FIG. 76 is a flowchart of the process procedure of the driving support control process in the controller 250 according to the twelfth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  In step S1331, the accelerator pedal opening signal dap detected by the accelerator pedal opening sensor 55 is read. In step S1333, the road curvature signal ρ of the road on which the host vehicle travels is read. In step S1335, the vehicle speed signal Vap of the host vehicle detected by the vehicle speed sensor 30 is read. In step S1337, an obstacle ahead of the host vehicle detected by the laser radar 10, specifically, an inter-vehicle distance dd and a relative speed vr with the preceding vehicle are read. In step S1339, the longitudinal acceleration xg of the host vehicle detected by the acceleration sensor 35 is read.

  In subsequent steps S1341 to S1349, it is determined whether or not to calculate the accelerator pedal opening entropy. Specifically, in step 1341, it is determined whether or not the road curvature signal ρ read in step S1333 is smaller than a road curvature predetermined value ρapo for determining that the vehicle is traveling on a straight road. If the road curvature signal ρ is equal to or less than the predetermined value ρapo, it is determined that the host vehicle is traveling on a straight road, and the process proceeds to step S1343. If ρ> ρapo, this process ends.

  In step S1343, it is determined whether the vehicle speed signal Vap read in step S1335 is greater than a predetermined value Vapo. The predetermined value Vapo is a threshold value for determining whether the host vehicle is traveling in a scene where the vehicle can travel at a constant vehicle speed, such as an expressway, for example, and is set to about Vapo = 60 km / h, for example. If the vehicle speed signal Vap is greater than the predetermined value Vapo, the process proceeds to step S1345. If the vehicle speed signal Vap is equal to or less than the predetermined Vapo, the process ends.

In step S1345, first, a margin time ttc of the host vehicle relative to the preceding vehicle is calculated using the inter-vehicle distance dd and the relative speed vr read in step S1337. The allowance time ttc represents the allowance time until the own vehicle and the preceding vehicle come into contact when the own vehicle speed Vap and the relative vehicle speed vr are constant, and is calculated from the following (Equation 55).
ttc = dd / vr (Formula 55)

  Then, the margin time ttc calculated from (Equation 55) is compared with a predetermined value ttcapo. The predetermined value ttcapo is a threshold value for determining whether the host vehicle follows the preceding vehicle while maintaining a sufficient margin time ttc. If the allowance time ttc is greater than the predetermined value ttcapo and the host vehicle follows the preceding vehicle, the process proceeds to step S1347. If ttc ≦ ttcap, this process ends.

  In step S1347, the longitudinal acceleration xg calculated in step S1339 is compared with a predetermined value xg_apo. The predetermined value xg_apo is a threshold value for determining whether or not the host vehicle is traveling at a substantially constant vehicle speed. If it is determined that xg <xg_apo and the host vehicle is traveling at a substantially constant vehicle speed, the process proceeds to step S1349. If xg ≧ xg_apo, the process ends.

  In step S1349, the accelerator pedal opening dap calculated in step S1331 is compared with a predetermined value dapo. The predetermined value dapo is a threshold value for determining whether the driver is depressing the accelerator pedal. If it is determined that the accelerator pedal is being operated with dap> dapo, the process proceeds to step 1351, and if dap ≦ dapo, this process ends.

  In step S1351, it is determined whether or not the sample number n of the accelerator pedal opening signal dap measured under the above-described conditions is greater than a predetermined value Nap1. Here, the predetermined value Nap1 is a threshold value for determining whether data for a long period of time sufficient to make it less susceptible to the influence of the driver's load is obtained. For example, Nap1 = 100000 . Note that 100,000 pieces of data are expected to be obtained in a traveling period of about three weeks. If the number of samples n is greater than the predetermined value Nap1, the process proceeds to step S1353, and if it is equal to or smaller than the predetermined value Nap1, this process ends.

  In step S1353, accelerator pedal opening entropy Hp_ap1 (hereinafter referred to as long-term accelerator pedal opening entropy) is calculated using the accelerator pedal opening signal dap measured over a long period of time. The long-term accelerator pedal opening entropy Hp_ap1 is a value representing an unstable state of the driving operation that the driver basically has, and can be said to be a driver's usual degree of instability. The calculation method of the accelerator pedal opening entropy Hp_ap1 is basically the same as the steering angle entropy Hp described above. However, in the following description, the smooth value of the accelerator pedal opening is represented by dapn-tilde, the estimated value of the accelerator pedal opening is represented by dapn-hat, and the accelerator pedal opening error is represented by e_apn.

  The long-time accelerator pedal opening entropy Hp_ap1 calculation process will be described with reference to the flowchart of FIG. In step S 1361, time series data of n accelerator pedal opening signals dapn measured at the sampling interval Ts under the above-described conditions is collected. The sampling interval Ts is, for example, 50 msec.

