JP2008018872A - Driving support device, automobile and driving support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To support driving by properly recognizing driving characteristics of a vehicle when accelerating/decelerating the vehicle. <P>SOLUTION: This driving support device is provided with: a vehicle speed detection part 2 for detecting the traveling status of its own vehicle; an intervehicle distance detecting part 1 for detecting the surroundings of its own vehicle; a throttle opening detection part 3 for detecting the accelerating/decelerating operation to its own vehicle; a follow-up traveling classification part 21 for obtaining driving characteristics by classifying a relationship between the traveling status of its own vehicle and the surroundings of its own vehicle when the accelerating/decelerating operation is detected by the throttle opening detection part 3 into a plurality of element characteristics; and an intervehicle distance automatic control part 8 for supporting driving of a driver based on at least any element characteristics in the driving characteristics obtained by the follow-up traveling classification part 21. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a driving support device, a car, and a driving support method that support driving.

  2. Description of the Related Art Conventionally, a driving support device that changes a driving support method according to a driver's state, skill, or preference has been developed. In such a driving assistance device, it is desired that the driver's acceptability with respect to the provided driving assistance is high. Here, acceptability is an index indicating whether the driver accepts or rejects the provided driving assistance. If the acceptability is high, the driving assistance is used by the driver, and if the acceptability is low, the acceptability is low. This means that the driving assistance becomes unnecessary for the driver.

In order to increase this acceptability, many driving support technologies have been developed that change the driving support control law by learning the driver's past driving history, but driving support that fully matches the driving characteristics of the driver. It is not possible for the driver to perform, and the driver does not feel uncomfortable, that is, many are unacceptable technologies.
On the other hand, a technique for matching the driving assistance provided with the driving characteristics of the driver has been proposed. For example, in Patent Document 1, as an automatic driving control device and an inter-vehicle distance alarm device for an automobile, control of an inter-vehicle distance or the like in an automatic driving control of an automobile or setting of an alarm notification distance in an inter-vehicle distance alarm device is made by the driver's intention. We propose an interface technology between the driver and the driver that is left to the task.

According to such a technique, it is considered that the driving support can be matched with the driving characteristics of the driver because it is based on the driver's own setting.
Japanese Patent Laid-Open No. 10-338057

However, since the driving characteristics of the driver are affected by various factors such as the driving environment, the state of surrounding vehicles, the skill of each driver, and the driving load state, it is difficult to specify the driving characteristics. For this reason, it is difficult to match the driving assistance provided with the driving characteristics of the driver with a technique of simply referring to the past driving history to provide driving assistance. Thus, if it is based on a driver | operator's own setting, since adjustment of a setting value is always needed, a driver | operator will feel troublesome, and using such a method is also not a sufficient solution.
As described above, in the conventional technology, it is difficult to appropriately grasp the driving characteristics of the driver when performing driving support.
An object of the present invention is to provide driving assistance by appropriately grasping the driving characteristics of the driver.

In order to solve the above-mentioned problem, a driving support device according to the present invention includes:
Traveling state detecting means for detecting the traveling state of the host vehicle, ambient state detecting means for detecting a situation around the host vehicle, acceleration / deceleration operation detecting means for detecting an acceleration / deceleration operation on the host vehicle, and the acceleration / deceleration operation detecting unit Driving characteristic grasping means for classifying the relationship between the traveling state of the own vehicle when the acceleration / deceleration operation is detected by the vehicle and the situation around the own vehicle into a plurality of element characteristics and grasping the driving characteristic; and the driving characteristic grasping means Driving support means for providing driving support to the driver based on at least one of the element characteristics in the driving characteristics grasped by the step.
In addition, the driving support device according to the present invention includes:
The driving behavior of the driver is classified into a plurality of element characteristics corresponding to the situation around the host vehicle, the driving characteristics are grasped, and driving assistance is performed based on the driving characteristics.

According to the present invention, the relationship between the traveling state of the host vehicle when the acceleration / deceleration operation is detected and the situation around the host vehicle is classified into a plurality of element characteristics, the driving characteristics are grasped, and at least the driving characteristics in the driving characteristics are determined. A driving support device capable of appropriately grasping the driving characteristics when the acceleration / deceleration operation of the vehicle is performed and performing driving support by performing driving support for the driver based on any element characteristic; can do.
Further, according to the present invention, the driving behavior of the driver is classified into a plurality of element characteristics corresponding to the situation around the host vehicle, the driving characteristics are grasped, and driving assistance is performed based on the driving characteristics. It can be set as the driving assistance device which can grasp a driving characteristic appropriately from a driver's driving action, and can perform driving assistance.

The best mode for carrying out the present invention (hereinafter referred to as an embodiment) will be described in detail with reference to the drawings.
(First embodiment)
The first embodiment is a driving assistance device to which the present invention is applied.
(Constitution)
FIG. 1 is a block diagram showing the configuration of the driving support apparatus.
As shown in FIG. 1, the driving assistance device includes an inter-vehicle distance detection unit 1, a vehicle speed detection unit 2, a throttle opening detection unit 3, a tracking state determination unit 20, a road attribute detection unit 4, a traffic volume detection unit 5, A tracking state estimation unit 6, a control law adjustment unit 7, and an inter-vehicle distance automatic control unit 8 are provided. The follow-up state determination unit 20 includes a follow-up traveling classification unit 21 and a follow-up state classification holding unit 22.

2 and 3 show a processing procedure in the driving support apparatus. The driving support apparatus varies the processing procedure according to the on state and the off state of the inter-vehicle distance automatic control unit 8, and FIG. 2 shows the processing procedure when the inter-vehicle distance automatic control unit 8 is in the off state. 3 shows a processing procedure when the inter-vehicle distance automatic control unit 8 is in the ON state.
First, FIG. 2 which shows the process sequence of the driving assistance device when the inter-vehicle distance automatic control unit 8 is in the off state will be described.
As shown in FIG. 2, when the processing is started, in step S1, the throttle opening degree detection unit 3 detects the throttle opening degree by the operation of the accelerator pedal 3a, and the throttle is released based on the detection result. It is determined whether or not. Here, it is determined that the throttle is released when the throttle shifts from a state where the throttle is opened to some extent to a state where it is closed.

  Further, if the throttle opening / closing is performed when the inter-vehicle distance automatic control unit 8 is in the off state, the opening / closing is based on the driver's accelerator operation, so the presence / absence of the driver's accelerator operation is directly detected, If there is an accelerator operation by the driver, it can be determined that the throttle is in an open state, and if there is no accelerator operation by the driver, it can also be determined that the throttle is in a closed state. If the throttle is released in step S1, the process proceeds to step S2.

  In step S2, the vehicle speed detection unit 2 detects the host vehicle speed, and the inter-vehicle distance detection unit 1 detects the inter-vehicle distance between the host vehicle and a preceding vehicle traveling in front of the host vehicle. Here, for example, the inter-vehicle distance between the host vehicle and the preceding vehicle is detected using a laser radar, a millimeter wave radar, or the like provided in the inter-vehicle distance automatic control unit 8. In this step S2, after detecting the inter-vehicle distance and the own vehicle speed in each part, the process proceeds to step S3.

After step S3, the follow-up traveling classification unit 21 of the follow-up state determination unit 20 uses the set of the inter-vehicle distance and the own vehicle speed detected in step S1 as a measurement point in the follow-up state classification holding unit 22 of the follow-up state determination unit 20. This is a so-called learning step for newly belonging to the stored information.
Here, as a result of recent research, when the driver follows the preceding vehicle and travels the host vehicle, the driver releases the throttle and adjusts the distance between the host vehicle and the preceding vehicle. It was clarified that there is a predetermined pattern (correlation) between and.

FIG. 4 shows a predetermined pattern that exists between the own vehicle speed and the distance between the preceding vehicle and the preceding vehicle. In FIG. 4, the horizontal axis represents the host vehicle speed, and the vertical axis represents the inter-vehicle distance. As illustrated in FIG. 4, the host vehicle speed and the inter-vehicle distance when the driver causes the host vehicle to follow the preceding vehicle are as follows: When developed on a coordinate plane corresponding to each of the two orthogonal axes, there is a distribution approximated by a plurality of straight lines having the same inclination, and there are a finite number of predetermined patterns that can be expressed by parallel lines. This means that if the number of finite number of parallel lines is N, there are N types of tracking states (tracking characteristics) 1 to N from the tracking state (tracking characteristics) 1 to the tracking state (tracking characteristics) N. To do.

