JP6330291B2 - Safe driving degree judgment device - Google Patents

Description

  The present invention relates to a safe driving degree determination device.

  As this type of technique, a technique described in Patent Document 1 below is disclosed. This document discloses one that measures vehicle speed, acceleration, steering angle, and the like at the time of turning left and right at an intersection and compares these measurement data with safety reference data to perform driving diagnosis.

Japanese Patent No. 3593502

In the technology described in Patent Document 1, driving technology can be diagnosed, but it cannot be diagnosed as to whether the driver is actually performing safe driving, and the driver has a risk of traveling road conditions. There is a problem that it is difficult to determine what kind of tendency it has against (contact with other vehicles, pedestrians, obstacles, etc.) and it is difficult to obtain a driver's satisfaction in the diagnosis result. It was.
The present invention pays attention to the above-mentioned problem, and an object thereof is to provide a safe driving degree determination device capable of determining a driver's risk tendency.

In the present invention in order to solve the above problems, it calculates the index representing the height of the potential risks sensitivity to predict the risk invisible driver from the travel information, the indicator of the level of evasion of risk to approach, setting period The risk tendency of the driver is determined based on the calculated index value.

  Therefore, the driver's risk tendency can be determined.

1 is a block diagram of a risk tendency determination device of Example 1. FIG. 6 is a flowchart showing a flow of risk tendency determination processing executed in the controller of the first embodiment. FIG. 3 is a diagram illustrating a calculation method of a vehicle speed integral value and a maximum deceleration according to the first embodiment. 3 is a graph showing a frequency distribution of a vehicle speed integrated value and a maximum deceleration of Example 1. 6 is a map for determining a driver's risk tendency based on a potential risk sensitivity index and an avoidance ability index according to the first embodiment. 10 is a flowchart showing a flow of risk tendency determination processing executed in the controller of the second embodiment. FIG. 6 is a diagram illustrating a method for calculating a minimum vehicle speed and a maximum acceleration according to the second embodiment. 6 is a graph showing a frequency distribution of minimum vehicle speed and maximum acceleration of Example 2. 6 is a map for determining a driver's risk tendency based on a potential risk sensitivity index and an avoidance ability index according to the second embodiment. 12 is a flowchart showing a flow of risk tendency determination processing executed in the controller of the third embodiment. FIG. 6 is a diagram illustrating a method for calculating a vehicle speed integral value according to a third embodiment. FIG. 10 is a block diagram of a risk tendency determination device according to a fourth embodiment. 10 is a flowchart showing a flow of risk tendency determination processing executed in the controller of the fourth embodiment. FIG. 10 is a diagram illustrating a calculation method of the maximum yaw rate and the maximum lateral acceleration according to the fourth embodiment. FIG. 10 is a block diagram of a risk tendency determination device according to a fifth embodiment. 10 is a flowchart showing a flow of risk tendency determination processing executed in a controller of Example 5.

Example 1
The driver risk tendency determination device 20 according to the first embodiment will be described.
[overall structure]
FIG. 1 is a block diagram of the driver risk tendency determination device 20.
The driver risk tendency determination device 20 includes a vehicle speed detection unit 1, an acceleration speed detection unit 2, a vehicle position detection unit 3, a controller 4, a map database 5, a notification unit 9, a read only memory (hereinafter referred to as ROM) 12, and a random access memory. (Hereinafter referred to as RAM) 13. These are mounted on the vehicle.

  The vehicle speed detection unit 1 detects the current vehicle speed V of the host vehicle and outputs information on the detected current vehicle speed V to the controller 4. As the vehicle speed detection unit 1, for example, a vehicle speed sensor that detects the vehicle speed based on the number of rotations of the wheels of the host vehicle is used.

  The acceleration speed detection unit 2 detects the current acceleration G of the host vehicle, and outputs information on the detected current acceleration G to the controller 4. As the acceleration speed detection unit 2, for example, an acceleration sensor capable of detecting longitudinal acceleration, deceleration, and lateral acceleration applied to the vehicle is used.