  In step S1362, the three accelerator pedal opening smooth values dapn-tilde are calculated according to the above (Equation 45) using the three accelerator pedal opening dapn adjacent to each other at intervals of 150 msec. In step S1363, using the three accelerator pedal opening smooth values dapn-tilde calculated in step 1362, an estimated value dapn-hat of the accelerator pedal opening is calculated according to the above (Equation 44). In step S1364, the accelerator pedal opening error e_apn is calculated according to the above (Equation 47) using the estimated accelerator pedal opening value dapn-hat calculated in step S1363 and the actual accelerator pedal opening signal dapn.

  In step S1365, the accelerator pedal opening error e_apn calculated in step S1364 is classified into nine sections b1 to b9 based on the α value (= αo_ap) in the reference state as shown in FIG. 54, and the accelerator included in each section bi. A probability Pi for all frequencies of the pedal opening error e_apn is obtained. Here, the α value (= αo_ap) in the reference state is set in advance based on the accelerator pedal opening signal of the general driver group (general drivers) and stored in the memory of the controller 250. Then, the category bi is set when the program shown in FIG. 76 is executed.

  In subsequent step S1366, long-term accelerator pedal opening entropy Hp_ap1 is calculated according to the above (Equation 50) using the probability Pi calculated in step S1365.

  The smaller the accelerator pedal opening entropy Hp_ap1 for a long period of time, the greater the steepness of the distribution of the accelerator pedal opening error e_apn, and the distribution of the accelerator pedal opening error e_apn falls within a certain range. That is, the accelerator pedal operation is performed smoothly and the operation is in a stable state. Conversely, the greater the long-term accelerator pedal opening entropy Hp_ap1 value, the smaller the steepness of the distribution of the accelerator pedal opening error e_apn, and the distribution of the accelerator pedal opening error e_apn varies. That is, the accelerator pedal operation is staggered, indicating that the driving is unstable.

  Thus, after calculating the accelerator pedal opening degree entropy Hp_ap1 for a long period in step S1353, the process proceeds to step S1355. In step S1355, a warning or guidance (improvement suggestion) is presented to the driver according to the long-term accelerator pedal opening entropy Hp_ap1. It can be considered that the greater the long-term accelerator pedal opening entropy Hp_ap1, the greater the instability of the driving operation compared to the average accelerator pedal opening entropy of the general driver group (general public).

  Therefore, for example, as shown in FIG. 78, the long-term accelerator pedal opening entropy Hp_ap1 is classified into five levels (SSS, SS, SM, SL, SLL), and the calculated long-term accelerator pedal opening is calculated. Inform the driver of the level of entropy Hp_ap1. In the classification shown in FIG. 78, five levels are appropriately set so that the average accelerator pedal opening entropy of the general driver group (general public) falls within the central level SM.

  FIG. 79 shows an example of notification contents according to the classification result of the long-term accelerator pedal opening entropy Hp_ap1. When the classification result of the accelerator pedal opening entropy Hp_ap1 for a long period of time is the level SLL, the text indicating that “the driving degree of instability is large” is displayed on the display monitor of the display unit 180. When the classification result is level SL, the text of the content “the degree of driving instability is a slightly large type” is displayed. When the classification result is level SM and is equivalent to the average accelerator pedal opening entropy of the general driver group, the text “Understanding of driving is normal” is displayed. When the classification result is level SS, the text of the content “the degree of driving instability is slightly small” is displayed. When the classification result is level SSS, the text of the content “the degree of driving instability is small” is displayed. Note that the notification content shown in FIG. 79 can be provided to the driver by voice output from the speaker 130.

Thus, in the twelfth embodiment described above, in addition to the effects of the first to eleventh embodiments described above, the following operational effects can be achieved.
As an index for driving diagnosis, a control result of driving operation by the driver is detected. Specifically, the accelerator pedal opening entropy Hp_ap representing the degree of instability of the accelerator pedal operation by the driver is calculated using the accelerator pedal operation amount. Thereby, it is possible to perform a driving diagnosis with high accuracy for the driving operation in the front-rear direction of the host vehicle.

-Modification 1 of the twelfth embodiment-
Here, the long-term accelerator pedal opening entropy Hp_ap1 calculated using the driver's accelerator pedal opening error distribution measured over a long period is set as the reference state. Then, the accelerator pedal opening entropy is calculated using the driver's accelerator pedal opening error distribution measured in the middle period, and the deviation from the unstable driving operation that the driver basically has is detected. To perform driving diagnosis.

  The operation of the vehicle driving support apparatus 3 according to the first modification of the twelfth embodiment will be described in detail with reference to FIGS. 80 and 81 are flowcharts of the processing procedure of the driving support control process in the controller 250 of the first modification of the twelfth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec. The processing in steps S1371 to S1393 is the same as the processing in steps S1331 to S1353 in the flowchart shown in FIG. However, if it is determined in step S139 that the number of samples n is equal to or smaller than the predetermined value Nap1, the process proceeds to step S1397 in FIG. 81 without terminating the process.