  Assuming that the driver's following characteristics can be expressed in this way, the following state classification holding unit 22 assigns (associates) each past measurement point to each of the N following states (following state information) and holds it. is doing. Here, the past measurement points consist of data indicating a set of the own vehicle speed and the inter-vehicle distance between the preceding vehicles when the driver releases the throttle in the past travel. In step S3 and subsequent steps, a process of newly assigning the measurement point detected in step S2 to the most appropriate tracking state among the N tracking states is performed.

  That is, the follow-up traveling classification unit 21 refers to the follow-up state classification holding unit 22 in step S3, and in subsequent step S4, the k past measurement points associated with each follow-up state are k in each follow-up state. It is determined whether or not the above is true. For example, FIG. 5 shows, as an image, a state where there are k past measurement points associated with each of the following states 1 to N. Here, if the number of following traveling classification units 21 is k or more, the process proceeds to step S5. If the number is less than k, the process proceeds to step S16.

  The tracking state held (stored) in the tracking state classification holding unit 22 can be expressed by a finite number of parallel lines, and the parallel lines are extracted (calculated) from past measurement points associated with each tracking state. ) In this embodiment, each parallel line extracted from the past measurement points is held as a characteristic line (regression line as described later) that expresses the characteristics of the individual following states, and the following state classification holding unit 22 The individual characteristic straight lines are held in association with the respective following states. In step S <b> 5, the following traveling classification unit 21 refers to the characteristic straight line held in the following state classification holding unit 22. Hereinafter, the characteristic lines corresponding to the following states 1 to N are denoted as characteristic lines 1 to N, respectively.

Subsequently, in step S6, the follow-up traveling classification unit 21 selects a characteristic line closest to the latest measurement point measured in step S2 from the characteristic line referenced in step S5, and the selected characteristic line is The latest measurement point is added (attached) to the corresponding tracking state.
Note that each characteristic line is y _i = m _i · x _i + n _i (i = 1, 2,..., N), measurement points are (a, b), and measurement points (a, b) and The distance d _i with the characteristic line y _i = mx _i + n _i is calculated by the following equation (1).
d _i = | b−m _i × a−n _i | / √ (1 + m _i ² ) (1)

Subsequently, in step S7, the follow-up travel classification unit 21 calculates a regression line based on all the measurement points associated with the follow-up state in which the measurement point is newly added in step S6, and the calculated regression line Is a characteristic straight line. For the calculation of the regression line, a generally used technique for obtaining a linear expression that minimizes the sum of squares of errors in the y-axis direction is applied.
Subsequently, in step S <b> 8, the following traveling classification unit 21 examines the parallelism of the N characteristic lines 1 to N corresponding to the following states 1 to N. Here, the follow-up travel classification unit 21 proceeds to step S10 when the N characteristic lines 1 to N are parallel to each other, and proceeds to step S9 when the N characteristic lines 1 to N are not parallel to each other.

In step S9, the follow-up run classification unit 21 calculates the average value of the slope of the N characteristic line 1 to N for each follow-up state 1 to N (corresponding to the value m _i),. 1 to the follow-up state When the slope of N is set to the calculated slope (average value), the distance between each of the following states 1 to N (characteristic straight lines 1 to N) and the measurement point associated with each of the following states 1 to N is the smallest. A straight line is calculated, and the calculated straight line is set as a new characteristic line y _i = m _i · x _i + n _i corresponding to each follow-up state.

In step S10, the follow-up travel classification unit 21 calculates the distance to the characteristic line corresponding to the following state based on the measurement point associated with the following state to which the new measurement point belongs. The process proceeds to step S11.
In step S11, the follow-up traveling classification unit 21 calculates the distribution of the distance calculated in step S10.
Subsequently, in step S12, the following traveling classification unit 21 determines whether or not the distribution calculated in step S11 has multimodality. Here, if the distribution has multimodality, the follow-up traveling classification unit 21 proceeds to step S13. If the distribution does not have multimodality, the following traveling classification unit 21 ends the processing shown in FIG. 2 (from step S1 again). Start processing, and so on).

  In step S13, the follow-up travel classification unit 21 divides the measurement points associated with the follow-up state in order to remove the multimodality of the distribution. In general, as a technique for dividing a distribution including multimodality into a plurality of unimodal distributions, there are known genetic algorithms and EM algorithms. Also in step S13, the follow-up traveling classification unit 21 divides the measurement points associated with the follow-up state so as to obtain a plurality of unimodal distributions by these methods. As a result, a new tracking state corresponding to the number of divisions is generated.

  Then, the follow-up travel classification unit 21 extracts (calculates) characteristic straight lines corresponding to the respective following states based on the measurement points associated with the newly generated following states. Here, as in step S7, a regression line is calculated from the measurement points, and the calculated regression line is used as a characteristic line. Again, a regression line is calculated by applying a commonly used method for obtaining a linear expression that minimizes the sum of squares of errors in the y-axis direction.

  Subsequently, in step S14, the follow-up traveling classification unit 21 determines whether the characteristic line corresponding to the newly generated following state in step S13 is parallel to the characteristic line corresponding to another following state (existing following state). Determine whether or not. Here, the follow-up travel classifying unit 21 proceeds to step S15 if not parallel, and ends the process shown in FIG. 2 if parallel.

In step S15, the follow-up traveling classifying unit 21 does not correspond to the N characteristic lines 1 to N corresponding to the following states 1 to N, as in step S9. calculates an average value of the gradient of the characteristic straight line 1 to N (corresponding to the value m _i), when the slope of the slope of each follow-up state 1 to N and the calculated (mean value), each of said follow-up state 1 to N (characteristic straight lines 1 to N) and a straight line that minimizes the distance between the measurement points associated with each of the following states 1 to N are calculated, and the calculated straight line is a new one corresponding to each following state. Characteristic line y _i = m _i · x _i + n _i . Then, the process shown in FIG.

Since a new follow-up state is generated in step S13, the number of follow-up states (characteristic straight lines) to be corrected for tilt in step S15 is larger than N.
On the other hand, in step S16 that proceeds when the number is less than k in step S4, the follow-up travel classification unit 21 calculates a regression line based on all past measurement points. For the calculation of the regression line, a generally used technique for obtaining a linear expression that minimizes the sum of squares of errors in the y-axis direction is applied.

Subsequently, in step S17, the follow-up travel classification unit 21 calculates the distance between the regression line calculated in step S16 and all measurement points.
Subsequently, in step S18, the follow-up traveling classification unit 21 calculates the variance of the distance calculated in step S17.
Subsequently, in step S19, the follow-up traveling classification unit 21 determines whether or not the variance calculated in step S18 is large. For example, the determination is made based on a threshold value determined based on experimental values, experience values, and the like.
Here, the follow-up travel classification unit 21 proceeds to step S20 when the variance is large, and ends the process shown in FIG. 2 when the variance is small.

  In step S20, the follow-up traveling classification unit 21 divides the plurality of categories into a follow-up state based on all measurement points. Here, as a division method, a method of classifying into two types of tracking states of measurement points larger and smaller than the average value of all measurement points, or when the standard deviation of all measurement points is σ, ± σ And a method of dividing into all three types of tracking states of measurement points (two types of measurement points) that fall within the range of (1) and measurement points (one type of measurement points) that deviate from ± σ. In step S20, the follow-up traveling classifying unit 21 performs both division methods, and adopts one of the methods (implementation results) that maximizes the similarity in the group (category) from the implementation results. To do. Then, a regression line is calculated from the measurement points associated with each following state obtained by the division, and this is associated with each following state as a characteristic line.

