JP6008049B2 - Vehicle information providing device - Google Patents

Description

  The present invention relates to a vehicle information providing apparatus.

Conventionally, as a vehicle information providing apparatus, for example, there is a technique described in Patent Document 1.
In the technique described in Patent Document 1, based on the steering angle of the steering wheel, a travel state distribution having a relatively long time range corresponding to a normal steering characteristic and a relatively temporal time corresponding to the current steering characteristic. A travel state distribution with a short range is calculated. And a driver | operator's driving state is estimated based on the difference amount between distribution of two calculated driving state distributions. That is, the driving state distribution, that is, the driver's steering characteristic is statistically learned, and the driving state of the driver is estimated based on the learned steering characteristic. Thereby, in the technique described in Patent Document 1, it is possible to accurately detect an unstable driving state regardless of a difference in traffic environment.

JP 2009-9495 A

However, in the technique described in Patent Document 1, the steering characteristics of the driver are statistically learned, and the driving state of the driver is estimated based on the learned steering characteristics. Therefore, for example, if the learning of the steering characteristics, that is, the calculation of the running state distribution is not properly performed, there is a possibility that the detection accuracy of the driving state of the driver is lowered.
The present invention focuses on the above points, and an object of the present invention is to improve the estimation accuracy of the driving state.

In order to solve the above problems, in one aspect of the present invention, the traveling state distribution calculating unit calculates a plurality of traveling state distributions having different time ranges based on the traveling state data. Subsequently, the driving state of the driver is estimated based on the plurality of calculated driving state distributions. Subsequently, presentation information is presented to the driver based on the estimated driving state. At that time, the traveling state distribution calculated by the traveling state distribution calculating unit is changed based on information related to traveling of the vehicle. Specifically, a first traveling state distribution having a relatively long temporal range and a second traveling state distribution having a shorter temporal range than the first traveling state distribution are calculated based on the traveling state data. To do. And if it is determined that the driving situation is disturbing with respect to the estimation of the driving state, the driving that is disturbing with respect to the estimation of the driving state when the elapsed time from the start of traveling of the vehicle is less than the set time. When the second running state distribution calculated before determining that the current state is in the situation is selected and the elapsed time is equal to or longer than the set time, the calculated second running state distribution is selected and the selected second running state distribution is selected. The first traveling state distribution is replaced with the traveling state distribution.

  According to one aspect of the present invention, the traveling state distribution calculated by the traveling state distribution calculating unit is changed based on information related to traveling of the vehicle. As a result, the running state distribution can be calculated more appropriately, and the driving state estimation accuracy can be improved.

It is a figure showing the structure of the vehicle carrying the vehicle information provision apparatus. It is a block diagram showing the system configuration example of the information provision apparatus for vehicles. It is a block diagram showing the structure of the driving assistance part 100A. 4 is a block diagram illustrating a configuration of a travel state distribution calculation unit 130. FIG. It is a flowchart showing a driving | running instability degree determination process. It is a figure for demonstrating the example of information presentation ON. It is a flowchart showing the example of calculation of a 1st driving state distribution and a 2nd driving state distribution. It is a figure for demonstrating the symbol used for relative entropy RHp calculation. It is a figure for demonstrating the calculation method of a 1st driving state distribution and a 2nd driving state distribution. It is a figure for demonstrating the calculation method of a 1st driving state distribution and a 2nd driving state distribution. It is a figure showing the range of the prediction error division bi. 3 is a graph showing a control map M. It is a graph showing the sensitivity of an alarm. It is a figure showing the structure of the vehicle carrying the vehicle information provision apparatus. It is a flowchart showing a driving | running instability degree determination process. It is a figure showing the structure of the vehicle carrying the vehicle information provision apparatus.

(First embodiment)
First, a first embodiment according to the present invention will be described with reference to the drawings.
(Constitution)
FIG. 1 is a diagram illustrating a configuration of a vehicle on which the vehicle information providing apparatus according to the present embodiment is mounted.
As shown in FIG. 1, the vehicle includes an accelerator pedal opening amount sensor 1, a brake pedal operation amount sensor 2, a steering angle sensor 3, a wheel speed sensor 4, a blinker detection sensor 5, and a navigation device 6. The vehicle also includes a G sensor 7, a shift sensor 8, a forward vehicle detection device 9, and a controller 100.

The accelerator pedal opening amount sensor 1 detects the opening amount of the accelerator pedal. Then, the accelerator pedal opening amount sensor 1 outputs the detected opening amount to the controller 100.
The brake pedal operation amount sensor 2 detects the operation amount of the brake pedal. Then, the brake pedal operation amount sensor 2 outputs the detected operation amount to the controller 100.
The steering angle sensor 3 detects the steering angle of a steering wheel (not shown). Then, the steering angle sensor 3 outputs the detected steering angle to the controller 100. As the steering angle sensor 3, for example, an angle sensor that detects the rotation angle of the steering column can be employed.

The wheel speed sensor 4 detects the rotation speed of the wheel (hereinafter also referred to as “wheel speed”). Subsequently, the wheel speed sensor 4 calculates the vehicle speed based on the detected wheel speed. The wheel speed sensor 4 outputs the detected wheel speed and the calculated vehicle speed to the controller 100.
The turn signal detection sensor 5 detects an operation state of a turn signal lever (not shown) (hereinafter also referred to as “turn signal operation”). As the blinker operation, for example, there is an operation. Then, the turn signal detection sensor 5 outputs the detected turn signal operation to the controller 100.

The shift sensor 8 detects an operation state (hereinafter also referred to as “shift operation”) of a shift lever (not shown). Examples of the shift operation include the positions of shift levers such as P, D, and R. Then, the shift sensor 8 outputs the detected shift operation to the controller 100.
The information presentation device presents an alarm and other information to the driver according to a control signal (described later) output by the controller 100. As a presentation method, there are sound and image. As the information presentation device, for example, a speaker 10 that provides information to the driver by a buzzer sound or voice, and a display unit that provides information to the driver by displaying an image or text can be employed. As the display unit, for example, the display monitor of the navigation device 6 may be used.

The navigation device 6 includes a GPS (Global Positioning System) receiver, a map database, and a display monitor. And the navigation apparatus 6 acquires the present position and road information of a vehicle from a GPS receiver and a map database. Subsequently, the navigation device 6 acquires various types of information such as the type of road on which the vehicle travels and the road width based on the acquired current position of the vehicle and road information. Subsequently, the navigation device 6 displays a route search result, a route guidance result, and the like on the display monitor based on the acquired information.
The G sensor 7 detects longitudinal acceleration and lateral acceleration generated in the vehicle. Then, the G sensor 7 outputs the detected longitudinal acceleration and lateral acceleration to the controller 100.
The forward vehicle detection device 9 detects information (for example, a distance to the obstacle) of other vehicles and other obstacles existing in the forward direction of the vehicle. Then, the forward vehicle detection device 9 outputs the detected information to the controller 100. As the forward vehicle detection device 9, for example, a laser distance meter that emits laser light forward of the vehicle in the traveling direction and detects reflected light can be employed.