  After calculating the long-term accelerator pedal opening entropy Hp_ap1 representing the reference state in step S1393, in step S1395, an α value used for calculating the accelerator pedal opening entropy in the middle period is calculated. Here, the driver's individual α value (hereinafter referred to as αap1 value) is calculated using the accelerator pedal opening dap measured over a long period of time. This process will be described with reference to the flowchart of FIG.

  In step S1421, time series data of n accelerator pedal openings dapn measured at the sampling interval Ts under the above-described conditions is collected. The sampling interval Ts is, for example, 50 msec. In step S <b> 1422, three accelerator pedal opening smooth values dapn-tilde are calculated according to the above (Equation 45) using the three accelerator pedal opening dapn adjacent to each other at intervals of 150 msec.

  In step S1423, using the three accelerator pedal opening smooth values dapn-tilde calculated in step 1422, the estimated value dapn-hat of the accelerator pedal opening is calculated according to the above (Equation 44). In step S1424, using the accelerator pedal opening estimated value dapn-hat calculated in step S1423 and the actual accelerator pedal opening signal dapn, the accelerator pedal opening error e_apn is calculated according to the above (Equation 47).

  In subsequent step S1425, the frequency of the accelerator pedal opening error e_apn is counted for each predetermined accelerator pedal opening error. Here, the predetermined accelerator pedal opening error is determined in consideration of the resolution of the accelerator pedal opening sensor 55. Here, for example, as shown in the table of FIG. 60 used for calculating the steering error distribution, the accelerator pedal opening error e_apn is classified every 0.001. In step S1426, 0 is set to i_ap indicating the classification of the accelerator pedal opening error.

  In step S1427, it is determined whether the probability Pap of the frequency Tap 0.000 at the accelerator pedal opening error e_apn = 0.000 is 90% or more with respect to all the frequencies of all accelerator pedal opening errors. When the probability Pap of the frequency Tap 0.000 is 90% or more, the process proceeds to step S1430. In this case, since i_ap = 0, the αap1 value is αap1 = 0.000. If a negative determination is made in step S1427, the process proceeds to step S1428. In step S1428, i_ap is incremented by 1 and set to (i_ap + 1).

  In step S1429, the accelerator pedal opening error classification is expanded, and all accelerator pedals having the frequency of the accelerator pedal opening error e_apn from -0.001 to +0.001 (Tap0.000 + Tap0.001 + Tap−0.001) are obtained. It is determined whether or not the probability Pap for the entire frequency of the opening error is 90% or more. When the probability Pap is 90% or more, the process proceeds to step S1430. In this case, since i_ap = 1, the αap1 value is αap1 = 0.001. If a negative determination is made in step S1429, the process returns to step S1428, i_ap is incremented again, and the determination in step S1429 is repeated.

  After calculating the α_ap1 value in step S1395 as described above, the process proceeds to step S1397 in FIG. In step S1397, it is determined whether or not the sample number n of the accelerator pedal opening dap measured under the above conditions is larger than a predetermined value Nap2. Here, the predetermined value Nap2 is a threshold value for determining whether data of a sufficient middle period (for example, a traveling period of about one day) has been acquired to determine a deviation from the normal driving operation of the driver. For example, Nap2 = 7200. If the number of samples n is greater than the predetermined value Nap2, the process proceeds to step S1399. If the number n of samples is equal to or smaller than the predetermined value Nap2, this process ends.

  In step S1399, the middle period steering angle entropy Hp_ap2 is calculated using the accelerator pedal opening signal dap measured in the middle period. The middle period accelerator pedal opening entropy Hp_ap2 is obtained by setting the accelerator pedal opening signal dap measured in the middle period using the long term accelerator pedal opening entropy Hp_ap1 using the accelerator pedal opening signal dap measured in the long period as a reference state. It can be said that this is a value calculated by using the value and represents a deviation (deviation) from an unstable state of the driving operation that the driver basically has. The calculation method of the middle period accelerator pedal opening entropy Hp_ap2 is the same as the long-term accelerator pedal opening entropy Hp_ap1 described with reference to the flowchart of FIG. However, the driver's individual αap1 value calculated in step S1395 is used as the α value.

  In step S1401, a difference ΔHp_ap2 (= Hp_ap2−Hp_ap1) between the long-term accelerator pedal opening entropy Hp_ap1 calculated in step S1393 and the middle period accelerator pedal opening entropy Hp_ap2 calculated in step S1399 is calculated. Then, the driving operation unstable state is determined using the calculated difference ΔHp_ap2. As the middle period accelerator pedal opening entropy Hp_ap2 is larger than the long term accelerator pedal opening entropy Hp_ap1, the instability of the driving operation is larger than the driving instability that the driver basically has. Can be considered. Therefore, for example, when the difference ΔHp_ap2 in the accelerator pedal opening entropy is larger than the predetermined value THap2, it is determined that the driving operation is in an unstable state, and the process proceeds to step S1403.

  In step S1403, it is notified that the driving operation of the driver is in an unstable state. Here, for example, as shown in FIG. 83, in a region equal to or greater than a predetermined value THap2, the level is classified into two levels (D2Pap, D2PPap) according to the magnitude of the accelerator pedal opening entropy difference ΔHp_ap2. Then, the driver is notified of the level corresponding to the difference ΔHp_ap2 in the accelerator pedal opening entropy calculated in step S1401.