Subsequently, in step S21, the follow-up traveling classification unit 21 determines whether or not the characteristic straight lines calculated in step S20 are parallel to each other. Here, the follow-up travel classification unit 21 proceeds to step S22 if not parallel, and ends the process shown in FIG. 2 if parallel.
In step S22, the follow-up traveling classification unit 21 selects the follow-up state to which the most measurement points belong from among the plurality of follow-up states obtained by dividing in step S20, and corresponds to the selected follow-up state. Correction is performed such that the slope of the characteristic line corresponds to another follow-up state (non-selected follow-up state). Then, the following traveling classification unit 21 calculates a straight line that minimizes the distance from the measurement point associated with each following state under the corrected inclination, and sets the calculated straight line to each following state. The corresponding new characteristic line y _i = m _i · x _i + n _i . Then, the process shown in FIG.
As will be described later, the following state extracted when the inter-vehicle distance automatic control unit 8 is in the off state is the road attribute detected by the road attribute detection unit 4 and the traffic volume detected by the traffic volume detection unit 5. The tracking state estimation unit 6 stores the associated information.
The above is the processing procedure of the driving support apparatus when the inter-vehicle distance automatic control unit 8 is in the off state.

Next, a processing procedure of the driving support apparatus when the inter-vehicle distance automatic control unit 8 is on will be described with reference to FIG.
As shown in FIG. 3, when the process is started, in step S31, the operation state determination unit 9 determines whether or not the inter-vehicle distance automatic control unit 8 is in the operation state of ACC (adaptive cruise control). Here, when the operation state determination unit 9 determines that the inter-vehicle distance automatic control unit 8 is in the ACC operation state, the process proceeds to the subsequent step S32.
In step S32, the vehicle speed detection unit 2 detects the host vehicle speed.

Subsequently, in step S33, the road attribute detection unit 4 detects the attribute of the road on which the host vehicle travels. Specifically, the attribute of the road is at least one of the number of lanes, the width of the lane, and the degree of gradient. Here, each attribute is obtained based on information held in the map information of the navigation device.
Subsequently, in step S34, the traffic volume detection unit 5 detects the traffic volume on the road on which the host vehicle travels. As a detection method of this traffic volume, there is a method of detecting the traffic volume by the number of other vehicles included in the image captured from the camera that images the front and side mounted on the vehicle, the distance between the vehicles, etc. Here, the traffic volume is detected by this method.

Subsequently, in step S35, the tracking state estimation unit 6 estimates the tracking state of the host vehicle with respect to the preceding vehicle.
Here, the following state extracted when the inter-vehicle distance automatic control unit 8 is in the off state is included in the following state estimation unit 6 based on the road attribute detected by the road attribute detection unit 4 and the traffic volume detected by the traffic volume detection unit 5. Is stored in association with. That is, even when the inter-vehicle distance automatic control unit 8 is in the off state, the road attribute detection unit 4 detects the road attribute, the traffic volume detection unit 5 detects the traffic volume, and the detected road attribute and traffic volume are detected. Each follow-up state is stored in association with.

In step S35, the follow-up state estimation unit 6 estimates one follow-up state corresponding to the road attribute detected in step S33 and the traffic detected in step S34 from the plurality of follow-up states.
Subsequently, in step S36, the control law adjusting unit 7 adjusts the control law for the inter-vehicle distance used by the inter-vehicle distance automatic control unit 8 in the ACC based on the one tracking state estimated in the step S35. The normal control rule for the inter-vehicle distance is such that the inter-vehicle distance is increased as the vehicle speed increases. Here, regardless of such a control rule, the control method corresponds to the tracking state estimated in step S35. The inter-vehicle distance is controlled according to the control law, that is, in accordance with the relationship between the own vehicle speed and the inter-vehicle distance in the following state.
The above is the processing procedure of the driving support apparatus when the inter-vehicle distance automatic control unit 8 is in the on state.

(Operation)
Next, the operation will be described.
In the driving assistance device, when the throttle is shifted from a state where it is opened to a certain degree, that is, when the throttle is released (step S1), the inter-vehicle distance detection unit 1 and the vehicle speed detection unit 2 The inter-vehicle distance and the own vehicle speed are detected (step S2). Then, the following traveling classification unit 21 refers to the following state classification holding unit 22 (step S3), and whether or not the number of past measurement points associated with each following state is k or more in each following state. Determine whether or not. The operation is as follows according to the determination result.

(1) When there are less than k past measurement points associated with each follow-up state When there are less than k past measurement points associated with each follow-up state, the follow-up traveling classification unit 21 is all Based on the past measurement points, a regression line is calculated, and the distance between the regression line and all the measurement points is calculated (steps S16 and S17).

Then, the follow-up traveling classification unit 21 calculates the variance of the distance. If the variance is large, the follow-up travel classification unit 21 divides the category into a plurality of categories based on all the measurement points, and sets each category as a follow-up state. A regression line is calculated from the measurement points associated with the state, and this is associated with each following state as a characteristic line (steps S18 to S20). If the characteristic straight lines calculated as the regression lines are not parallel to each other (step S21), the follow-up traveling classification unit 21 selects the follow-up state to which the most measurement points belong from among the plurality of follow-up states. Then, correction is performed so that the slope of the characteristic line corresponding to the selected follow-up state becomes the same as the slope of the characteristic line corresponding to another follow-up state (non-selected follow-up state). Further, the following traveling classification unit 21 calculates a straight line that minimizes the distance from the measurement point associated with each following state under the corrected inclination, and sets the calculated straight line to each following state. The corresponding new characteristic line is set (step S22).
Here, when the variance is small (the step S19), the tracking state is not calculated. When the characteristic lines calculated as the regression lines are parallel to each other (step S20), the inclination of the characteristic line is not corrected.

(2) When there are k or more past measurement points associated with each follow-up state When there are k or more past measurement points associated with each follow-up state, the follow-up traveling classification unit 21 follows With reference to the characteristic straight line held in the state classification holding unit 22, the characteristic straight line closest to the latest measurement point is selected from the characteristic straight line, and the latest tracking point corresponds to the tracking state corresponding to the selected characteristic straight line. Measurement points are added (steps S5 and S6). Then, the follow-up traveling classification unit 21 calculates a regression line based on all the measurement points associated with the follow-up state including the latest measurement point, and sets the calculated regression line as a characteristic line (the above-mentioned Step S7).

  Further, when the N characteristic lines 1 to N are not parallel to each other, the following traveling classification unit 21 calculates an average value of the inclinations of the N characteristic lines 1 to N, and calculates the inclinations of the respective following states 1 to N. When the calculated inclination (average value) is used, a straight line that minimizes the distance between each of the following states 1 to N (characteristic straight lines 1 to N) and the measurement point associated with each of the following states 1 to N is obtained. The calculated straight line is set as a new characteristic straight line corresponding to each follow-up state (steps S8 and S9). Here, when the N characteristic lines 1 to N are parallel to each other (step S8), the inclination of the characteristic line is not corrected.

  On the other hand, the following traveling classification unit 21 calculates the distance to the characteristic straight line corresponding to the following state based on the measurement point associated with the following state to which the new measurement belongs, and the distance Is calculated (steps S10 and S11). And when the distribution has multimodality, the tracking traveling classification unit 21 divides the measurement points associated with the tracking state in order to remove the multimodality of the distribution, and based on the divided measurement points, A characteristic straight line corresponding to the following state is extracted (calculated) (steps S12 and S13). Here, when the characteristic straight line corresponding to the newly generated follow-up state is not parallel to the characteristic straight line corresponding to another follow-up state (existing follow-up state), the follow-up traveling classification unit 21 changes the follow-up state 1 to N to each follow-up state 1-N. When the average value of the slopes of the corresponding N characteristic lines 1 to N is calculated and the slope of each of the following states 1 to N is set to the calculated slope, each of the following states 1 to N (characteristic lines 1 to N) is calculated. ) And the measurement point associated with each of the following states 1 to N is calculated, and the calculated straight line is set as a new characteristic line corresponding to each following state (step S14, Step S15).

  Here, when the distribution does not have multimodality (step S12), there is no division of measurement points and no new tracking state is generated. In addition, when the characteristic line corresponding to the newly generated tracking state is parallel to the characteristic line corresponding to another tracking state (existing tracking state) (step S14), the inclination of the characteristic line is corrected. There is no.

(3) At the time of ACC operation And at the time of ACC operation, the tracking state estimation unit 6 corresponds to the road attribute and the traffic volume from the plurality of tracking states obtained based on the measurement points as described above. The control law adjustment unit 7 adjusts the control rule for the inter-vehicle distance used by the inter-vehicle distance automatic control unit 8 in the ACC based on the one follow-up state (FIG. 3).