  The vehicle position detection unit 3 detects the current position of the host vehicle and outputs the detected current position information of the host vehicle to the controller 4. As the vehicle position detection unit 3, for example, a Global Positioning System (hereinafter referred to as GPS) receiver is used.

  The map database 5 records road information, map information such as intersections, and the like. The intersection information includes, for example, information on the presence / absence of a traffic light and whether or not the intersection needs to be temporarily stopped (hereinafter referred to as a temporarily stopped intersection).

For example, the notification unit 9 performs notification with signal sound, voice, video, or the like using a monitor or a speaker.
The ROM 12 is a memory that can only read recorded information.
The RAM 13 is a memory that can read recorded information and write information.

[Controller configuration]
The controller 4 is configured as a driver risk tendency determination unit, and has an intersection approach determination unit 6, a potential risk sensitivity index calculation unit 7, an avoidance ability index calculation unit 8, an index calculation value recording unit 9, and a risk trend determination unit 10. ing.

  Based on the information on the current position of the vehicle detected by the vehicle position detection unit 3 and the intersection information in the map database 5, the intersection approach determination unit 6 determines that the host vehicle is approaching and entering the temporarily stopped intersection. And the information (approaching approach information) is output to the potential risk sensitivity index calculation unit 7 and the avoidance capability index calculation unit 8.

  The potential risk sensitivity index calculation unit 7 receives the approach approach information to the temporarily stopped intersection, and calculates the potential risk sensitivity index based on the vehicle speed V when approaching and approaching the temporarily stopped intersection detected by the vehicle speed detection unit 1. Then, the information of the potential risk sensitivity index calculation value is output to the index calculation value recording unit 9.

  The avoidance capability index calculation unit 8 receives the approach approach information to the temporarily stopped intersection, calculates the avoidance capability index based on the deceleration detected by the acceleration speed detection unit 2, and calculates the avoidance capability index calculation value. Information is output to the index calculation value recording unit 9.

  The index calculation value recording unit 9 records information on the latent risk sensitivity index calculation value and the avoidance ability index calculation value. As the index calculation value recording unit 9, for example, a hard disk or RAM is used.

  The risk tendency determination unit 10 reads the potential risk sensitivity index calculation value and the avoidance ability index calculation value recorded in the index calculation value recording unit 9 for a set period, and determines the driver's risk tendency. Further, based on the determined risk tendency information, a notification command for notifying the determination result of the risk tendency of the driver of the host vehicle is output to the notification unit 9.

  The notification unit 9 notifies the determination result of the risk tendency of the driver of the host vehicle based on the notification command output from the risk tendency determination unit 10.

[Driver risk tendency judgment process]
FIG. 2 is a flowchart showing the flow of the driver risk tendency determination process executed in the controller 4.

  In step S1, the intersection approach determination unit 6 determines whether or not the host vehicle has approached the temporarily stopped intersection based on the current position of the host vehicle detected by the vehicle position detection unit 3 and the intersection information in the map database 5. . Specifically, the approach to the temporary stop intersection is determined based on whether or not the host vehicle is within a preset range of the temporary stop intersection (for example, within a radius of 30 [m] from the center of the temporary stop intersection). This setting range is set in consideration of the road width of the paused intersection and the GPS position accuracy. When it is determined that the host vehicle is within the set range of the temporary stop intersection (Yes), the process proceeds to step S2. If it is determined that the host vehicle is outside the setting range of the temporary stop intersection (No), it is determined that the vehicle is not approaching the temporary stop intersection, and this determination is performed again.

  In step S2, the potential risk sensitivity index calculation unit 7 records time series data V (x) of the vehicle speed V when the host vehicle enters the temporary stop intersection, and the avoidance capability index calculation unit 8 records the host vehicle at the temporary stop intersection. The time series data G (t) of the acceleration G when entering the vehicle is recorded, and the process proceeds to step S3.