  The controller 100 includes a CPU (Central Processing Unit) and CPU peripheral components such as a ROM (Read Only Memory), a RAM (Random Access Memory), and an A / D (Analog to Digital) conversion circuit. The controller 100 (CPU, CPU peripheral components) includes a driving support unit 100A that performs driving instability determination processing. In the driving instability determination process, the driving support unit 100A determines the operation state of the driving operator that can be operated by the driver based on the detection results output by the accelerator pedal opening amount sensor 1, the brake pedal operation amount sensor 2, and the like. And travel state data including at least one of the vehicle states. Examples of the driving operator include a steering wheel, an accelerator pedal, and a brake pedal. As the vehicle state, there is inter-vehicle information for the preceding vehicle. In the present embodiment, information on the steering angle output by the steering angle sensor 3 (hereinafter also referred to as “steering angle information”) is adopted as the traveling state data.

  Subsequently, the driving support unit 100A has a plurality of travel state distributions (first travel state distribution (described later), second travel state distribution (described later) having different time ranges based on the acquired travel state data (steering angle information). ) Is calculated. Subsequently, the driving support unit 100A determines the driver's driving based on a difference amount (relative entropy RHp (described later)) between the calculated distributions of the plurality of driving state distributions (first driving state distribution, second driving state distribution). Estimate the state (driving instability (described later)). Then, the driving support unit 100A outputs a control signal for causing the driver to present an alarm or other information (hereinafter also referred to as “presentation information”) based on the estimated driving state (driving instability). To do. Thereby, the driving support unit 100A presents the presentation information to the driver and alerts the driver about the degree of driving instability (driving instability state).

  In addition, as driving state data, you may employ | adopt the acceleration / deceleration information based on operation of an accelerator pedal or a brake pedal, distance information (vehicle distance, distance between vehicles) with respect to a preceding vehicle. When using inter-vehicle information (inter-vehicle distance, inter-vehicle time), acceleration / deceleration information, etc., the travel state distribution (first travel state distribution, second travel state distribution) and the amount of difference (relative entropy RHp) between the distributions are calculated. For example, it may be calculated by a known method as described in, for example, the publication of International Publication No. WO2009 / 013815 (Japanese Patent Application No. 2009-524342).

FIG. 2 is a block diagram illustrating a system configuration example of the vehicle information providing apparatus according to the present embodiment.
As shown in FIG. 2, in this embodiment, a visual information presentation device and an auditory information presentation device are illustrated as information presentation devices. Moreover, as a visual information presentation apparatus, the display monitor of the navigation apparatus 6 and the speaker 10 are illustrated as an auditory information presentation apparatus.
FIG. 3 is a block diagram showing the configuration of the driving support unit 100A of the present embodiment.
As illustrated in FIG. 3, the driving support unit 100A includes a driving state data acquisition unit 110, a driving state determination unit 120, a driving state distribution calculation unit 130, a driving instability determination unit 140, and an information presentation unit 150.
The traveling state data acquisition unit 110 acquires the detection result output from the steering angle sensor 3. Then, the traveling state data acquisition unit 110 sets the acquired detection result as traveling state data.

  The driving status determination unit 120 is configured to detect the current driving status (disturbance driving status (described later), normal driving status (described later) based on the detection results output from the accelerator pedal opening amount sensor 1, the brake pedal operation amount sensor 2, and the like. )) Is determined. Specifically, the driving state determination unit 120 determines the operating state and travel of the driving operator that can be operated by the driver based on the detection results output by the accelerator pedal opening amount sensor 1, the brake pedal operation amount sensor 2, and the like. Detect the environment. Subsequently, the driving state determination unit 120 determines whether the detected operation state of the driving operator is the set operation state or whether the travel environment is the set travel environment.

  Examples of the setting operation state include curve operation, lane change operation, vehicle acceleration / deceleration, accelerator pedal (not shown) operation, brake pedal (not shown) operation, turn signal operation, navigation device 6 operation, and audio device. There is an operation (not shown). The curve operation is detected based on the detection result of the steering angle sensor 3, for example. Specifically, the curve operation is determined to be a curve operation when a state equal to or greater than the set steering angle continues for a set time or longer. The lane change operation is detected based on, for example, a turn signal operation, a lane position, a host vehicle position, a traveling direction of the host vehicle, and the like. The acceleration / deceleration of the vehicle is detected based on the detection result of the G sensor 7, for example. The operation of the accelerator pedal is detected based on the detection result of the accelerator pedal opening amount sensor 1, for example. The operation of the brake pedal is detected based on the detection result of the brake pedal operation amount sensor 2, for example. The winker operation is detected based on the detection result of the winker detection sensor 5. The operation of the navigation device 6 is detected based on a signal from the navigation device 6. An operation of an audio device (not shown) is detected based on a signal from the audio device (not shown).

  Examples of the set travel environment include irregular roads, swells, tunnels, branching junctions, toll gates, and slopes. The irregular road and the undulation are detected based on the detection result of the wheel speed sensor 4, for example. Specifically, for the irregular road, the difference between the current value and the previous value of the wheel speed measured at each set time is calculated, and it is determined that there is an irregular road when the calculated difference is equal to or greater than the set threshold value. The tunnel, the junction, and the toll gate are detected based on a signal from the navigation device 6. The slope is detected based on the detection result of the G sensor 7. In addition, when the driving state determination unit 120 determines that the operation state of the driving operator is the set operation state, or determines that the driving environment is the set driving environment, the driving state determination unit 120 determines the driving instability. On the other hand, it is determined that the driving situation is a disturbance (hereinafter also referred to as “disturbance driving situation”). On the other hand, when the driving state determination unit 120 determines that neither the setting operation state nor the setting traveling environment is present, the driving state determination unit 120 is not in a driving state that causes disturbance to the determination of driving instability (hereinafter, “normal” It is also called “driving situation”.

  The traveling state distribution calculation unit 130 has a plurality of traveling state distributions (first traveling state distribution, second traveling state distribution) having different time ranges based on the traveling state data (steering angle information) acquired by the traveling state data acquisition unit 110. ) Is calculated. Specifically, the traveling state distribution calculation unit 130 is based on the traveling state data acquired by the traveling state data acquisition unit 110, and is calculated based on the steering angle information acquired in a predetermined relatively long time range. A state distribution and a second traveling state distribution obtained from the steering angle information acquired in a shorter time range than the first traveling state distribution are calculated.

  Here, the predetermined relatively long time range (time range of the first running state distribution) is a time range in which normal steering characteristics can be acquired. For example, when a predetermined set time (for example, 2160 seconds) has not elapsed since the start of the trip, the time range from the present to 180 seconds before is set as the time range of the first running state distribution. Trips include, for example, a period from when an ignition switch (not shown) is turned on to when it is turned off, or a period from when the navigation device 6 starts route guidance to when it ends. On the other hand, when the set time (2160 seconds) has elapsed since the start of the trip, the time range from the present to the set time (2160 seconds) before is set as the time range of the first travel state distribution.

  In addition, the time range shorter than the first travel state distribution (the time range of the second travel state distribution) is a time range in which the current steering characteristics (the latest driving characteristics) can be determined. For example, when the set time (2160 seconds) has not elapsed since the start of the trip, the time range from the present to 120 seconds before is set as the time range of the second running state distribution. On the other hand, when the set time (2160 seconds) has elapsed since the start of the trip, the time range from the present to 90 seconds before is set as the time range of the second running state distribution. The first traveling state distribution and the second traveling state distribution are calculated every time the traveling state data acquisition unit 110 acquires the traveling state data (steering angle information). An example of calculating the first traveling state distribution and the second traveling state distribution will be described later.