(Function)
Next, the operation will be described.
Each time the driver stops the accelerator operation (every time the accelerator pedal 3a is returned), it is assumed that the driver intends to adjust the relative relationship between the host vehicle and the preceding vehicle. Are accumulated as measurement points (step S2), and a plurality of following states (following state information) expressed as a plurality of characteristic straight lines can be obtained based on all the accumulated measurement points (the aforementioned state information). Step S16 to Step S22). This is because the relationship between the own vehicle speed and the inter-vehicle distance when the driver detects a deceleration operation is classified into a plurality of groups according to the degree of similarity to each other, and the characteristics of each classified group are calculated. Equivalent to element characteristics. The characteristic straight lines are always adjusted so as to be parallel to each other (steps S21 and S22).

  And when the number of times the driver stops the accelerator operation increases to some extent, that is, when a certain number of measurement points consisting of a set of inter-vehicle distance and own vehicle speed obtained when the driver stops the accelerator operation is accumulated. Based on the latest measurement point, the characteristic straight line corresponding to the following state is adjusted (steps S5 to S15). For example, it is corrected so that characteristic straight lines corresponding to a plurality of follow-up states are parallel to each other (steps S8 and S9), or measurement points associated with the follow-up state to which the latest measurement points belong. When the has a further multimodal distribution, a new following state (characteristic straight line) that eliminates the multimodal distribution is generated (steps S10 to S13). Even when a new following state (characteristic line) is generated, the characteristic lines corresponding to the complex following state are corrected so as to be parallel to each other (steps S14 and S15).

As a result, the driver's accelerator operation, that is, the driver's intention to adjust the relative relationship between the host vehicle and the preceding vehicle is always divided into a plurality of the following states for the preceding vehicle, and the following state Characteristic straight lines corresponding to are always parallel to each other.
Here, as a result of recent research, when the driver follows the preceding vehicle and travels the host vehicle, the driver releases the throttle and adjusts the distance between the host vehicle and the preceding vehicle. It has been elucidated that a predetermined pattern (correlation) exists between the following states 1 and N. Specifically, as shown in FIG. It is supposed that N can be shown by N parallel lines with a finite number. That is, the driver unconsciously uses a plurality of types of follow-up states according to the driving scene (the own vehicle speed and the inter-vehicle distance). When developed on the coordinate plane, a distribution is approximated (regressed) by a plurality of straight lines having the same inclination.

  In this embodiment, on the premise that the driver has the following state (following characteristic) as described above, a plurality of types of following states are obtained based on the traveling scene (the own vehicle speed and the inter-vehicle distance). And, in the case of following control with ACC by associating a plurality of following states corresponding to the driver obtained in this way with the driving environment (road attribute and traffic volume) that obtained the following state, Based on the traveling environment at the time of the control, one tracking state is selected from a plurality of tracking states, and the inter-vehicle distance control and the vehicle speed control are performed so as to realize the selected tracking state.

  As described above, a plurality of types (finite number) of following states (following characteristics) to the preceding vehicle by the driver are prepared in advance, and the relative relationship between the driver's own vehicle and the preceding vehicle is determined by releasing the throttle. When detecting the intention to adjust, the driver determines (selects) one following state from the plurality of following states based on the inter-vehicle distance and the own vehicle speed as indices when the driver follows the preceding vehicle. ing. Thereby, this one following state can be specified as what follows a driver's driving characteristic (following characteristic). That is, it is possible to appropriately grasp the driving characteristics when the vehicle is decelerated.

Then, one tracking state is determined (selected) from a plurality of tracking states, and the characteristics of the inter-vehicle distance control are adjusted based on the one tracking state to assist driving. Thus, since the one following state is specified as being in accordance with the driving characteristics of the driver, the inter-vehicle distance control characteristic can be set in accordance with the driving characteristics of the driver.
Further, when the vehicle speed and the inter-vehicle distance are developed on coordinate planes corresponding to the two orthogonal axes, a distribution approximated by a plurality of straight lines having the same inclination is formed, and a plurality of types of following states are obtained. It is expressed as a straight line. As described with reference to FIG. 4, this expression form conforms to the actual driving characteristics of the driver (original driving characteristics). It can be specified as a rule. In other words, on the premise that the actual driver's following characteristic shows a certain characteristic, one following state (one following state pattern) is specified, so that one following state is identified. Can be specified more in accordance with the driving characteristics of the driver.

Further, the relationship between the plurality of characteristic straight lines is corrected at the timing when the driver performs a deceleration operation, that is, the timing at which measurement points are acquired. As a result, the latest driving characteristics of the driver can be reflected in a plurality of types of tracking states (tracking state information) prepared in advance. As a result, the latest driving characteristics can always be grasped.
Further, the driver's deceleration operation is detected based on the opening / closing state of the throttle for adjusting the engine output, specifically, when the throttle is reversed from the open state to the closed state. Thereby, the driver's deceleration operation can be easily detected.
Further, the traveling environment of the host vehicle is set to road attributes such as the number of lanes, the gradient, and the lane width of the traveling path of the host vehicle and the traffic volume. As a result, the traveling environment of the host vehicle can be easily detected, and the driving characteristics can be grasped in consideration of the number of lanes, the gradient, the lane width, and the traffic volume of the traveling path of the host vehicle.

The present invention can also be realized by the following configuration.
That is, one tracking state is selected from a plurality of types of tracking states (tracking characteristics) based on measurement points (a set of inter-vehicle distance and own vehicle speed) obtained when the vehicle deceleration operation is performed. It is also possible to select the closest tracking state and provide driving assistance based on the selected tracking state. In this case, multiple types of tracking states (tracking characteristics) on the side selected by the measurement point are prepared as general (universal) ones, or, as described above, the driver's deceleration operation is performed. It may be obtained in advance as unique to the driver from the relationship between the vehicle speed and the inter-vehicle distance at the time of detection. Further, as driving assistance, for example, there is one that can operate in real time in response to a driver's deceleration operation, for example, a buzzer or a monitor. Thereby, driving assistance can be performed in conformity with the driving characteristics of the driver.
In the first embodiment, the driver's deceleration operation is detected based on the accelerator operation state (throttle open / close state). On the other hand, the driver's deceleration operation can also be detected based on the brake pedal operation.

In the description of the first embodiment, the vehicle speed detection unit 2 realizes a traveling state detection unit that detects the traveling state of the host vehicle, and the inter-vehicle distance detection unit 1 determines the situation around the host vehicle. The throttle opening detection unit 3 realizes an acceleration / deceleration operation detection unit that detects an acceleration / deceleration operation for the host vehicle, and the follow-up traveling classification unit 21, particularly in step S 6, is realized. The processing classifies the relationship between the traveling state of the host vehicle when the acceleration / deceleration operation detecting means detects the acceleration / deceleration operation and the situation around the host vehicle into a plurality of element characteristics and grasps the driving characteristics. The vehicle distance automatic control unit 8 realizes a grasping means, and the inter-vehicle distance automatic control unit 8 performs driving assistance to the driver based on at least one element characteristic in the driving characteristics grasped by the driving characteristic grasping means. It is realized.
The follow-up travel classification unit 21, particularly the processing in step S15, realizes a correction unit that corrects the relationship between the plurality of straight lines at a timing when the acceleration / deceleration operation detection unit detects an acceleration / deceleration operation on the host vehicle. ing.

(effect)
(1) Traveling state detecting means for detecting the traveling state of the host vehicle, ambient state detecting means for detecting a situation around the host vehicle, acceleration / deceleration operation detecting means for detecting an acceleration / deceleration operation on the host vehicle, and the acceleration / deceleration Driving characteristic grasping means for grasping driving characteristics by classifying the relationship between the traveling state of the own vehicle when the acceleration / deceleration operation is detected by the operation detecting means and the situation around the own vehicle into a plurality of element characteristics; and Driving assistance means for providing driving assistance to the driver based on at least one element characteristic in the driving characteristics grasped by the characteristic grasping means. As a result, it is possible to appropriately understand the driving characteristics when the acceleration / deceleration operation of the vehicle is performed and perform driving support.

(2) The driving characteristic grasping means retains a plurality of element characteristics, and the traveling state of the host vehicle when an acceleration / deceleration operation is detected by the acceleration / deceleration operation detecting means among the plurality of retained element characteristics. And the driving support means performs driving support for the driver based on the element characteristics selected by the driving characteristic grasping means. It is characterized by. As a result, the most approximate characteristic is selected from the plurality of retained element characteristics, so that the driving characteristics can be easily grasped and driving assistance can be performed.