  FIG. 84 shows an example of notification contents according to the classification result of the difference ΔHp2 in the accelerator pedal opening entropy. When the classification result of the difference ΔHp2 is a level D2PPap that is larger than the predetermined value THap2L (> THap2), the text “Unstable driving is greater than usual” is displayed on the display monitor of the display unit 180. indicate. When the classification result is a level D2Pap that is larger than the predetermined value THap2, a text indicating that “the degree of instability of driving is slightly larger than usual” is displayed. Note that the notification content shown in FIG. 84 can also be provided to the driver by sound output from the speaker 130.

  If a negative determination is made in step S1401, the unstable state notification process in step S1403 is skipped. As described above, after detecting the deviation from the unstable state of the normal driving operation and informing the driver, the driving based on the comparison result of the past and present middle period accelerator pedal opening entropy in the processing after step S1405. Notification to the person. Specifically, the middle period accelerator pedal opening entropy (hereinafter referred to as Hp_ap2p) calculated in the past and the latest middle period accelerator pedal opening entropy Hp_ap2 are compared to make the driving operation unstable during the middle period. A change in the degree is detected and notified to the driver.

  First, in step S1405, the difference between the past middle period accelerator pedal opening entropy Hp_ap2p calculated and stored in the past for the same driver and the latest middle period accelerator pedal opening entropy Hp_ap2 calculated in step S1399. ΔHp_ap2p is calculated. Then, a change in instability of the driving operation is determined using the calculated difference ΔHp_ap2p. The smaller the latest middle-period accelerator pedal opening entropy Hp_ap2 than the past middle-period accelerator pedal opening entropy Hp_ap2p, that is, the difference ΔHp_ap2p has a negative sign and the larger the value, the more the driving operation is performed. It can be considered that the degree of instability has changed in a decreasing direction. Therefore, for example, when the difference ΔHp_ap2p in the accelerator pedal opening entropy is smaller than the predetermined value THap2P, it is determined that the instability of the driving operation is small, and the process proceeds to step S1407.

  In step S1407, it is notified that the degree of instability of the driving operation of the driver is small and the accelerator pedal operation is smoothing (driving improvement suggestion). Here, for example, as shown in FIG. 85, in a region smaller than a predetermined value THap2P, the level is classified into two levels (D2PMap, D2PMMMap) according to the magnitude of the accelerator pedal opening entropy difference ΔHp_ap2p. Then, the driver is notified of the level corresponding to the difference ΔHp_ap2p in the accelerator pedal opening entropy calculated in step S1405.

  FIG. 86 shows an example of the notification content corresponding to the classification result of the accelerator pedal opening entropy difference ΔHp_ap2p. When the classification result of the difference ΔHp_ap2p is a level D2PMap that is smaller than the predetermined value THap2P, the text “The degree of instability of operation is slightly smaller than before” is displayed on the display monitor of the display unit 180. When the classification result is a level D2PMMap that is smaller than a predetermined value THap2PL (<THap2P), a text indicating that the degree of instability of driving is smaller than before is displayed. Note that the notification content shown in FIG. 86 can also be provided to the driver by voice output from the speaker 130.

  If a negative determination is made in step S1405, the driving improvement suggestion process in step S1407 is skipped and the process proceeds to step S1409. In step S1409, the intermediate period accelerator pedal opening entropy Hp_ap2 calculated in step S1399 is set as the past intermediate period accelerator pedal opening entropy Hp_ap2p in preparation for the next processing.

  In the processing subsequent to step S1411, the driver's personal αap1 value calculated based on the driver's long-term accelerator pedal opening signal dap is used to suggest driving improvement to the driver. As described above, the αap1 value is obtained by calculating the 90% tile value in the distribution of the accelerator pedal opening error calculated based on the long-term accelerator pedal opening signal data of the driver. Therefore, it can be said that the αap1 value is a factor that represents the long-term distribution of the accelerator pedal opening error of the driver.

  In step S1411, the αap1 value calculated in step S1430 is compared with a predetermined value THαap1. When the long-term αap1 value of the driver is smaller than the predetermined value THαap1, it can be considered that the degree of instability of the driving operation of the driver is small. Therefore, if the αap1 value is smaller than the predetermined value THαap1, the process proceeds to step S1413.

  In step S1413, the driver is informed that the degree of instability of the driving operation is small and the accelerator pedal operation is smooth (driving improvement suggestion). Here, for example, as shown in FIG. 87, in an area smaller than a predetermined value THαap1, the level is classified into two levels (α1apS, α1apSS) according to the αap1 value. Then, the driver is notified of the level corresponding to the αap1 value calculated in step S1430.