FIG. 4 is a block diagram illustrating a configuration of the traveling state distribution calculation unit 130 of the present embodiment.
As shown in FIG. 4, the traveling state distribution calculation unit 130 includes a first traveling state distribution calculation unit 130A, a second traveling state distribution calculation unit 130B, a distribution accumulation unit 130C, a distribution selection unit 130D, and a distribution setting unit 130E. .
The first traveling state distribution calculation unit 130A calculates a first traveling state distribution.
The second traveling state distribution calculation unit 130B calculates a second traveling state distribution.
The distribution accumulating unit 130C acquires the second traveling state distribution calculated by the second traveling state distribution calculating unit 130B. Then, the distribution accumulation unit 130C accumulates the acquired second traveling state distribution.

The distribution selection unit 130D changes the traveling state distribution (first traveling state distribution) calculated by the first traveling state distribution calculating unit 130A based on information related to traveling of the vehicle. As information regarding the traveling of the vehicle, for example, there is an elapsed time from the start of the trip. Specifically, the distribution selection unit 130D determines whether or not the driving situation determination unit 120 has detected a disturbance driving situation. Then, if the distribution selection unit 130D determines that the driving situation determination unit 120 has detected the disturbance driving situation, whether or not the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds). Determine. As the set time, for example, a time range of the first running state distribution can be adopted. When the distribution selection unit 130D determines that the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds), the distribution selection unit 130D determines from the second running state distribution accumulated in the distribution accumulation unit 130C. The second running state distribution (previous second running state distribution) calculated and accumulated before the detection of the disturbance driving situation is selected.
On the other hand, when the distribution selection unit 130D determines that the elapsed time from the start of the trip is equal to or longer than a set time (for example, 2160 seconds), the current second traveling state distribution detected by the second traveling state distribution calculating unit 130B. Select. Thereby, the distribution setting unit 130E replaces (changes) the first traveling state distribution calculated by the first traveling state distribution calculating unit 130A with the selected second traveling state distribution, as will be described later.

The distribution setting unit 130E determines whether or not the distribution selection unit 130D has selected the second traveling state distribution. Then, when the distribution setting unit 130E determines that the distribution selection unit 130D has selected the second traveling state distribution, the first traveling state distribution calculating unit 130A calculates the selected second traveling state distribution. A state distribution (replaces the first running state distribution).
The driving instability determination unit 140 includes the first traveling state distribution calculated by the traveling state distribution calculating unit 130 (the first traveling state distribution after replacement when replaced with the second traveling state distribution) and the second traveling state. Based on the distribution, the driving state (driving instability) of the driver is estimated.
The information presenting unit 150 presents presentation information to the driver based on the driving state (driving instability) of the driver estimated by the driving instability determining unit 140 (hereinafter also referred to as “information presenting process”). )I do. In the information presenting process, the information presenting unit 150 outputs a control signal for presenting the presenting information (alarm or other information presented to the driver) to the information presenting device.

(Operation instability judgment processing)
Next, the driving instability determination process executed by the driving support unit 100A will be described. The driving instability determination process is performed at a preset control cycle (for example, every 100 milliseconds).
FIG. 5 is a flowchart showing the driving instability degree determination process.
As shown in FIG. 5, first, in step S <b> 101, the driving support unit 100 </ b> A (the driving state data acquisition unit 110 and the driving state determination unit 120) acquires vehicle information. The vehicle information includes, for example, travel state data (steering angle information) and operation state information of the driving operator.
Then, it transfers to step S102 and 100 A of driving assistance parts (driving condition determination part 120) acquires traffic environment information. As the traffic environment information, for example, there is information on a travel environment.

  Subsequently, the process proceeds to step S103, and the driving support unit 100A (driving state determination unit 120) determines the current driving state of the vehicle (based on the vehicle information acquired in step S101 and the traffic environment information acquired in step S102). Disturbance driving situation, normal driving situation). Specifically, the driving support unit 100A (driving condition determination unit 120) determines that the operation state or traveling environment of the driving operator is a curve based on the vehicle information acquired in step S101 and the traffic environment information acquired in step S102. Operation, lane change operation, acceleration / deceleration of vehicle, operation of accelerator pedal (not shown), operation of brake pedal (not shown), turn signal operation, operation of navigation device 6, operation of audio device (not shown), rough road, It is determined whether it corresponds to at least one of swell, tunnel, merging / merging, toll gate, and slope. And driving support part 100A (driving condition judging part 120) judges that the current driving condition of a vehicle is in disturbance driving condition, when it judges with corresponding to at least one. On the other hand, if the driving support unit 100A (driving condition determination unit 120) determines that none of these applies, it determines that the current driving condition of the vehicle is in the normal driving condition.

Next, the process proceeds to step S104, and the driving support unit 100A (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) calculates the first traveling state distribution and the second traveling state distribution. Subsequently, the driving support unit 100A (distribution accumulation unit 130C) accumulates (stores) the calculated second traveling state distribution in the distribution accumulation unit 130C.
Subsequently, the process proceeds to step S105, and the driving support unit 100A (distribution selection unit 130D) corresponds to either the normal driving state or the disturbance driving state based on the determination result of step S103. It is determined whether. If the driving support unit 100A (distribution selecting unit 130D) determines that the normal driving situation is satisfied, the driving supporting unit 100A proceeds to step S106. On the other hand, when it is determined that the driving support unit 100A (distribution selection unit 130D) corresponds to the disturbance driving situation, it is determined whether or not the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds). judge. If the distribution selection unit 130D determines that the elapsed time from the start of the trip is less than the set time (2160 seconds), the process proceeds to step S107. On the other hand, when the distribution selection unit 130D determines that the elapsed time from the start of the trip is equal to or longer than the set time (2160 seconds), the process proceeds to step S108.

In step S106, the driving support unit 100A (distribution setting unit 130E) does not replace the first traveling state distribution calculated in step S104 with the second traveling state distribution (reset (described later), without performing restoration (described later)). ), The process proceeds to step S109.
On the other hand, in step S107, the driving support unit 100A (distribution setting unit 130E) replaces the first traveling state distribution calculated in step S104 with the second traveling state distribution calculated in step S104 (hereinafter, “reset”). Also called), the process proceeds to step S109.
On the other hand, in step S108, the driving support unit 100A (distribution setting unit 130E) uses the first driving state distribution calculated in step S104 as the disturbance driving state among the second driving state distributions stored in the distribution storage unit 130C. After replacing with the second running state distribution calculated and accumulated before the time of detection (hereinafter also referred to as “restore”), the process proceeds to step S109.