(3) The driving characteristic comprehension means may determine the relationship between the traveling state of the host vehicle and the situation around the host vehicle when the acceleration / deceleration operation is detected in the past driving according to the degree of similarity between the plurality of groups. And calculating the characteristics of each classified group to obtain the plurality of element characteristics, and the driving support means provides driving assistance to the driver based on the element characteristics calculated by the driving characteristic grasping means. Do. Thereby, driving characteristics can be grasped more appropriately and driving assistance can be performed.

(4) The traveling state detecting means detects the own vehicle speed as the traveling state, and the surrounding state detecting means detects an inter-vehicle distance from a preceding vehicle as the surrounding state of the own vehicle. As a result, it is possible to appropriately grasp the relationship between the own vehicle speed when the acceleration / deceleration operation is performed and the inter-vehicle distance between the preceding vehicle as driving characteristics and perform driving support.

(5) When the driving characteristic grasping means develops the own vehicle speed and the inter-vehicle distance when the acceleration / deceleration operation is detected on a coordinate plane corresponding to each of the two orthogonal axes, By regressing on a straight line, the relationship between the vehicle speed and the inter-vehicle distance is classified into a plurality of element characteristics. Thus, when the acceleration and deceleration operation is performed on the coordinate plane corresponding to the two orthogonal axes, the vehicle speed and the inter-vehicle distance can be returned to a plurality of straight lines having the same inclination. The proper driving characteristics of the person can be grasped appropriately.

(6) A correction unit that corrects a relationship between the plurality of straight lines at a timing when the acceleration / deceleration operation detection unit detects an acceleration / deceleration operation on the host vehicle. Thereby, it is possible to always grasp the latest driving characteristics.
(7) The acceleration / deceleration operation detecting means detects the acceleration / deceleration operation based on an opening / closing state of a throttle for adjusting engine output. As a result, the acceleration / deceleration operation of the driver can be easily detected.
(8) The acceleration / deceleration operation detecting means detects the acceleration / deceleration operation when the throttle for adjusting the engine output is reversed from the open state to the closed state. As a result, the acceleration operation of the driver can be easily detected.

(9) The driving support means is based on any of a plurality of element characteristics indicating a relationship between a traveling state of the own vehicle classified by the driving characteristic grasping means and a situation around the own vehicle. Controls the inter-vehicle distance from the preceding vehicle. Thereby, based on the element characteristic obtained based on the inter-vehicle distance with the preceding vehicle, the driving support for controlling the inter-vehicle distance with the preceding vehicle can be performed, so that the driving support for the inter-vehicle distance control adapted to the driving characteristic can be performed. .

(10) The situation around the host vehicle includes at least one of the number of lanes, the gradient, the lane width, and the traffic volume of the travel path of the host vehicle. As a result, the driving characteristics can be grasped in consideration of the number of lanes, the gradient, the lane width, and the traffic volume of the traveling path of the host vehicle.
(11) The driving behavior of the driver is classified into a plurality of element characteristics corresponding to the situation around the host vehicle, the driving characteristics are grasped, and driving assistance is performed based on the driving characteristics. Thereby, it can be set as the driving assistance apparatus which can grasp | ascertain a driving characteristic appropriately and can perform driving assistance.

(12) an accelerator pedal that performs an acceleration operation, a brake pedal that performs a deceleration operation, a traveling state detection unit that detects a traveling state of the host vehicle, an ambient state detection unit that detects a circumstance of the host vehicle, Acceleration / deceleration operation detecting means for detecting an acceleration / deceleration operation on the vehicle, and a relationship between the traveling state of the own vehicle and the situation around the own vehicle when the acceleration / deceleration operation is detected by the acceleration / deceleration operation detecting means Driving characteristic grasping means for classifying characteristics into driving characteristics and driving assistance for the driver by controlling the in-vehicle device based on at least one element characteristic in the driving characteristics grasped by the driving characteristic grasping means Driving support means. As a result, it is possible to detect the acceleration / deceleration operation of the vehicle by the driver from the accelerator pedal or the brake pedal, appropriately grasp the driving characteristics when the acceleration / deceleration operation of the vehicle is performed, and perform driving support. It can be a car.

(13) The acceleration / deceleration operation detection step for detecting the acceleration / deceleration operation by the driver, and the relationship between the traveling state of the host vehicle and the situation around the host vehicle when the acceleration / deceleration operation is detected are converted into a plurality of element characteristics. A driving characteristic grasping step for classifying and grasping driving characteristics, and a driving support step for performing driving assistance for the driver based on at least one element characteristic in the grasped driving characteristics. Thereby, it can be set as the driving assistance method which can grasp | ascertain appropriately the driving characteristic at the time of acceleration / deceleration operation of a vehicle, and can perform driving assistance.

(Second Embodiment)
Next, a second embodiment will be described.
(Constitution)
The second embodiment is a driving support device to which the present invention is applied.
FIG. 6 is a block diagram showing the configuration of the driving support apparatus.
As shown in FIG. 6, the basic configuration of the driving support apparatus of the second embodiment is the same as the configuration of the driving support apparatus of the first embodiment shown in FIG. 1, but the second embodiment. In particular, the driving support apparatus includes the driving load estimation unit 10, the driving support providing unit 11, and the tracking state determination unit 20 with a tracking state determination unit 23. In the following description, in the driving support device of the second embodiment, the same reference numerals as those of the driving support device of the first embodiment are the same unless otherwise specified.

7 and 8 show a processing procedure in the driving support apparatus of the second embodiment.
The basic part of the processing procedure in the driving support apparatus of the second embodiment shown in FIG. 7 is the same as the processing procedure of the driving support apparatus of the first embodiment shown in FIG. In the processing of the driving support device of the embodiment, in particular, step S51 is provided instead of step S1, and step S52 is provided between step S3 and step S4. In the following description, in the processing of the driving support device of the second embodiment, the same reference numerals as those of the driving support device of the first embodiment are the same unless otherwise specified. . Moreover, the process of FIG. 7 and the process of FIG. 8 are progressing in parallel.

  As shown in FIG. 7, in step S51, the throttle opening degree detection unit 3 detects the opening degree of the throttle provided in the vehicle, and based on the detection result, whether the throttle has been opened (throttle ON). Determine whether or not. Here, it is determined that the throttle is opened when the throttle is shifted from the closed state to the opened state. In addition, the presence or absence of the driver's accelerator operation is directly detected, and when the driver performs an accelerator operation, it is determined that the throttle is in an open state, and when there is no accelerator operation by the driver, the throttle is in a closed state. You may judge. If the throttle is opened in step S51, the process proceeds to step S2.

As in the first embodiment, the inter-vehicle distance and the own vehicle speed are detected in the step S2, and the follow-up state classification holding unit 22 is referred to in the subsequent step S3. Then, the process proceeds to step S52.
In step S <b> 52, the follow-up state determination unit 23 determines the follow-up state of the host vehicle with respect to the current preceding vehicle while referring to the information held in the follow-up state classification holding unit 22.

  In step S3, the tracking state determination unit 23 refers to the tracking state classification holding unit 22, and the tracking state determination unit 23 sets the inter-vehicle distance detected in step S2 and the host vehicle speed in step S52. And the distance between a plurality of characteristic lines corresponding to each of the following states held in the following state classification holding unit 22 is calculated. Then, the tracking state determination unit 23 determines that the measurement point is closer to the tracking state corresponding to the characteristic straight line as the distance is shorter.

And the process after said step S4 is performed similarly to the said 1st Embodiment.
On the other hand, in FIG. 8, which is parallel processing with the processing of FIG. 7, in step S61, the determination result of the tracking state obtained in step S52 is input to the driving load estimation unit 10.
Subsequently, in step S62, the driving load estimation unit 10 calculates the frequency of change in a predetermined time for the following state input in step S61. For example, if the follow-up state changes 6 times in a measurement time of 30 minutes, the frequency is expressed as “5 minutes per change” (that is, a rate of change of 1 every 5 minutes on average).
Subsequently, in step S63, the driving load estimation unit 10 estimates the driving load based on the frequency of change of the tracking state calculated in step S62. Here, it is estimated that the driving load is higher as the frequency of change in the follow-up state is higher, and that the operation load is lower as the frequency of change in the follow-up state is lower.