  Specifically, the potential risk sensitivity index calculation unit 7 and the avoidance capability index calculation unit 8 first determine time-series data V (V) of the vehicle speed V from time t1 when it is determined in step S1 that the host vehicle has approached the temporary stop intersection. Recording of time series data G (t) of x) and acceleration G is started. The sampling time of the time series data V (x) and the time series data G (t) is, for example, 10 [msec].

  Subsequently, the potential risk sensitivity index calculation unit 7 and the avoidance capability index calculation unit 8 determine whether or not the host vehicle has passed the temporary stop intersection, and at time t2 when it is determined that the vehicle has passed, the time series data V ( Recording of x) and time-series data G (t) is terminated, and the process proceeds to step S3. When it is determined that the host vehicle has not passed the temporary stop intersection, the time series data V (x) and time series data G (t) are continuously recorded, and this determination is performed again.

  In step S3, the potential risk sensitivity index calculation unit 7 records the time series data V (x) of the vehicle speed V from the point x1 at time t1 when the host vehicle entered the temporary stop intersection to the point x2 at time t2 when the vehicle passed. From the above, it is determined whether or not the minimum vehicle speed Vmin in the intersection approach section is equal to or less than the set value Vs. When it is determined that the minimum vehicle speed Vmin is equal to or lower than the set value Vs (Yes), the process proceeds to step S4. If it is determined that the minimum vehicle speed Vmin is greater than the set value Vs (No), the process proceeds to step S5.

In step S4, the potential risk sensitivity index calculation unit 7 calculates the integrated value Iv (vehicle speed integrated value Iv) of the time series data V (x) from the point x1 to the point x2, and the process proceeds to step S6. FIG. 3 is a diagram showing a calculation method of the vehicle speed integral value Iv and the maximum deceleration Gmax described later. FIG. 3 (a) is a diagram illustrating a situation in which the host vehicle enters a temporary stop intersection. FIG. 3 (b) is a graph showing time-series data V (x) of the vehicle speed V. FIG. 3C is a graph showing time series data G (t) of acceleration G. Specifically, data that is equal to or lower than the set value Vs is extracted from the time-series data V (x) from the point x1 to the point x2. The difference between the extracted time series data V (x) and the set value Vs is obtained, and this integral value is calculated. The vehicle speed integral value Iv is represented by the filled area in FIG. 3 (b) and is obtained by the following equation (1).

  In step S5, the potential risk sensitivity index calculation unit 7 simply calculates the vehicle speed integrated value Iv as the difference between the set value Vs and the minimum vehicle speed Vmin (Iv = Vs-Vmin), and the vehicle speed integrated value Iv is a negative value. And the process proceeds to step S6.

  In step S6, the avoidance capability index calculation unit 8 calculates the maximum deceleration Gmax in the temporary stop intersection approach section, and the process proceeds to step S7. As shown in Fig. 3 (c), the specific calculation method is to calculate the negative maximum value from time series data G (t) of acceleration G from time t1 when the host vehicle entered the temporary stop intersection to time t2 when it passed. Extracted as maximum deceleration Gmax.

  In step S7, the index calculation value recording unit 9 stores the vehicle speed integral value Iv calculated in step S4 or step S5 and the maximum deceleration Gmax calculated in step S6, and the process proceeds to step S8.

The process of step S8 to step S11 is performed in the risk tendency determination unit 10.
In step S8, the vehicle speed integrated value Iv and the maximum deceleration Gmax for the set period recorded in the index calculation value recording unit 9 are extracted, and the process proceeds to step S9. The set period is a predetermined period. For example, the set period may be the period from the start of driving the host vehicle to the end, or the set period may be a part of the time from the start of driving the host vehicle to the end. In addition, a predetermined time and number of days may be set as the setting period. In other words, the set period is a period set to collect a certain number of data of the vehicle speed integrated value Iv and the maximum deceleration Gmax for each passage through the temporarily stopped intersection.