  In step S109, the driving support unit 100A (driving instability determination unit 140) uses the steering entropy method to calculate the first traveling state distribution calculated in step S104 (if replaced with the second traveling state distribution) A difference amount (relative entropy RHp) between the distributions of the first travel state distribution and the second travel state distribution is calculated. An example of calculating the difference between the distributions (relative entropy RHp) will be described later. Accordingly, the driving support unit 100A (driving instability determination unit 140) determines how the current driving operation of the driver is different from the normal driving operation based on the first driving state distribution and the second driving state distribution. That is, a difference amount (relative entropy RHp) for determining whether or not the state is unstable compared to the normal driving operation is calculated. That is, the driving support unit 100A (driving instability determination unit 140) calculates the relative entropy RHp as a value that represents a non-smooth disorder of the driving operation. 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 accumulates. Therefore, the corrected steering amount tends to increase when the driver's attention returns to driving.

  Subsequently, the process proceeds to step S110, and the driving support unit 100A (driving instability determination unit 140) estimates the driving state of the driver based on the difference between the distributions (relative entropy RHp) calculated in step S109. (Determines whether the driving state of the driver is in an unstable state). Specifically, the driving support unit 100A (driving instability determination unit 140) determines whether or not the difference (relative entropy RHp) between the distributions calculated in step S109 is larger than a predetermined determination threshold. . When the driving support unit 100A (driving instability determination unit 140) determines that the difference between the distributions (relative entropy RHp) is larger than the determination threshold, the driving state of the driver becomes unstable. Judge that there is. On the other hand, if the driving support unit 100A (driving instability determination unit 140) determines that the difference between the distributions (relative entropy RHp) is equal to or less than the determination threshold, the driving state of the driver becomes unstable. Judge that there is no.

Subsequently, the process proceeds to step S111, and the driving support unit 100A (information presenting unit 150) presents the presenting information (alarm or other information presented to the driver) to the driver based on the driving state estimated in step S110. Processing (information presentation processing) is performed. Specifically, driving support unit 100A (information presenting unit 150) determines whether or not the state determined to be unstable in step S110 has continued for a predetermined instability determination threshold (for example, 5 seconds) or more. . When the driving support unit 100A (information presenting unit 150) determines that the state determined to be an unstable state continues for an unstable determination threshold (for example, 5 seconds) or longer, the driving support unit 100A performs an information presentation process. On the other hand, the driving support unit 100A (information presenting unit 150) does not perform the information presenting process when it is determined that the state determined to be an unstable state has not continued for an unstable determination threshold (for example, 5 seconds). .
An example of the information presentation process is shown in FIG. In this example, a warning is displayed, and a warning is presented with a voice such as “Why don't you take a break soon?”

(Example of calculating the first running state distribution and the second running state distribution)
Next, calculation examples of the first traveling state distribution and the second traveling state distribution will be described.
FIG. 7 is a flowchart illustrating a calculation example of the first traveling state distribution and the second traveling state distribution.
As shown in FIG. 7, first, in step S201, the traveling state distribution calculation unit 130 (the first traveling state distribution calculation unit 130A and the second traveling state distribution calculation unit 130B) is a traveling scene in which the relative entropy RHp can be calculated. It is determined whether or not there is. Specifically, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) has a vehicle speed within a predetermined vehicle speed range (for example, 40 to 120 km / h). It is determined whether or not there is. When the traveling state distribution calculating unit 130 determines that the vehicle speed is within a predetermined vehicle speed range (40 km / h to 120 km / h) (Yes), the traveling state distribution calculating unit 130 is a traveling scene in which the relative entropy RHp can be calculated. And the process proceeds to step S202.

  On the other hand, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) determines that the vehicle speed is outside a predetermined vehicle speed range (40 km / h to 120 km / h). If (No), it is determined that it is not a traveling scene in which the relative entropy RHp can be calculated, and this calculation process is terminated. As a result, the traveling state distribution calculation unit 130 (the first traveling state distribution calculation unit 130A and the second traveling state distribution calculation unit 130B) is used when the vehicle speed is extremely low and extremely high (less than 40 km / h, or 120 km / h or higher) is excluded from the driving scenes where the relative entropy RHp can be calculated.

  In step S202, the traveling state distribution calculation unit 130 (the first traveling state distribution calculation unit 130A and the second traveling state distribution calculation unit 130B) acquires the steering angle θ output from the steering angle sensor 3. Subsequently, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) calculates a steering angle prediction error θe based on the acquired steering angle θ. Here, FIG. 8 shows special symbols used for calculating the relative entropy RHp and names of the special symbols. The steering angle smooth value θn-tilde is the steering angle θ in which the influence of quantization noise is reduced. Further, the estimated value θn-hat of the steering angle is a value obtained by estimating the steering angle θ at the sampling time on the assumption that the steering wheel is operated smoothly. The steering angle estimated value θn-hat is obtained by performing a second-order Taylor expansion on the steering angle smooth value θn-tilde, as shown in (Equation 1) below.

In (Expression 1), tn is the sampling time of the steering angle θn.
The steering angle smooth value θn-tilde is calculated from the following (Equation 2) as an average value of three adjacent steering angles θn in order to reduce the influence of quantization noise.

In (Expression 2), l is within 150 milliseconds when the calculation time interval of the steering angle smooth value θn-tilde is 150 milliseconds, that is, the minimum time interval that can be intermittently operated by a human in manual operation. Represents the number of samples of the steering angle θn included in.
When the sampling interval of the steering angle θn is Ts, the number of samples 1 is expressed by the following (Equation 3).
l = round (0.15 / Ts) (Expression 3)
In (Equation 3), k = 1, 2, and 3 are taken, and the smooth value θn− is calculated based on the steering angle at intervals of 150 milliseconds and three adjacent steering angles θn by (k * 1). You can ask for tilde. Therefore, the estimated value θn-hat calculated based on the smooth value θn-tilde is calculated based on the steering angle θ obtained substantially at intervals of 150 milliseconds.
The steering angle prediction error θe at the sampling time is expressed as the difference between the estimated steering angle θn-hat and the actual steering angle θn at the sampling time when the steering wheel is operated smoothly. It can be calculated.

However, the steering angle prediction error θe is calculated only for the minimum time interval that can be intermittently operated by a human in manual operation, that is, the steering angle θn every 150 milliseconds.
A specific method for calculating the steering angle prediction error θe will be described below. Note that the sampling interval Ts of the steering angle θ is, for example, 50 milliseconds. First, using the three adjacent steering angles θn at intervals of 150 milliseconds, the three steering angle smooth values θn-tilde are calculated from the above (Equation 2). The three steering angle smooth values θn-tilde are expressed by the following (formula 5).

  Next, an estimated value θn-hat of the steering angle is calculated from the above (Equation 1) using the calculated three steering angle smooth values θn-tilde. The estimated value θn-hat is expressed by the following (formula 6).

Then, using the calculated steering angle estimated value θn-hat and the actual steering angle θn, the steering angle prediction error θe is calculated from the above (Equation 4).
Subsequently, the process proceeds to step S203, where the travel state distribution calculation unit 130 (first travel state distribution calculation unit 130A, second travel state distribution calculation unit 130B) calculates up to the present time and stores it in the memory of the controller 100. The steering angle prediction error θe for a set time T seconds (for example, 2160 seconds) is updated by adding the current value of the steering angle prediction error θe calculated in step S202. That is, the travel state distribution calculation unit 130 (the first travel state distribution calculation unit 130A and the second travel state distribution calculation unit 130B) has the oldest T seconds (ie, the steering angle prediction error θe stored in the memory of the controller 100). 2160 seconds), the previous data is discarded, and the current value of the steering angle prediction error θe calculated in step S202 is accumulated as the latest steering angle prediction error θe instead. As a result, the traveling state distribution calculating unit 130 (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) stores the steering angle from the current value to T seconds (2160 seconds before) in the memory of the controller 100. Accumulate the prediction error θe.