(Operation)
Next, the operation will be described.
In the driving support device of the second embodiment, in particular, the process start condition is that the throttle is shifted from the closed state to the opened state, that is, the throttle is opened (step S51), and When the processing is started, the inter-vehicle distance detection unit 1 and the vehicle speed detection unit 2 detect the inter-vehicle distance and the own vehicle speed at that time (step S2), and the follow-up traveling classifying unit 21 includes the following state classification holding unit. 22 is referred to (step S3). In the second embodiment, the tracking state determination unit 23 calculates the distance between the latest measurement point and a plurality of characteristic lines corresponding to the tracking states held in the tracking state classification holding unit 22. The shorter the distance, the closer the measurement point is to the tracking state corresponding to the characteristic line, that is, the tracking state by the current driver has already been obtained based on the past measurement points. Then, it is determined that the state is close to a certain following state (step S52).

  On the other hand, the driving load estimation unit 10 calculates the frequency of change of the tracking state within a predetermined time, and estimates the driving load based on the calculated frequency of change of the tracking state (FIG. 8). Specifically, it is estimated that the driving load is higher as the frequency of change in the follow-up state is higher, and it is estimated that the operation load is lower as the frequency of change in the follow-up state is lower.

(Function)
Next, the operation will be described.
Each time the driver opens the accelerator from the closed state, a set of the inter-vehicle distance and the own vehicle speed at that time is accumulated as measurement points (step S2), and based on all the accumulated measurement points, a plurality of A plurality of following states expressed as a characteristic straight line can be obtained (steps S16 to S22). At this time, the characteristic straight lines are always adjusted so as to be parallel to each other (steps S21 and S22).

  Then, when the number of times the driver opens the accelerator from the closed state increases to some extent, that is, a certain number of measurement points that are a set of the inter-vehicle distance and the own vehicle speed obtained when the driver opens the accelerator are accumulated. Then, the characteristic straight line corresponding to the follow-up state is adjusted based on the latest measurement point (steps S5 to S15). For example, it is corrected so that characteristic straight lines corresponding to a plurality of follow-up states are parallel to each other (steps S8 and S9), or measurement points associated with the follow-up state to which the latest measurement points belong. When the has a further multimodal distribution, a new following state (characteristic straight line) that eliminates the multimodal distribution is generated (steps S10 to S13). Further, even when a new following state (characteristic line) is generated, the characteristic lines corresponding to a plurality of following states are corrected so as to be parallel to each other (steps S14 and S15).

Thereby, based on the driver's accelerator operation, the following state of the driver with respect to the preceding vehicle is always divided into a plurality of, and the characteristic lines corresponding to the following state are always parallel to each other.
Furthermore, in the second embodiment, the driving load is estimated based on the frequency of change in the tracking state (tracking state corresponding to the latest measurement point) close to the latest measurement point (transition state of the tracking state). . That is, paying attention to the fact that the driver's driving load increases if the driver's follow-up state changes frequently, the driving load is estimated based on the frequency of the time-dependent change in the follow-up state close to the latest measurement point. Yes. For example, thereby, the inter-vehicle distance control is performed based on a control law different from the normal control law based on the state of the driving load. In this case, based on the driving load estimated by the driving load estimating unit 10, the execution content (control law) of the inter-vehicle distance automatic control unit 8 forming the driving support providing unit 11 is changed by the execution content changing unit, and the inter-vehicle distance automatic control is performed. The unit 8 performs inter-vehicle distance control as driving assistance based on the changed execution content.

  As described above, the driver's driving load is estimated based on the frequency of change in the tracking state corresponding to the latest measurement point. This is because the frequency of change in the driver's follow-up state and the driving load of the driver are closely related. For example, the frequency of change in the follow-up state is closely related to the driver's decision making in the following situation. This is because the driver's decision-making becomes a driving load on the driver. Therefore, by estimating the driver's driving load based on the frequency of change in the tracking state corresponding to the latest measurement point, the driver's driving can be performed according to the actual driving characteristics of the driver. The load can be estimated.

  Further, it is estimated that the higher the frequency of change in the driver's follow-up state, the higher the driving load. This is because as the frequency of the change in the follow-up state increases, the driver pays more attention to decision making in the follow-up scene, and the driving load on the driver increases. For this reason, it is possible to appropriately estimate the actual driving load of the driver by estimating that the driving load is higher as the frequency of the change in the driver's follow-up state is higher.

Further, the inter-vehicle distance control is performed based on a control law different from the normal control law based on the state of the driving load (the control content of the inter-vehicle distance control is changed). As a result, inter-vehicle distance control adapted to driving characteristics in consideration of the driving load state can be realized.
Further, the driver's deceleration operation is detected when the throttle is reversed from the closed state to the open state. Thereby, the driver's deceleration operation can be easily detected.
In the description of the second embodiment, the driving load estimation unit 10 classifies the relationship between the traveling state of the host vehicle and the situation around the host vehicle into the plurality of element characteristics by the driving characteristic grasping unit. The driving load estimating means for estimating the driving load received by the driver is realized based on the transition state of the element characteristic as the classification destination.

(effect)
(1) Based on the transition state of the element characteristic that is a classification destination when the relationship between the traveling state of the host vehicle and the situation around the host vehicle is classified into the plurality of element characteristics by the driving characteristic grasping unit. The driving load estimating means for estimating the driving load received by the driver is provided. When classifying the relationship between the driving state of the host vehicle and the situation around the host vehicle into multiple element characteristics, the transition state of the element characteristic that is the classification destination and the driving load received by the driver are closely related. The driving load received by the driver can be accurately estimated based on the transition state of the element characteristic as the classification destination.

(2) The driving load estimation means estimates that the driving load received by the driver is larger as the frequency of the change of the element characteristic as the classification destination is higher. Since there is a relationship in which the driving load received by the driver increases as the frequency of changes in the element characteristics as the classification destination increases, the driving load received by the driver can be accurately estimated from this relationship. .

(3) The driving support means changes the execution content of the driving support based on the driving load estimated by the driving load estimation means. By changing the execution contents of the driving assistance based on the estimated driving load, driving assistance adapted to the driving characteristics can be performed.
(4) The acceleration / deceleration operation detecting means detects the acceleration / deceleration operation when the throttle is reversed from the closed state to the open state. As a result, the acceleration operation of the driver can be easily detected.

(Third embodiment)
Next, a third embodiment will be described.
(Constitution)
FIG. 9 is a block diagram showing the configuration of the driving support device.
As shown in FIG. 9, the basic configuration of the driving support apparatus of the third embodiment is the same as the configuration of the driving support apparatus of the first embodiment shown in FIG. 2, but the third embodiment. In particular, the driving support apparatus includes a driving skill estimation unit 12. In the following description, in the driving support device of the third embodiment, the same reference numerals as those of the driving support device of the first and second embodiments are the same unless otherwise specified. is there.

10 and 11 show a processing procedure in the driving support apparatus of the third embodiment.
The processing configuration of the driving support apparatus according to the second embodiment shown in FIG. 10 is the same as the processing configuration of the driving support apparatus according to the first embodiment shown in FIG. In the processing of the driving support apparatus according to the embodiment, step S71 and step S72 are provided in place of step S1. In the following description, in the processing of the driving support apparatus of the third embodiment, the same reference numerals as those of the driving support apparatus of the first embodiment are the same unless otherwise specified. . Moreover, the process of FIG. 10 and the process of FIG. 11 are progressing in parallel.

  As shown in FIG. 10, in step S71, the throttle opening degree detection unit 3 detects the opening degree of the throttle provided in the vehicle, and whether or not the throttle opening degree has changed based on the detection result. Determine. Here, the presence or absence of the accelerator operation by the driver is directly detected, and if the driver makes an accelerator operation, it is determined that the throttle opening is “changed”, and if there is no accelerator operation by the driver, the throttle is opened. It may be determined that “no change” each time. If there is a change in the throttle opening in step S51, the process proceeds to step S2.