  FIG. 88 shows an example of the notification content corresponding to the αap1 value classification result. When the result of classifying the αap1 value is a level α1apS smaller than the predetermined value THαap1, a text indicating “the degree of driving instability is slightly small” is displayed on the display monitor of the display unit 180. When the classification result is a level α1apSS that is smaller than a predetermined value THαap1S (<THαap1), a text indicating that “the degree of driving instability is small” is displayed. The notification content shown in FIG. 88 can also be provided to the driver by voice output from the speaker 130.

  Thus, the current process is terminated. If a negative determination is made in step S1411, the driving improvement suggestion process in step S1413 is skipped, and the current process ends.

-Modification of the twelfth embodiment-
Here, the long-term accelerator pedal opening entropy Hp_ap1 calculated using the driver's accelerator pedal opening error distribution measured over a long period is set as the reference state. Then, the accelerator pedal opening entropy is calculated using the driver's accelerator pedal opening error distribution measured in a short time, and the short-term deviation from the unstable driving operation that the driver basically has is calculated. By detecting this, driving diagnosis is performed.

  The operation of the vehicle driving support apparatus 3 according to the second modification of the twelfth embodiment will be described in detail with reference to FIG. FIG. 89 is a flowchart of the process procedure of the driving support control process in the controller 250 of the second modification of the twelfth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec. The processing in steps S1141 to S1465 is the same as the processing in steps S1371 to S1395 in the flowchart shown in FIG.

  After calculating the αap1 value in step S1395, the process proceeds to step S1467. In step S1467, it is determined whether or not the sample number n of the accelerator pedal opening signal dap measured under the above conditions is larger than a predetermined value Nap3. Here, the predetermined value Nap3 is a threshold for determining whether data of a short period (for example, a traveling period of about 5 minutes) for determining a short-term deviation from the normal driving operation of the driver is acquired. For example, Nap3 = 1200. If the number of samples n is greater than the predetermined value Nap3, the process proceeds to step S1469. If the number n of samples is equal to or smaller than the predetermined value Nap3, this process ends.

  In step S1469, a short-term accelerator pedal opening entropy Hp_ap3 is calculated using the accelerator pedal opening signal dap measured in a short time. The short-term accelerator pedal opening entropy Hp_ap3 is calculated using the accelerator pedal opening signal dap measured for a short period with the accelerator pedal opening error distribution using the accelerator pedal opening signal dap measured for a long period as a reference state. It is a calculated value, and can be said to be a value representing a short-term deviation (deviation) from an unstable driving operation that the driver basically has. The calculation method of the short-term accelerator pedal opening entropy Hp_ap3 is the same as the long-term accelerator pedal opening entropy Hp_ap1 described using the flowchart of FIG. However, the driver's individual αap1 value calculated in step S1465 is used as the α value.

  In step S1471, a difference ΔHp_ap3 (= Hp_ap3-Hp_ap1) between the long-term accelerator pedal opening entropy Hp_ap1 calculated in step S1463 and the short-term accelerator pedal opening entropy Hp_ap3 calculated in step S1469 is calculated. Then, the driving operation unstable state is determined using the calculated difference ΔHp_ap3. As the short-term accelerator pedal opening entropy Hp_ap3 is larger than the long-term accelerator pedal opening entropy Hp_ap1, the instability of the current driving operation becomes larger than the driving instability basically possessed by the driver. Can be considered. Therefore, for example, when the difference ΔHp_ap3 of the accelerator pedal opening entropy is larger than the predetermined value THap3, it is determined that the driving operation is in an unstable state, and the process proceeds to step S1473.

  In step S1473, it is notified that the current driving operation of the driver is in an unstable state. Here, for example, as shown in FIG. 90, in a region equal to or greater than a predetermined value THap3, the level is classified into two levels (D3Pap, D3PPap) according to the magnitude of the accelerator pedal opening entropy difference ΔHp_ap3. Then, the driver is notified of the level corresponding to the difference ΔHp_ap3 in the accelerator pedal opening entropy calculated in step S1471.

  FIG. 91 shows an example of the notification content corresponding to the classification result of the accelerator pedal opening entropy difference ΔHp_ap3. When the classification result of the difference ΔHp_ap3 is a level D3PPap that is larger than the predetermined value THap3L (> THap3), the text “The degree of driving instability is large” is displayed on the display monitor of the display unit 180. When the classification result is a level D3Pap that is larger than the predetermined value THap3, a text with the content “the degree of driving instability is slightly high” is displayed. Note that the notification content shown in FIG. 62 can be provided to the driver by voice output from the speaker 130.

  Thus, the current process is terminated. If a negative determination is made in step S1471, the unstable state notification process in step S1473 is skipped, and the current process ends.

<< Thirteenth embodiment >>
A vehicle driving support apparatus according to a thirteenth embodiment of the present invention will be described with reference to the drawings. FIG. 92 is a system diagram showing the configuration of the vehicle driving support apparatus 7 according to the thirteenth embodiment of the invention. In FIG. 92, the same components as those in the first to twelfth embodiments described above are denoted by the same reference numerals. Here, differences from the first to twelfth embodiments will be mainly described.