9 and 10 are diagrams for explaining a calculation method of the first traveling state distribution and the second traveling state distribution. FIG. 11 is a diagram illustrating the range of the prediction error classification bi.
Subsequently, the process proceeds to step S204, where the traveling state distribution calculation unit 130 (first traveling state distribution calculation unit 130A) calculates the first traveling state distribution based on the steering angle prediction error θe accumulated in the memory of the controller 100. calculate. Specifically, the traveling state distribution calculation unit 130 (first traveling state distribution calculation unit 130A), as shown in FIGS. 9, 10, and 11, steer angle prediction error θe accumulated in the memory of the controller 100. Among these, the steering angle prediction for 2160 seconds from the set time T seconds (for example, 2160 seconds) before (when the set time (2160 seconds) has not elapsed since the start of the trip, 180 seconds before) to the present time The error θe is classified into nine prediction error categories bi (= b1, b2, b3, b4, b5, b6, b7, b8, b9).

  The range of the prediction error category bi is set based on the α value used for calculating the steering entropy. As the α value, for example, the steering angle prediction error θe within a predetermined time based on the time series data of the steering angle θ, that is, the estimated steering angle θn-hat when the steering wheel is operated smoothly and the actual value The difference between the steering angle θn and the distribution (variation) of the steering angle prediction error θe is measured to calculate a 90 percent tile value (range of distribution including 90% of the steering angle prediction error θe). That is, the α value is set so that 90% of the steering angle prediction error θe is included in the section [−α, α].

  Specifically, the prediction error section b1 is less than 5α, the prediction error section b2 is −5α or more and less than −2.5α, the prediction error section b3 is −2.5α or more and less than −α, and the prediction error The segment b4 is greater than or equal to -α and less than -0.5α, and the prediction error segment b5 is greater than or equal to -0.5α and less than 0.5α. The prediction error category b6 is 0.5α or more and less than α, the prediction error category b7 is greater than or equal to α and less than 2.5α, the prediction error category b8 is greater than or equal to 2.5α and less than 5α, and the prediction error category b9 is 5α or more. The same prediction error classification bi (= b1 to b9) is used for the first running state distribution and the second running state distribution. Subsequently, the traveling state distribution calculating unit 130 (first traveling state distribution calculating unit 130A) has probabilities pi (= p1, p2, p3, frequency) of the frequencies of the steering angle prediction error θe included in each prediction error category bi. p4, p5, p6, p7, p8, p9) are obtained.

Subsequently, the process proceeds to step S205, where the traveling state distribution calculation unit 130 (second traveling state distribution calculation unit 130B) calculates the second traveling state distribution based on the steering angle prediction error θe accumulated in the memory of the controller 100. After the calculation, this calculation process is terminated. Specifically, the travel state distribution calculation unit 130 (second travel state distribution calculation unit 130B) is the latest 90 seconds before the current trip time (from the trip angle prediction error θe stored in the memory of the controller 100). When the set time (2160 seconds) has not elapsed since the start, the steering angle prediction error θe up to 120 seconds before) is classified into nine prediction error categories bi (= b1 to b9). Subsequently, the traveling state distribution calculation unit 130 (second traveling state distribution calculation unit 130B) has probabilities q i (= q1, q2, q3, frequency) of the frequencies of the steering angle prediction error θe included in each prediction error category bi. q4, q5, q6, q7, q8, q9).
(Example of calculating relative entropy RHp)
An example of calculating the relative entropy RHp will be described.
The relative entropy RHp is calculated from the following (Equation 7) based on the probability pi calculated in step S204 and the probability qi calculated in step S205.

  From the above (Equation 7), the relative entropy RHp becomes 0 when the probability pi (= p1 to p9) of the first running state distribution and the probability qi (= q1 to q9) of the second running state distribution are equal. . On the other hand, when the probability pi of the first running state distribution and the probability qi of the second running state distribution are deviated, the relative entropy RHp increases as the deviation increases.

(Operation other)
Next, the operation of the vehicle equipped with the vehicle information providing apparatus of this embodiment will be described.
It is assumed that the driver turns on an ignition switch (not shown) and starts a trip. It is assumed that the vehicle enters the curve when the elapsed time from the start of the trip is less than a set time (for example, 2160 seconds). Then, the driving support unit 100A (driving condition determination unit 120) determines that the curve operation has been performed, and determines that the current driving condition of the vehicle is a disturbance driving condition (step S103 in FIG. 5). Subsequently, the steering angle information acquired by the driving support unit 100A (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) in the first relatively long time range. The travel state distribution obtained in step 1) and the second travel state distribution (the travel state distribution acquired in a shorter time range than the first travel state distribution) are calculated (step S104 in FIG. 5). Subsequently, the driving support unit 100A (the first traveling state distribution calculating unit 130A and the second traveling state distribution calculating unit 130B) accumulates the calculated second traveling state distribution in the distribution accumulating unit 130C (step S104 in FIG. 5). .

  Subsequently, the driving support unit 100A (distribution selection unit 130D) determines that the current driving state of the vehicle is a disturbance driving state based on the determination result of step S103 (step S105 in FIG. 5). Subsequently, the driving support unit 100A (distribution selection unit 130D) determines that the elapsed time from the start of the trip is less than the set time (2160 seconds) (step S105 in FIG. 5). Subsequently, the driving support unit 100A (distribution setting unit 130E) replaces (resets) the first traveling state distribution calculated in step S104 with the second traveling state distribution calculated in step S104 (step S107 in FIG. 3). Thus, the driving support unit 100A equalizes the probability pi (= p1 to p9) of the first traveling state distribution and the probability qi (= q1 to q9) of the second traveling state distribution.

  Subsequently, the driving support unit 100A (driving instability determination unit 140) calculates a difference amount (relative entropy RHp = 0) between the replaced first traveling state distribution and second traveling state distribution (FIG. 5). Step S109 of 5). Subsequently, the driving support unit 100A (driving instability determination unit 140) determines that the relative entropy RHp (= 0) is equal to or less than the determination threshold value, and estimates the driving state (determines that there is no unstable state). Step S110 in FIG. Subsequently, the driving support unit 100A (information presenting unit 150) determines that the state determined to be in an unstable state in step S110 has not continued for an unstable determination threshold (for example, 5 seconds), and performs an information presentation process. No (step S110 in FIG. 5). Here, the driving support unit 100A calculates the first traveling state distribution and the second traveling state distribution based on the steering angle information (the traveling state data) (step S104 in FIG. 5). Therefore, the driving support unit 100A wants to acquire only the steering angle information (running state data) for determining the degree of driving instability (measuring the driving instability state).