Then, the same processes as those in the first embodiment, step S2, step S3, step S5 to step S7 and step S9, are performed, and the process proceeds to step S72.
In step S72, the following traveling classification unit 21 calculates the distance between the measurement point belonging to each following state and the characteristic straight line associated with each following state, and further calculates the distribution thereof.
On the other hand, in FIG. 11, which is parallel processing with the processing of FIG. 10, in step S81, the characteristic line calculated in step S9 (characteristic line that corrects the inclination and minimizes the distance to the measurement point). The number and the number of all measurement points held by the tracking state classification holding unit 22 are input to the driving skill estimation unit 21.

  Subsequently, in step S82, the driving skill estimation unit 12 estimates the driving skill of the driver based on the number of characteristic straight lines input in step S81. Here, when the number of measurement points is M and the number of characteristic straight lines generated based on all the M measurement points is n, the driving skill estimation unit 12 is 1 ≦ n <p. Is determined (estimated) that the driver is an elementary driver, and when p ≦ n <q, the driver is determined (estimated) as an intermediate driver, and when q ≦ n, It is determined (estimated) that the driver is an advanced driver. Here, p and q are threshold values for determining the skill of the driver based on the number of characteristic lines, for example, experimental values and experience values.

  Note that the processing content of FIG. 10 is different from the processing content of FIG. 2 or FIG. Specifically, in the process of FIG. 10, a process of determining whether or not there are k or more past measurement points associated with each follow-up state (step S4), and Such processing (steps S16 to S22) is not performed. However, as the third embodiment, the processing content of FIG. 2 or 7 can be executed instead of the processing content of FIG.

(Operation)
Next, the operation will be described.
In the driving support device of the third embodiment, when the throttle opening is changed (step S51), the inter-vehicle distance detection unit 1 and the vehicle speed detection unit 2 detect the inter-vehicle distance and the own vehicle speed at that time. At the same time (step S2), the following traveling classification unit 21 refers to the following state classification holding unit 22 (step S3). In particular, in the third embodiment, the number of characteristic straight lines and the number of all measurement points held by the tracking state classification holding unit 22 are input to the driving skill estimation unit 12, and the driving skill estimation unit 12 When there are M measurement points and n characteristic lines generated based on the M measurement points, and 1 ≦ n <p, the driver is a beginner driver. If p ≦ n <q, it is determined that the driver is an intermediate driver, and if q ≦ n, the driver is determined to be an advanced driver (FIG. 11). .

(Function)
Next, the operation will be described.
Each time the driver operates the accelerator (step S71), the characteristic straight line corresponding to the follow-up state is adjusted based on the measurement point that is a set of the inter-vehicle distance and the own vehicle speed at that time (see above). Step S2 to Step S15). For example, if the measurement point associated with the follow-up state to which the latest measurement point belongs has a multi-modal distribution, the multi-modality of the distribution disappears and a new one is added. A follow-up state (characteristic straight line) is generated (steps S10 to S13).

In the third embodiment, the driving skill of the driver is estimated based on the number of characteristic straight lines adjusted as described above (FIG. 11). Specifically, when the number of characteristic straight lines is large, it is determined that the driver is an advanced driver.
For example, thereby, the inter-vehicle distance control is performed based on the driving skill based on a control law different from the normal control law. In this case, based on the driving skill estimated by the driving skill estimating unit 12, the execution content (control law) of the inter-vehicle distance automatic control unit 8 constituting the driving support providing unit 11 is changed by the execution content changing means, and the inter-vehicle distance automatic control is performed. The unit 8 performs inter-vehicle distance control as driving assistance based on the changed execution content.

As described above, the driving skill is estimated (determined) based on the number of characteristic straight lines. As a result, the number of characteristic lines usually varies depending on the driver, so that the driving skill of the driver can be estimated appropriately for each driver.
Specifically, it is estimated that the driving skill increases as the number of characteristic lines increases. The fact that there are many characteristic straight lines means that the driver can have various driving characteristics and the driving experience is rich and the driving skill is high. For this reason, the driving skill of the driver can be estimated in accordance with the actual driving characteristics of the driver by estimating that the driving skill is higher as the number of characteristic lines increases.
Further, based on the driving skill, the inter-vehicle distance control is performed with a control law different from the normal control law. As a result, inter-vehicle distance control adapted to driving characteristics in consideration of driving skills can be realized.
Further, the driver's deceleration operation is detected based on the throttle opening. Thereby, the driver's deceleration operation can be easily detected.

The present invention can also be realized by the following configuration.
That is, it is possible to estimate the driving skill of the driver based on the degree of variation of the measurement points (a set of the inter-vehicle distance and the own vehicle speed) that the tracking state (characteristic straight line) has. For example, it is estimated that the driving skill increases as the degree of variation in the measurement points (a set of inter-vehicle distance and own vehicle speed) with respect to the following state (characteristic straight line) decreases. For example, when the degree of variation (degree of distribution) between the measurement points constituting the tracking state (characteristic line) is low, it can be estimated that the driving skill is high because the driving characteristic is stable. Thereby, a driver | operator's driving skill can be estimated appropriately.

  In addition, the driving skill that can be estimated by the degree of variation of the measurement points (a set of the inter-vehicle distance and the own vehicle speed) with respect to the following state (characteristic straight line), and the driving skill that can be estimated by the number of the characteristic straight lines described above, For example, the execution content (control law) of the inter-vehicle distance automatic control unit 8 can be changed based on, for example, one of the driving skills or based on the weighted driving skills. By doing in this way, a driving skill can be estimated appropriately and the inter-vehicle distance control based on the driving skill can be performed.

  In the description of the third embodiment, the driving addition estimation unit 10 determines that the relationship between the traveling state of the own vehicle classified by the driving characteristic grasping means and the situation around the own vehicle is the element characteristic of the classification destination. And a driving state of the host vehicle when the acceleration / deceleration operation is detected by the acceleration / deceleration operation detecting means, based on the degree of variation of As a result of classifying the relationship with the situation around the host vehicle, second driving skill estimating means for estimating the driving skill of the driver is realized based on the number of element characteristics that are the classification destinations.

(effect)
(1) The driving skill of the driver based on the degree of variation that the relationship between the traveling state of the own vehicle classified by the driving characteristic grasping means and the situation around the own vehicle has with respect to the element characteristic of the classification destination First driving skill estimation means for estimating Since the relationship between the traveling state of the own vehicle and the situation around the own vehicle is closely related to the element characteristics of the classification destination and the driving skill of the driver, the traveling state of the own vehicle and the own vehicle The driver's driving skill can be appropriately estimated from the degree of variation that the relationship with the surrounding situation has with respect to the element characteristic that is the classification destination.

(2) As a result of classifying the relationship between the traveling state of the host vehicle when the acceleration / deceleration operation is detected by the acceleration / deceleration operation detecting unit and the situation around the host vehicle, the number of element characteristics as a classification destination is determined. And a second driving skill estimating means for estimating the driving skill of the driver. As a result of classifying the relationship between the running state of the host vehicle when the acceleration / deceleration operation is performed and the situation around the host vehicle, the number of element characteristics that are the classification destination and the driving skill of the driver are closely related. The driving skill of the driver can be appropriately estimated based on the number of element characteristics that are the classification destinations.

(3) The driving support means changes the execution contents of the driving support based on the driving skill estimated by at least one of the first driving skill estimation means and the second driving skill estimation means. As a result, the optimum driving skill can be estimated, and the driving assistance adapted to the driving characteristics can be performed by changing the execution contents of the driving assistance based on the estimated driving skill.
(4) The acceleration / deceleration operation detecting means detects the acceleration / deceleration operation when the throttle opening changes. As a result, the acceleration operation of the driver can be easily detected.

The first to third embodiments of the present invention have been described above. However, the present invention can also be realized by the following configuration.
That is, in the first to third embodiments, the driving support device has been described individually. On the other hand, it can also be set as the structure which combined the driving assistance device of the 1st-3rd embodiment. For example, as shown in FIG. 12, it can also be set as the driving assistance apparatus provided with all the structures shown as the 1st-3rd embodiment. Thereby, the effect acquired by the said 1st-3rd embodiment can be acquired with one driving assistance device.