  As shown in FIG. 92, the vehicle driving assistance device 7 includes a steering angle sensor 5, a laser radar 10, a front camera 15, a vehicle speed sensor 30, an acceleration sensor 35, a navigation system 50, an accelerator pedal stroke sensor 55, and a brake pedal stroke sensor. 60, a blinker switch 65, a controller 500, a speaker 130, and a display unit 180.

  The vehicle driving support device 7 includes a steering angle sensor 5, a laser radar 10, a front camera 15, a vehicle speed sensor 30, an acceleration sensor 35, a navigation system 50, an accelerator pedal stroke sensor 55, a brake pedal stroke sensor 60, a blinker switch 65, and the like. Based on the input signal, the driving characteristics of the driver are analyzed, and the driving diagnosis is performed by comprehensively detecting the characteristics of the driver's recognition, judgment and operation.

  Specifically, as shown in FIG. 93, as a driving diagnosis index, the driver's risk recognition characteristics, the driver's safety awareness / driving preference characteristics, the driver's risk tolerance, and the driving performance control results by the driver The driving diagnosis is performed by comprehensively determining the detection results of these indices. Then, based on the driving diagnosis result, information such as a warning to the driver and an improvement suggestion of driving operation is provided.

  The operation of the vehicle driving support apparatus 7 according to the thirteenth embodiment will be described in detail with reference to FIG. FIG. 94 is a flowchart of the process procedure of the driving support control process in the controller 500 according to the thirteenth embodiment. This processing content is continuously performed at regular intervals, for example, every 50 msec.

  First, in step S1510, the traveling state of the host vehicle is detected. Here, as the traveling state of the host vehicle, the host vehicle speed V detected by the vehicle speed sensor 30, the inter-vehicle distance D and the relative speed Vr between the host vehicle and the preceding vehicle detected by the laser radar 10, and imaging by the front camera 15 are detected. The distance to the lane marker obtained from the image is acquired. In step S1520, the operation state of the driver is detected. Here, as an operation state of the driver, an accelerator pedal operation amount detected by the accelerator pedal stroke sensor 55, a brake pedal operation amount detected by the brake pedal stroke sensor 60, and presence / absence of a winker lever operation detected by the winker switch 65 The steering angle detected by the steering angle sensor 5 is acquired.

  In step S1530, an individual indicator for performing a driving diagnosis is calculated based on the vehicle running state acquired in step S1510 and the driver operation state acquired in step S1520. 93, as shown in FIG. 93, as shown in FIG. 93, the margin time TTC distribution when the accelerator pedal is off, the lane departure time TLC distribution during the correction steering, the vehicle speed distribution during the independent traveling, the inter-vehicle time THW distribution during the following traveling, Calculates maximum acceleration at start-up, distribution of minimum margin time TTC during braking, minimum inter-vehicle distance during overtaking, smoothness of steering (steering angle entropy), and stability of accelerator pedal operation (accelerator pedal opening entropy) To do. These indicators indicate the respective characteristics for recognition, judgment, and operation in the driving process of the driver.

  The distribution of the allowance time TTC when the accelerator pedal is off is data indicating the driver's risk perception characteristics, and is calculated based on the inter-vehicle distance D between the host vehicle and the preceding vehicle as described in the first embodiment. can do. The distribution of the lane departure time TLC during the correction steering is also data indicating the driver's risk recognition characteristics, and can be calculated based on the lane lateral position of the host vehicle as described in the second embodiment. .

  The vehicle speed distribution when traveling alone is data indicating the driver's safety consciousness / driving preference characteristics, and can be calculated based on the own vehicle speed V as described in the third embodiment. The distribution of the inter-vehicle time THW during the following traveling is also data indicating the driver's safety consciousness / driving preference characteristics, and as described in the fourth to sixth embodiments, the inter-vehicle time between the host vehicle and the preceding vehicle It can be calculated based on D. The maximum acceleration at the time of start is also data indicating the driver's safety consciousness / driving preference characteristics, and can be calculated based on the acceleration / deceleration of the host vehicle as described in the seventh embodiment.

  The distribution of the minimum margin time TTC during braking is data indicating the risk tolerance of the driver, and is calculated based on the inter-vehicle distance D between the host vehicle and the preceding vehicle, as described in the eighth embodiment. be able to. The minimum inter-vehicle distance at the time of overtaking is also data indicating the risk tolerance of the driver, and can be calculated based on the inter-vehicle distance between the host vehicle and the preceding vehicle as described in the ninth embodiment. .

  The smoothness of steering (steering angle entropy) is data indicating the control result of the driving operation by the driver, and can be calculated based on the steering angle as described in the tenth and eleventh embodiments. . The stability of the accelerator operation (accelerator pedal opening entropy) is also data indicating the control result of the driving operation by the driver, and is calculated based on the accelerator pedal operation amount as described in the twelfth embodiment. Can do.

  In step S1540, each individual index calculated in step S1530 is stored.