  However, in the driving support unit 100A, the steering wheel operation may be disturbed in a driving situation (disturbance driving situation) in which a steering change or the like due to the operating state of the driving operator or the driving environment occurs. Therefore, when the driving support unit 100A uses the driving state distribution (first driving state distribution) using the steering angle information including the steering angle due to the disorder of the steering wheel operation, the detection accuracy of the unstable driving state is improved. It can get worse. On the other hand, when the driving support unit 100A of the present embodiment detects a driving situation (disturbing driving situation) that becomes a disturbance with respect to the unstable driving state, the elapsed time from the start of the trip is set time (for example, 2160 seconds), the first traveling state distribution based on the traveling state data when the disturbance driving state is detected is replaced with the second traveling state distribution (steps S105 and S107 in FIG. 5). As a result, the driving support unit 100A of the present embodiment can prevent erroneous detection of being in an unstable state in determining the degree of driving instability (measurement of an unstable driving state).

  Further, it is assumed that the elapsed time from the start of the trip becomes equal to or longer than a set time (for example, 2160 seconds) while traveling on the curve. Then, the driving support unit 100A (distribution selection unit 130D) determines that the elapsed time from the start of the trip is a set time (for example, 2160 seconds) or more (step S105 in FIG. 5). Subsequently, the driving support unit 100A (distribution setting unit 130E) detects the disturbance driving state from the second driving state distribution accumulated in the distribution accumulating unit 130C with the first driving state distribution calculated in step S104. Calculated and accumulated before the time and replaced (restored) with the second running state distribution (step S108 in FIG. 3).

  Subsequently, the driving support unit 100A (driving instability determination unit 140) calculates a difference amount (relative entropy RHp) between the replaced first traveling state distribution and second traveling state distribution (FIG. 5). Step S109). Subsequently, the driving support unit 100A (driving instability determination unit 140) estimates the driving state based on the relative entropy RHp (determines whether or not the driving state is unstable. Step S110 in FIG. 5). Subsequently, the driving support unit 100A (information presenting unit 150) determines whether or not the state determined to be an unstable state in step S110 continues for an unstable determination threshold (for example, 5 seconds), and the determination result is obtained. Information presentation processing is determined based on this (step S110 in FIG. 5). Here, when the driving support unit 100A of the present embodiment detects a driving situation (disturbing driving situation) that becomes a disturbance with respect to an unstable driving state, the elapsed time from the start of the trip is set to a set time (for example, 2160). When the disturbance driving situation is detected, the first running state distribution based on the running state data is calculated and accumulated before the disturbance driving situation is detected and replaced with the second running state distribution ( Steps S105 and S108 in FIG. As a result, the driving support unit 100A of the present embodiment can prevent erroneous detection of being in an unstable state in determining the degree of driving instability (measurement of an unstable driving state).

  In the present embodiment, the traveling state data acquisition unit 110 in FIG. 3 and step S101 in FIG. 5 constitute a traveling state data acquisition unit. Similarly, the traveling state distribution calculating unit 130 in FIG. 3, the first traveling state distribution calculating unit 130A in FIG. 4, the second traveling state distribution calculating unit 130B, and step S104 in FIG. 5 constitute the traveling state distribution calculating unit. 3, the distribution selection unit 130D and the distribution setting unit 130E in FIG. 4, and steps S105, S107, and S108 in FIG. 5 constitute a setting change unit. Further, the driving instability determination unit 140 in FIG. 3 and step S110 in FIG. 5 constitute the driving state estimation unit. Moreover, the information presentation part 150 of FIG. 3 and step S111 of FIG. 5 comprise an information presentation part. Further, the driving situation determination unit 120 in FIG. 3 and step S103 in FIG. 5 constitute the driving situation determination unit.

(Effect of this embodiment)
This embodiment has the following effects.
(1) The driving support unit 100A calculates a plurality of traveling state distributions (for example, a first traveling state distribution and a second traveling state distribution) having different time ranges based on traveling state data (for example, steering angle information). . Subsequently, the driving support unit 100A estimates the driving state of the driver based on the plurality of calculated driving state distributions (for example, the first driving state distribution and the second driving state distribution) (for example, in an unstable state). Whether or not). At that time, the driving support unit 100A changes the driving state distribution (for example, the first driving state distribution) calculated by the driving support unit 100A based on the information about the driving of the vehicle (for example, the elapsed time from the start of the trip). To do.
With such a configuration, the travel state distribution (for example, the first travel state distribution) calculated by the driving support unit 100A based on information related to the travel of the vehicle (for example, the elapsed time from the start of the trip) is changed. As a result, the running state distribution (for example, the first running state distribution) can be calculated more appropriately, and the estimation accuracy of the driving state (for example, whether or not the vehicle is in an unstable state) can be improved.

(2) The driving support unit 100A changes the driving state distribution (for example, the first driving state distribution) calculated by the driving support unit 100A based on the elapsed time from the start of the trip.
With such a configuration, for example, when the elapsed time from the start of the trip is short and the acquired traveling state data is small, a calculation method corresponding to the small traveling state data can be set.
(3) When the driving support unit 100A detects a driving situation (for example, a disturbance driving situation) that becomes a disturbance with respect to the estimation of the driving state (for example, measurement of an unstable driving state), the elapsed time from the start of the trip When a driving situation (for example, a disturbance driving situation) that causes a disturbance to the estimation of the driving state (for example, measurement of an unstable driving state) is detected when the time is less than a set time (for example, 2160 seconds). If the elapsed time from the start of the trip is equal to or longer than a set time (for example, 2160 seconds), the current second traveling state distribution is selected and selected. The first traveling state distribution is replaced with the second traveling state distribution.
With such a configuration, the estimation accuracy of the driving state can be improved more appropriately.

(Second Embodiment)
Next, a second embodiment according to the present invention will be described with reference to the drawings.
In addition, the same code | symbol is used about the structure similar to the said 1st Embodiment.
In the present embodiment, the driving support unit 100A (driving instability determination unit 140) determines that the driving state is a disturbance in response to the determination of driving instability (measurement of driving instability). calculating a ratio T _a / T _B between the cumulative value T _B of the elapsed time from the start of the cumulative value T _a and trip time, the driving support unit 100A is calculated based on the calculated ratio T _a / T _B The point which changes driving state distribution (1st driving state distribution, 2nd driving state distribution) differs from 1st Embodiment. Specifically, the present embodiment differs from the first embodiment in the content of step S109 in FIG.

In step S109, the driving support unit 100A (driving instability determination unit 140) uses the steering entropy method to calculate the first traveling state distribution calculated in step S104 (if replaced with the second traveling state distribution) A difference amount (relative entropy RHp) between the distributions of the first travel state distribution and the second travel state distribution is calculated. At that time, the driving support unit 100A (driving instability determination unit 140) determines the driving instability (measurement of the driving instability state) for the time when it is determined that the driving state is a disturbance. _A ratio T _A / T _B (hereinafter also referred to as “mask ratio”) between the accumulated value T _A and the accumulated value T _B of the elapsed time from the start of the trip is calculated. Subsequently, the driving support unit 100A (driving instability determination unit 140) reads an α value corresponding to the calculated mask ratio T _A / T _B from the control map M. Accordingly, the driving support unit 100A (driving instability determination unit 140) causes the driving support unit 100A (first travel state distribution calculation unit 130A, second travel state distribution calculation unit to be based on the calculated ratio T _A / T _B. 130B) changes the running state distribution (first running state distribution, second running state distribution).