  The processing of step S8 to step S10 is not performed every time the vehicle speed integrated value Iv and the maximum deceleration Gmax are calculated, but may be performed after the number of data of the vehicle speed integrated value Iv and the maximum deceleration Gmax is uniform to some extent. good.

  In step S9, 75% tile value IV and 75% tile value IG are represented as representative values of the vehicle speed integral value Iv and the maximum deceleration Gmax of the driver of the vehicle from the distribution of the vehicle speed integral value Iv and the maximum deceleration Gmax. Extract and move to step S10. 4 is a graph showing the frequency distribution of the vehicle speed integral value Iv (FIG. 4 (a)) and the frequency distribution of the maximum deceleration Gmax (FIG. 4 (b). Specifically, as shown in FIG. Based on the frequency distribution of the integral value Iv and the maximum deceleration Gmax, the vehicle speed integral value Iv and the maximum deceleration Gmax located in the 75% tile on the dangerous side are used as the driving behavior index of the driver.

In step S10, the driver's risk tendency is determined based on the representative value IV (potential risk sensitivity index IV) of the vehicle speed integral value Iv and the representative value IG (avoidance ability index IG) of the maximum deceleration Gmax, and the process proceeds to step S11. Transition. FIG. 5 is a map for determining a driver's risk tendency based on the latent risk sensitivity index IV and the avoidance ability index IG. As shown in FIG. 5, in Example 1, risk trends are classified into the following three types.
・ Low risk tendency: Potential risk sensitivity index IV is more than the predetermined value IVs ・ Medium risk tendency: Potential risk sensitivity index IV is less than the predetermined value IVs and avoidance ability index IG is more than the predetermined value IGs ・ High risk tendency: Potential risk sensitivity index IV Is less than the predetermined value IVs and the avoidance ability index IG is less than the predetermined value IGs. By applying the results of the potential risk sensitivity index IV and the avoidance ability index IG for each driver to the above determination conditions, the driver's risk tendency is determined.
In step S11, a notification command for notifying the driver of the risk tendency determination result is output to the notification unit 9.

[Driver risk tendency judgment operation]
When the host vehicle is not approaching the temporary stop intersection, the process of step S1 is repeated.
When the host vehicle approaches the temporary stop intersection, the process proceeds from step S1 to step S2. Time series data V (x) of vehicle speed V and time series data of acceleration G from time t1 when it is determined that the host vehicle approaches the temporary stop intersection to time t2 when it is determined that the vehicle has passed the temporary stop intersection Record G (t).

  After time t2, the process proceeds to step S3, and when the minimum vehicle speed Vmin in the intersection approach section is equal to or less than the set value Vs, the vehicle speed integrated value Iv is calculated in step S4. When the minimum vehicle speed Vmin in the intersection approach section is larger than the set value Vs, the vehicle speed integrated value Iv is calculated in step S5.

  After calculating the vehicle speed integral value Iv, the maximum deceleration Gmax is calculated in step S6.

  When the vehicle speed integral value Iv and the maximum deceleration Gmax are recorded in the set period, the process proceeds from step S7 → step S8 → step S9 → step S10 → step S11. The risk tendency of the driver is determined based on the potential risk sensitivity index IV obtained from the vehicle speed integral value Iv in the set period and the avoidance ability index IG obtained from the maximum deceleration Gmax, and the determination result is notified by the notification unit 9.

[Action]
Even a driver with high driving skill cannot say that a driver with low sensitivity to potential risks is driving safely. The potential risk refers to a situation at a point that is not currently visible to the driver, such as a pedestrian at the corner of a temporary stop. On the other hand, it can be said that a driver who has high sensitivity to potential risk is performing safe driving even if the driver has low driving skill.