  In step S1600, a driver's driving diagnosis is comprehensively performed from the individual index calculated in step S1530. First, for each individual index, as described in the first to twelfth embodiments, the current distribution (the data distribution of “this time” or “this day” to be compared) is changed to the normal distribution (reference). Compared to the “normal” data distribution). Then, the degree of deviation is calculated with a minus value when deviating to the high risk side and a plus value deviating to the low risk side with respect to the normal state. The deviation degree is scored so that a state that completely matches the normal distribution is set to zero and each falls within a range of ± 10.

  Furthermore, a predetermined weight is given to each individual index, and a total index is calculated by obtaining the sum of products of the weight and the score of each deviation degree. The weights between the individual indexes are set equally. However, it is also possible to set the weights individually in consideration of the degree of influence on the actual vehicle behavior. In this case, rather than the weights for the indicators related to cognition (distribution of the margin time TTC when the accelerator pedal is off, the distribution of the lane departure time TLC when the steering is corrected), the indicators are related to the judgment (the vehicle speed distribution when traveling alone, the distance between following vehicles) Set weights for time THW distribution, maximum acceleration when starting, distribution of minimum margin time TTC during braking, minimum inter-vehicle distance during overtaking, and further control indicators (smoothness of steering, accelerator pedal operation) (Stability) is set larger.

  Finally, the sum of the product of the weight and each score is normalized by dividing by the highest possible score and multiplied by 10 to score the overall index within the range of ± 10.

  In subsequent step S1610, the score of the comprehensive index calculated in step S1600 is compared with a first predetermined value in order to determine whether or not to execute the alarm presenting process. The first predetermined value for determining whether or not to present an alarm is, for example, -3 points. When the score of the comprehensive index is equal to or less than the first predetermined value, an affirmative determination is made in step S1610 and the process proceeds to step S1620. In step S <b> 1620, an alarm sound is output from the speaker 130 in order to notify the driver that the driver's driving is totally deviating in the high risk direction. Specifically, a warning is presented by a buzzer or voice so that the driving tendency is effectively communicated to the driver in an easy-to-understand manner and does not lead to a high-risk state.

  In step S1630, one of the individual indices having the lowest score is selected, and the selected index is displayed on the display screen of the display unit 180 as a point requiring improvement. As a result, attention is paid to points to be improved that the driver should pay particular attention to while driving.

  If a negative determination is made in step S1610, the process proceeds to step S1710, and the score of the comprehensive index calculated in step S1600 is compared with a second predetermined value in order to determine whether or not an improvement suggestion is presented. The second predetermined value for determining whether or not to present an improvement suggestion is, for example, +3 points. If the score of the comprehensive index is greater than or equal to the second predetermined value, an affirmative decision is made in step S1710 and the process proceeds to step S1720. When the score of the comprehensive index is equal to or greater than the second predetermined value, the result of the driver's driving diagnosis is good, indicating that stable and excellent driving is possible. Accordingly, in step S1720, for example, the score of the comprehensive index is displayed on the display screen of the display unit 180 together with a message such as “appropriately good driving is possible”. If step S1710 is negatively determined, improvement suggestion is not performed. Thus, the current process is terminated.

Thus, in the thirteenth embodiment described above, the following operational effects can be achieved.
(1) The vehicle driving support device 7 comprehensively detects the characteristics of the driver's recognition, determination, and operation as an index for driving diagnosis. As a result, it is possible to comprehensively determine whether the driver is driving well and perform an accurate driving diagnosis.
(2) As an index for driving diagnosis, (a) The distribution of the degree of approach (for example, margin time TTC) at the timing when the accelerator pedal is released while approaching the preceding vehicle, or corrective steering is performed while approaching the lane boundary The distribution of the degree of approach to the lane boundary at the timing (for example, the lane departure time TLC), (b) the distribution of the own vehicle speed V when there is no preceding vehicle, and the inter-vehicle time between the own vehicle following the preceding vehicle and the preceding vehicle THW distribution, or distribution of acceleration of the host vehicle when starting from a stopped state, (c) the degree of approach between the host vehicle and the preceding vehicle at the timing when the brake operation is performed while the preceding vehicle is approaching (for example, margin time TTC) Distribution or distribution of minimum inter-vehicle distance D when overtaking the preceding vehicle from the following state, and (d) smoothness of steering operation (steering angle entropy) or smoothness of accelerator pedal operation Accelerator pedal opening entropy), the use of the comprehensive determining indicators. This makes it possible to comprehensively determine whether the driver is driving well.

  In the first to thirteenth embodiments described above, after performing the driving diagnosis, the alarm presenting process or the improvement suggesting process is performed according to the driving diagnosis result. However, the present invention is not limited to this, and the vehicle driving support device may be configured to perform only driving diagnosis from the driving state and the driving operation. In this case, for example, the driving diagnosis result can be presented only when the driver desires, or the driving diagnosis result can be transmitted to a base station or the like that collects data.