Moreover, the said 1st-3rd embodiment demonstrated the case where one following state was estimated based on a road attribute and traffic volume. On the other hand, one following state can also be estimated based on at least one of the road attribute and the traffic volume. In this case, if at least one of the road attribute and the traffic volume is known, one following state can be estimated.
Moreover, the said 1st-3rd embodiment demonstrated the case where this invention was applied to a driving assistance device. On the other hand, it is determined whether the latest measurement point belongs to one of a plurality of types of tracking states, that is, one of a plurality of types of tracking states based on the latest measurement point. It can also be limited to determining the following state.

It is a block diagram which shows the structure of the driving assistance device of 1st Embodiment which concerns on this invention. 4 is a flowchart illustrating a processing procedure when an inter-vehicle distance automatic control unit is in an off state in the driving support device according to the first embodiment. 4 is a flowchart illustrating a processing procedure when an inter-vehicle distance automatic control unit is in an on state in the driving support device according to the first embodiment. It is a characteristic view which shows the relationship between the own vehicle speed and the inter-vehicle distance with a preceding vehicle at the time of throttle release. It is a figure which shows an image in case there are k past measurement points linked | related with each following state 1-N. It is a block diagram which shows the structure of the driving assistance device of 2nd Embodiment which concerns on this invention. It is a flowchart which shows the process sequence which calculates a tracking state based on the measurement point in the driving assistance device of the said 2nd Embodiment. It is a flowchart which shows the process sequence which estimates a driving | running load based on the tracking state obtained based on the measurement point in the driving assistance device of the said 2nd Embodiment. It is a block diagram which shows the structure of the driving assistance device of 3rd Embodiment which concerns on this invention. It is a flowchart which shows the process sequence which calculates a tracking state based on the measurement point in the driving assistance device of the said 3rd Embodiment. In the driving support device of the 3rd embodiment, it is a flow chart which shows a processing procedure which presumes a driving skill based on the number of following states acquired based on a measurement point. It is a block diagram which shows the driving assistance apparatus which consists of a combination of the driving assistance apparatus of the said 1st-3rd embodiment.

Explanation of symbols

  1 vehicle distance detection unit, 2 vehicle speed detection unit, 3 throttle opening detection unit, 4 road attribute detection unit, 5 traffic volume detection unit, 6 tracking state estimation unit, 7 control law adjustment unit, 8 inter-vehicle distance automatic control unit, DESCRIPTION OF SYMBOLS 10 Driving load estimation part, 11 Driving assistance provision part, 12 Driving skill estimation part, 20 Following state determination part, 21 Following running classification part, 22 Following state classification holding part, 23 Following state determination part

Claims (19)

Traveling state detection means for detecting the traveling state of the host vehicle;
Ambient situation detection means for detecting the situation around the host vehicle,
Acceleration / deceleration operation detecting means for detecting an acceleration / deceleration operation for the host vehicle;
Driving characteristic grasping means for classifying the relationship between the traveling state of the own vehicle when the acceleration / deceleration operation detecting means is detected and the situation around the own vehicle into a plurality of element characteristics to grasp the driving characteristic; ,
Based on at least one element characteristic in the driving characteristics grasped by the driving characteristic grasping means, driving assistance means for performing driving assistance for the driver;
A driving support apparatus comprising: The driving characteristic grasping means holds a plurality of element characteristics, and the driving state of the own vehicle when the acceleration / deceleration operation detecting means is detected by the acceleration / deceleration operation detecting means among the plurality of held element characteristics and the own vehicle Select the element characteristics that most closely relate to the surrounding situation,
The driving support apparatus according to claim 1, wherein the driving support means performs driving support for the driver based on the element characteristic selected by the driving characteristic grasping means. The driving characteristic grasping means classifies each relationship between the traveling state of the host vehicle and the situation around the host vehicle when the acceleration / deceleration operation is detected in past driving into a plurality of groups according to the degree of similarity to each other. And calculating the characteristics of each classified group as the plurality of element characteristics,
The driving support apparatus according to claim 1, wherein the driving support means performs driving support for the driver based on the element characteristic calculated by the driving characteristic grasping means. The traveling state detecting means detects the own vehicle speed as the traveling state,
The driving assistance apparatus according to any one of claims 1 to 3, wherein the surrounding state detection unit detects an inter-vehicle distance from a preceding vehicle as a state around the host vehicle.   The driving characteristic grasping means returns to a plurality of straight lines having the same inclination when the vehicle speed and the inter-vehicle distance when the acceleration / deceleration operation is detected are developed on coordinate planes corresponding to two orthogonal axes, respectively. The driving support device according to claim 4, wherein the relationship between the own vehicle speed and the inter-vehicle distance is classified into a plurality of element characteristics.   The driving support apparatus according to claim 5, further comprising a correction unit that corrects a relationship between the plurality of straight lines at a timing when the acceleration / deceleration operation detection unit detects an acceleration / deceleration operation on the host vehicle.   The driving support device according to any one of claims 1 to 6, wherein the acceleration / deceleration operation detecting means detects the acceleration / deceleration operation based on an open / close state of a throttle for adjusting an engine output.   The acceleration / deceleration operation detecting means is at least one of a case where a throttle for adjusting engine output is reversed from an open state to a closed state, a case where the throttle is reversed from a closed state to an open state, and a case where the throttle opening is changed. 8 is detected as the acceleration / deceleration operation. The driving assistance apparatus according to claim 1, wherein the acceleration / deceleration operation is detected.   The driving support means, based on any of a plurality of element characteristics indicating the relationship between the traveling state of the own vehicle and the situation around the own vehicle classified by the driving characteristic grasping means, The driving support device according to any one of claims 4 to 6, wherein the inter-vehicle distance is controlled.   The situation around the host vehicle includes at least one of the number of lanes, the gradient, the lane width, and the traffic volume of the travel path of the host vehicle. Driving assistance device.   Based on the transition state of the element characteristic as a classification destination when the relationship between the traveling state of the host vehicle and the situation around the host vehicle is classified into the plurality of element characteristics by the driving characteristic grasping means The driving support apparatus according to claim 1, further comprising a driving load estimation unit that estimates a driving load received by the vehicle.   12. The driving support apparatus according to claim 11, wherein the driving load estimating means estimates that the driving load received by the driver is larger as the frequency of change of the element characteristic as the classification destination is higher.   The driving support device according to claim 11 or 12, wherein the driving support means changes the execution contents of the driving support based on the driving load estimated by the driving load estimation means.   The driver's driving skill is estimated based on the degree of variation that the relationship between the traveling state of the own vehicle classified by the driving characteristic grasping means and the situation around the own vehicle has with respect to the element characteristic of the classification destination. The driving support apparatus according to claim 1, further comprising first driving skill estimation means.   As a result of classifying the relationship between the traveling state of the own vehicle and the situation around the own vehicle when the acceleration / deceleration operation detecting means is detected by the acceleration / deceleration operation detecting means, the driver The driving support device according to claim 1, further comprising second driving skill estimation means for estimating the driving skill of the vehicle.   The driving support means changes execution contents of the driving support based on a driving skill estimated by at least one of the first driving skill estimating means and the second driving skill estimating means. Item 16. The driving support device according to Item 14 or 15.   A driving support device characterized in that a driving behavior of a driver is classified into a plurality of element characteristics corresponding to a situation around the host vehicle, the driving characteristics are grasped, and driving support is performed based on the driving characteristics. An accelerator pedal for acceleration operation,
A brake pedal to be decelerated,
Traveling state detection means for detecting the traveling state of the host vehicle;
Ambient situation detection means for detecting the situation around the host vehicle,
Acceleration / deceleration operation detecting means for detecting an acceleration / deceleration operation for the host vehicle;
Driving characteristic grasping means for classifying the relationship between the traveling state of the own vehicle when the acceleration / deceleration operation detecting means is detected and the situation around the own vehicle into a plurality of element characteristics to grasp the driving characteristic; ,
Based on at least one of the element characteristics in the driving characteristics grasped by the driving characteristics grasping means, driving assistance means for driving assistance for the driver by controlling the in-vehicle device;
An automobile characterized by comprising: An acceleration / deceleration operation detection step for detecting an acceleration / deceleration operation by the driver;
A driving characteristic grasping step for classifying the relationship between the traveling state of the own vehicle when the acceleration / deceleration operation is detected and the situation around the own vehicle into a plurality of element characteristics and grasping the driving characteristic;
A driving support step for performing driving support for the driver based on at least one of the element characteristics in the grasped driving characteristics;
A driving support method comprising:

Images