  Therefore, in Example 1, the driver's potential risk sensitivity index IV and the avoidance ability index IG are calculated from the driving information, and the driver's risk is calculated based on the driver's potential risk sensitivity index IV and avoidance ability index IG within the set period. The tendency was judged.

  The potential risk sensitivity index IV is obtained from the vehicle speed integrated value Iv. The vehicle speed integral value Iv increases when the driver slowly approaches the temporary stop intersection, makes a temporary stop, and starts slowly. That is, the degree of deceleration, the degree of stopping, and the degree of acceleration when approaching a temporarily stopped intersection are shown. The avoidance capability index IG is obtained from the maximum deceleration Gmax. The maximum deceleration Gmax is indicated as a large deceleration when the driver can reliably apply the sudden braking. That is, it shows that a sudden operation can be performed.

  Accordingly, the driver's risk tendency can be determined from both the sensitivity to the invisible risk (latent risk) and the avoidance ability against the approaching risk as well as the driver's simple determination of the driving skill.

In Example 1, as a risk tendency of the driver, a high risk tendency, a medium risk tendency, and a low risk tendency are determined.
High-risk trends have low driver sensitivity sensitivity and low avoidance capability. In other words, a driver with this tendency cannot predict an invisible risk and has a low ability to avoid even if the risk approaches, so it can be said that the driver is likely to cause an accident.

  The medium risk tendency is high in avoidance ability although the driver's sensitivity to potential risk is low. In other words, a driver with this tendency cannot predict an invisible risk, but can avoid it when the risk approaches, so it is a driver who faces a risk but is unlikely to lead to an accident (so-called near-miss). I can say that.

  The low risk trend is more sensitive to the driver's potential risk. In other words, a driver with this tendency can predict an invisible risk, and thus can be said to be a driver who rarely faces the risk. The driver's risk tendency can be classified into the above three trends in the order of ease of connection to the accident from both the sensitivity to the invisible risk and the avoidance ability for the approaching risk.

  In the first embodiment, the potential risk sensitivity index calculation unit 7 calculates the vehicle speed integrated value Iv when entering the temporarily stopped intersection as the driver's potential risk sensitivity index IV. It can be said that the driver who approaches slowly at the temporary stop intersection, stops temporarily, and starts slowly has high sensitivity to invisible risks. By using the vehicle speed integral value Iv at the time of approaching a temporarily stopped intersection where the driver's potential risk sensitivity is likely to appear, the accuracy of the driver's potential risk sensitivity index IV can be increased.

  In the first embodiment, the avoidance capability index calculation unit 8 calculates the deceleration G when approaching the temporarily stopped intersection as the driver's avoidance capability index IG. It can be said that a driver who can step on a sudden brake when the risk approaches approaches has high ability to avoid the risk. By using the deceleration G at the time of approaching the temporary stop intersection where the driver's avoidance ability tends to appear, the accuracy of the driver's avoidance ability index IG can be improved.

[effect]
(1) Vehicle speed detection unit 1 and vehicle speed detection unit 2 (travel information detection unit) that detect vehicle speed V and acceleration G (vehicle travel information), and when approaching a temporary stop intersection determined based on detected vehicle speed V The potential risk sensitivity index calculation unit 7 that calculates the driver's potential risk sensitivity index IV (potential risk sensitivity index) from the degree of stoppage, and the driver's avoidance ability based on the driver's sudden operation degree calculated based on the acceleration G An avoidance ability index calculation unit 8 for calculating an index IG (avoidance ability index), an index calculation value recording unit 9 for recording the calculated potential risk sensitivity index IV and an avoidance ability index IG, and an index calculation value recording unit 9 And a risk tendency determination unit 10 that determines a risk tendency of the driver based on the latent risk sensitivity index IV and the avoidance ability index IG (index calculation value) within the set setting period.
Therefore, the driver's risk tendency can be determined from both the potential risk sensitivity to the invisible risk and the avoidance ability to the approaching risk.