  In the first to thirteenth embodiments described above, the laser radar 10, the front camera 15, the vehicle speed sensor 30, the acceleration sensor 35, and the navigation system 50 function as running state detection means, and the rudder angle sensor 5 and accelerator pedal. Stroke sensor 55, brake pedal stroke sensor 60, and blinker switch 65 function as driving operation detection means, and controllers 100, 200, 250, 300, 350, 400, 500 function as driving diagnosis means and information setting means, and a speaker. 130 and the display unit 180 can function as information providing means. Note that the traveling state detection unit and the driving operation detection unit are not limited to these, and for example, another type of radar can be used instead of the laser radar 10. Further, only the speaker 130 or the display unit 180 can be used as the information providing means. The above description is merely an example, and when interpreting the invention, there is no limitation or restriction on the correspondence between the items described in the above embodiment and the items described in the claims.

5: rudder angle sensor, 10: laser radar, 15: front camera, 30: vehicle speed sensor, 35: acceleration sensor, 50: navigation system, 55: accelerator pedal opening sensor, 60: brake pedal stroke sensor, 65: winker switch , 100, 200, 250, 300, 350, 400, 500: controller, 130: speaker, 180: display unit

Claims (9)

Traveling state detecting means for detecting the traveling state of the vehicle;
Driving operation detection means for detecting a driving operation by the driver;
A parameter representing the driver's safety awareness / driving preference characteristics as an indicator for driving diagnosis based on the driving state detected by the driving state detecting unit and the driving operation detected by the driving operation detecting unit. Driving diagnosis means for detecting the distribution of the driver and diagnosing the driving of the driver from the detected index;
The result of driving diagnosis by the driving diagnosis means is evaluated according to an evaluation criterion including a first evaluation criterion based on the driving state of the driver of the day and a second evaluation criterion based on the normal driving state of the driver. Information setting means for setting the content of information provision to the driver by evaluating;
A vehicle driving support apparatus comprising: information providing means for providing information to the driver with the contents set by the information setting means. Traveling state detecting means for detecting the traveling state of the vehicle;
Driving operation detection means for detecting a driving operation by the driver;
Based on the driving state detected by the driving state detection unit and the driving operation detected by the driving operation detection unit, the smoothness and / or stability of the driving operation is detected as an index for driving diagnosis. Driving diagnostic means for diagnosing the driving of the driver from the detected index;
The result of driving diagnosis by the driving diagnosis means is evaluated according to an evaluation criterion including a first evaluation criterion based on the driving state of the driver of the day and a second evaluation criterion based on the normal driving state of the driver. Information setting means for setting the content of information provision to the driver by evaluating;
A vehicle driving support apparatus comprising: information providing means for providing information to the driver with the contents set by the information setting means . In the vehicle driving support device according to claim 1 or 2 ,
The driving diagnosis means performs driving diagnosis using a single index,
The information setting means compares the driving diagnosis result using the single index with the evaluation criteria and sets the content of the information provision. In the vehicle driving support device according to any one of claims 1 to 3 ,
The evaluation criteria vehicular driving support apparatus further comprising a third evaluation criteria relative to the operating condition of a general driver. In the vehicle driving assistance device according to any one of claims 1 to 4 ,
The information setting unit, the evaluating the drive diagnosis result in accordance with the two criteria, the evaluation result in accordance with the information provided in the vehicular driving support apparatus characterized by setting the alarm or operating improvements suggested as content . The vehicle driving support device according to claim 5 ,
The information setting means sets an alarm when the driving diagnosis result falls below a first evaluation reference value based on the evaluation criterion, and driving when the driving diagnosis result exceeds a second evaluation reference value based on the evaluation criterion. A driving support apparatus for a vehicle characterized by setting an improvement suggestion. The vehicle driving support device according to claim 1 ,
The driving diagnosis means, as a parameter distribution representing the driver's safety awareness and driving preference characteristics , distribution of own vehicle speed when there is no preceding vehicle, between the own vehicle and the preceding vehicle following the preceding vehicle A vehicle driving support device that detects either the inter-vehicle time distribution or the acceleration distribution of the host vehicle when starting from a stopped state. Detects the running state of the vehicle,
Detects driving operation by the driver,
Based on the detected driving state and the driving operation, a parameter distribution representing the driver's safety awareness / driving preference characteristics is detected as an index for driving diagnosis, and the driver's driving is detected from the detected index. Diagnose and
By evaluating the driving diagnosis result according to an evaluation criterion including a first evaluation criterion based on the driving state of the driver of the day and a second evaluation criterion based on the normal driving state of the driver , Set the content of information provision to the driver,
A vehicle driving support method, wherein information is provided to the driver with the set contents. Detects the running state of the vehicle,
Detects driving operation by the driver,
Based on the detected driving state and the driving operation, as an index for driving diagnosis, the smoothness and / or stability of the driving operation is detected, and the driving of the driver is diagnosed from the detected index,
By evaluating the driving diagnosis result according to an evaluation criterion including a first evaluation criterion based on the driving state of the driver of the day and a second evaluation criterion based on the normal driving state of the driver, Set the content of information provision to the driver,
A vehicle driving support method , wherein information is provided to the driver with the set contents .