FIG. 12 is a graph showing the control map M.
As shown in FIG. 12, in the control map M, the mask ratio T _A / T _B is greater than or equal to a first set value T _A / T _B 1 (for example, 0.2) and the second set value T _A / T _B 2. In a range less than (> T _A / T _B 1. For example, 0.8), the α value is set to a predetermined set value regardless of the size of the mask ratio T _A / T _B. Further, the control map M is in the range mask ratio T _A / T _B is first less than the set value T _A / T _B 1, linearly from the set value α value in accordance with a decrease of the mask ratio T _A / T _B Reduce to. Further, the control map M is the mask ratio T _A / T _B is the second set value T _A / T _B 2 or more ranges linearly from the set value of the α value in accordance with the increase of the mask ratio T _A / T _B Increase to.

FIG. 13 is a graph showing alarm sensitivity.
As a result, when the mask ratio T _A / T _B is less than the first set value T _A / T _B 1, the driving instability determination unit 140 increases the α value as the mask ratio T _A / T _B decreases. Get smaller. Therefore, as shown in FIG. 13, the driving instability degree determination unit 140 increases the sensitivity of the alarm (information presentation process) as the mask ratio T _A / T _B decreases (the alarm (information presentation process) is executed). Easier). Further, when the mask ratio T _A / T _B is greater than or equal to the second set value T _A / T _B 2, the driving instability determination unit 140 increases the α value as the mask ratio T _A / T _B increases. Become. Therefore, the driving instability determination unit 140 becomes less sensitive to warning (information presentation processing) as the mask ratio T _A / T _B increases (it becomes difficult to execute the warning (information presentation processing)).

(Effect of this embodiment)
(1) The driving support unit 100A detects a driving state of the vehicle (for example, a disturbance driving state or a normal driving state). Subsequently, the driving support unit 100A calculates the driving state distribution (for example, the first driving state distribution and the second driving state) calculated by the driving support unit 100A based on the detected driving state (for example, disturbance driving state and normal driving state). Distribution).
According to such a configuration, for example, a travel state distribution (for example, a first travel state distribution, a second travel state distribution) according to the driving state of the vehicle can be calculated.

(2) the driving support unit 100A may estimate the driving state (e.g., determination of the degree of instability of the operation) driving situation becomes disturbance against (e.g., disturbance operating conditions) of time is determined that the accumulated value T _A Driving state distribution calculated by the driving support unit 100A based on a ratio T _A / T _B of the elapsed time from the start of the trip and a cumulative value T _B of the elapsed time (for example, first traveling state distribution, second traveling state distribution) To change.
According to such a configuration, for example, a travel state distribution (first state) according to the occurrence of a driving situation (for example, a disturbance driving situation) that becomes a disturbance with respect to the estimation of the driving state (for example, determination of instability of driving). 1 traveling state distribution, second traveling state distribution) can be calculated.

(3) The driving support unit 100A determines a driving situation (for example, a disturbance driving situation or a normal driving situation) based on at least one of a curve operation, a lane change, and a vehicle acceleration / deceleration.
According to such a configuration, it is possible to relatively easily determine a driving situation (for example, a disturbance driving situation) that becomes a disturbance with respect to the estimation of the driving state.
(4) The driving support unit 100A determines a driving situation (disturbance driving situation) based on at least one of an irregular road and a swell.
According to such a configuration, it is possible to relatively easily determine a driving situation (for example, a disturbance driving situation) that becomes a disturbance with respect to the estimation of the driving state.

(Third embodiment)
Next, a third embodiment according to the present invention will be described with reference to the drawings.
In addition, the same code | symbol is used about the same structure as said each embodiment.
FIG. 14 is a diagram illustrating a configuration of a vehicle equipped with the vehicle information providing apparatus of the present embodiment. FIG. 15 is a flowchart showing the driving instability degree determination process.
In this embodiment, when the seat belt of the driver's seat is removed or attached (hereinafter also simply referred to as “removal”), the steering angle is predicted for a set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100. The difference from the first embodiment is that the error θe is initialized.
Specifically, as shown in FIG. 15, the present embodiment is different from the first embodiment in that step S206 is provided between step S201 and step S202 of FIG.

The vehicle also includes a seat belt sensor 11 as shown in FIG.
The seat belt sensor 11 detects whether the seat belt (not shown) in the driver's seat is attached or detached (removed or attached). Subsequently, the seat belt sensor 11 outputs the detected attachment / detachment state to the controller 100.
In step S <b> 206, the driving support unit 100 </ b> A determines whether the seat belt (not shown) in the driver's seat has been attached / detached (removed or attached) based on the attached / detached state output by the seat belt sensor 11. When the driving support unit 100A determines that the seat belt (not shown) of the driver's seat has been attached / detached (Yes), the steering angle during the set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 is determined. After the prediction error θe is initialized, this calculation process is terminated. On the other hand, if the driving assistance unit 100A determines that the driver's seat belt (not shown) is not attached or detached (No), the driving assistance unit 100A proceeds to step S203.

(Effect of this embodiment)
(1) The seat belt sensor 11 detects the attachment / detachment state of the seat belt in the driver's seat. Subsequently, the driving assistance unit 100A changes the traveling state distribution (for example, the first traveling state distribution and the second traveling state distribution) calculated by the driving assistance unit 100A based on the detected attachment / detachment state.
According to such a configuration, for example, when the driver changes and the seat belt is attached / detached, the traveling state distribution (first traveling state distribution, second traveling state distribution) can be changed.

(2) The driving support unit 100A calculates distribution data (for example, first traveling state distribution, second traveling state distribution) based on traveling state data (for example, steering angle information) (for example, distribution data (for example, steering angle information)). Steering angle prediction error θe) is stored in the memory of the controller 100. In addition, the driving support unit 100A estimates the driving state of the driver based on distribution data (for example, the steering angle prediction error θe) stored in the memory of the controller 100. At this time, if the driving support unit 100A determines that the seat belt of the driver's seat has been attached / detached, the driving support unit 100A initializes distribution data (for example, steering angle prediction error θe) stored in the memory of the controller 100.
According to such a configuration, for example, when the driver changes and the seat belt is attached / detached, the traveling state distribution (first traveling state distribution, second traveling state distribution) can be initialized.

(Modification)
In the present embodiment, when it is determined that the seat belt (not shown) is attached / detached (removed or attached), the steering angle for a set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 is determined. Although an example in which the prediction error θe is initialized has been shown, other configurations may be employed. For example, when it is determined that the door of the driver's seat has been opened or closed (hereinafter, also simply referred to as “open / close”), the steering angle during a set time T seconds (for example, 2160 seconds) accumulated in the memory of the controller 100 The prediction error θe may be initialized.

FIG. 16 is a diagram illustrating a configuration of a vehicle on which the vehicle information providing apparatus of this embodiment is mounted.
Specifically, as shown in FIG. 16, the vehicle includes a door opening / closing sensor 12.
The door opening / closing sensor 12 detects the opening / closing state (opened or closed) of the door of the driver's seat. Subsequently, the door opening / closing sensor 12 outputs the detected opening / closing state to the controller 100.
In step S <b> 206, the driving support unit 100 </ b> A determines whether the door of the driver's seat has been opened / closed based on the open / closed state output by the door open / close sensor 12. When the driving support unit 100A determines that the door of the driver's seat has been opened and closed (Yes), the steering angle prediction error θe for a set time T seconds (for example, 2160 seconds) stored in the memory of the controller 100 is initialized. This calculation process is terminated. On the other hand, if the driving assistance unit 100A determines that the door of the driver's seat is not opened or closed (No), the driving assistance unit 100A proceeds to step S203.
In the present embodiment, the door opening / closing sensor 12 of FIG. 16 constitutes an opening / closing state detection unit.

(Effect of this modification)
(1) The door opening / closing sensor 12 detects the opening / closing state of the door of the driver's seat. Subsequently, the driving assistance unit 100A changes the traveling state distribution (for example, the first traveling state distribution and the second traveling state distribution) calculated by the driving assistance unit 100A based on the detected opening / closing state.
According to such a configuration, for example, when the driver changes and opens and closes the door, the traveling state distribution (first traveling state distribution, second traveling state distribution) can be changed.

(2) The driving support unit 100A calculates distribution data (for example, first traveling state distribution, second traveling state distribution) based on traveling state data (for example, steering angle information) (for example, distribution data (for example, steering angle information)). Steering angle prediction error θe) is generated. Subsequently, the driving support unit 100A accumulates the generated distribution data (for example, the steering angle prediction error θe) in the memory of the controller 100. In addition, the driving support unit 100A estimates the driving state of the driver based on distribution data (for example, the steering angle prediction error θe) stored in the memory of the controller 100. At this time, if the driving support unit 100A determines that the door of the driver's seat has been opened / closed, the driving support unit 100A initializes distribution data (for example, steering angle prediction error θe) stored in the memory of the controller 100.

According to such a configuration, for example, when the driver changes and opens and closes the door, the traveling state distribution (first traveling state distribution, second traveling state distribution) can be initialized.
As described above, the entire contents of Japanese Patent Application 2013-150724 (filed on July 19, 2013) to which the present application claims priority form part of the present disclosure by reference.
Although the present invention has been described with reference to a limited number of embodiments, the scope of rights is not limited thereto, and modifications of each embodiment based on the above disclosure are obvious to those skilled in the art.

11 Seat belt sensor (detachment state detector)
110 Traveling state data acquisition unit (traveling state data acquisition unit)
120 Driving status determination unit (driving status determination unit)
130 Traveling state distribution calculation unit (traveling state distribution calculation unit, setting change unit)
130A First traveling state distribution calculating unit (running state distribution calculating unit)
130B Second travel state distribution calculation unit (travel state distribution calculation unit)
130D Distribution selection unit (setting change unit)
130E Distribution setting part (setting change part)
140 Driving instability determination unit (driving state estimation unit)
150 Information presentation part (Information presentation part)
Step S101 (running state data acquisition unit)
Step S103 (driving condition determination unit)
Steps S105, S107, S108 (setting change unit)
Step S110 (driving state estimation part)
Step S111 (information presentation part)

Claims (9)

A driving state data acquisition unit that acquires driving state data including at least one of an operation state of the driving operator and a vehicle state that can be operated by the driver;
A driving state distribution calculating unit that calculates a plurality of driving state distributions having different temporal ranges based on the driving state data acquired by the driving state data acquiring unit;
A setting changing unit for changing the running state distribution calculated by the running state distribution calculating unit based on information related to running of the vehicle;
A driving state estimating unit that estimates the driving state of the driver based on the plurality of driving state distributions calculated by the driving state distribution calculating unit;
An information presentation unit for presenting information to the driver based on the driving state estimated by the driving state estimation unit;
As the information related to the travel of the vehicle, an elapsed time detection unit that detects an elapsed time from the start of the travel of the vehicle,
The setting changing unit changes the traveling state distribution calculated by the traveling state distribution calculating unit based on the elapsed time detected by the elapsed time detecting unit,
A driving condition determination unit for determining the driving condition of the vehicle;
The travel state distribution calculating unit is configured to calculate a first travel state distribution having a relatively long time range based on the travel state data acquired by the travel state data acquisition unit, and the first travel state distribution. Calculate the second running state distribution with a short time range,
When the setting change unit determines that the driving state determination unit is in a driving state that is a disturbance to the estimation of the driving state, when the elapsed time detected by the elapsed time detection unit is less than a set time, The elapsed time detected by the elapsed time detection unit selected by selecting the second running state distribution calculated before the driving status determination unit determines that the driving situation is a disturbance to the estimation of the driving state. Is equal to or longer than a set time, the current second traveling state distribution calculated by the traveling state distribution calculating unit is selected, and the first traveling state distribution is replaced with the selected second traveling state distribution. A vehicle information providing device. The driving state determination section, as the information concerning the traveling of the vehicle, to determine the driving condition of the vehicle,
2. The vehicle information according to claim 1, wherein the setting change unit changes the travel state distribution calculated by the travel state distribution calculation unit based on the driving state determined by the driving state determination unit. Providing device. The setting change unit is configured to perform the travel based on a ratio between a cumulative value of the time determined to be in the driving situation that is a disturbance by the driving status determination unit and a cumulative value of an elapsed time from the start of traveling of the vehicle. The vehicle information providing apparatus according to claim 2 , wherein the traveling state distribution calculated by the state distribution calculating unit is changed. The driving state determination section may curve operation, change lanes, and the vehicle information providing device according to claim 2 or 3, characterized in that determining the operating conditions based on at least one of acceleration and deceleration of the vehicle . It said driving condition determining unit, rough road, and vehicle information providing device according to claim 2 or 3, characterized in that determining the operating conditions based on at least one undulation. As information relating to the traveling of the vehicle, a detachable state detection unit for detecting the detachable state of the seat belt of the driver's seat,
The setting change unit, any one of claims 1, 4, and 5, characterized in that to change the running state distribution where the running state distribution calculating section calculates, based on the detachable state in which the detachable state detection unit detects The vehicle information providing apparatus according to claim 1. The setting change unit accumulates distribution data for calculating a plurality of the traveling state distributions based on the traveling state data acquired by the traveling state data acquisition unit, and a plurality of the setting change units based on the accumulated distribution data. claims to calculate the running state distribution, when it detects that it has detachable seat belt of the driver's seat in the detachable state detector, and wherein the initializing the distribution data has accumulated 6 The vehicle information providing device according to claim 1. As the information on the traveling of the vehicle, comprising an open / closed state detection unit for detecting the open / closed state of the door of the driver's seat,
The setting change unit, any one of claims 1 to 7, characterized in that to change the running state distribution where the running state distribution calculating section calculates, based on the opening and closing state of the opening and closing state detection unit detects The vehicle information providing device according to Item. The setting change unit accumulates distribution data for calculating a plurality of the traveling state distributions based on the traveling state data acquired by the traveling state data acquisition unit, and a plurality of the setting change units based on the accumulated distribution data. claim 8 of to calculate the running state distribution, when it detects that the opening and closing the seat belt of the driver's seat in the closed state detection section, and wherein the initializing the distribution data stored The vehicle information providing device according to claim 1.

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