(2) The risk tendency determination unit 10 determines a high risk tendency, a medium risk tendency, and a low risk tendency as the driver's risk tendency.
Therefore, the driver's risk tendency can be classified into three tendencies in the order of ease of connection to the accident from both the potential risk sensitivity to the invisible risk and the avoidance ability for the approaching risk.

(3) The potential risk sensitivity index calculation unit 7 calculates the vehicle speed integral value Iv when entering the temporarily stopped intersection as the driver's potential risk sensitivity index IV.
Therefore, the accuracy of the driver's potential risk sensitivity index IV can be increased.

(4) The avoidance ability index calculation unit 8 calculates the deceleration G when approaching the temporarily stopped intersection as the avoidance ability index IG of the driver.
Therefore, the accuracy of the driver's avoidance ability index IG can be increased.

(Example 2)
The driver risk tendency determination device 20 of the second embodiment will be described. The second embodiment differs from the first embodiment in the driver risk tendency determination process executed in the controller 4. The same components as those in the first embodiment are denoted by the same reference numerals and description thereof is omitted.

[Driver risk tendency judgment process]
FIG. 6 is a flowchart showing the flow of the driver risk tendency determination process executed in the controller 4.

  In step S21, the intersection approach determination unit 6 determines whether the host vehicle has approached the temporarily stopped intersection based on the current position of the host vehicle detected by the vehicle position detection unit 3 and the intersection information in the map database 5. . A specific determination method is the same as step S1 in the first embodiment. When it is determined that the host vehicle is within the set range of the temporary stop intersection (Yes), the process proceeds to step S2. If it is determined that the host vehicle is outside the setting range of the temporary stop intersection (No), it is determined that the vehicle is not approaching the temporary stop intersection, and this determination is performed again.

  In step S22, the potential risk sensitivity index calculation unit 7 records time-series data V (t) of the vehicle speed V when the host vehicle enters the temporary stop intersection, and the avoidance capability index calculation unit 8 determines that the host vehicle is a temporary stop intersection. The time series data G (t) of the acceleration G when entering the vehicle is recorded, and the process proceeds to step S23. A specific recording method is the same as that in step S2 of the first embodiment.

  In step S23, the potential risk sensitivity index calculation unit 7 calculates the minimum vehicle speed Vmin in the intersection approach section, and the process proceeds to step S24. FIG. 7 is a diagram showing a calculation method of the minimum vehicle speed Vmin and the maximum acceleration Gmaxa described later. FIG. 7 (a) is a diagram illustrating a situation in which the host vehicle enters a temporarily stopped intersection. FIG. 7 (b) is a graph showing time-series data V (t) of the vehicle speed V. FIG. 7 (c) is a graph showing time series data G (t) of acceleration G. FIG. As shown in Fig. 7 (b), the specific calculation method is to calculate the minimum value from the time series data V (t) of the vehicle speed V from the time t1 when the host vehicle entered the temporary stop intersection to the time t2 when it passed. Extracted as Vmin and proceeds to step S24.

  In step S24, the avoidance ability index calculation unit 8 calculates the maximum acceleration Gmaxa in the intersection approach section. As shown in Fig. 7 (c), the specific calculation method is to calculate the maximum acceleration from the time series data G (t) of acceleration G from time t1 when the vehicle entered the temporary stop intersection to time t2 when it passed. Extract as Gmaxa and proceed to Step S25.

  In step S25, the index calculation value recording unit 9 stores the minimum vehicle speed Vmin calculated in step S23 and the maximum acceleration Gmaxa calculated in step S24, and the process proceeds to step S26.

The process of step S26 to step S29 is performed in the risk tendency determination unit 10.
In step S26, data of the minimum vehicle speed Vmin and the maximum acceleration Gmaxa in the set period recorded in the index calculation value recording unit 9 is extracted, and the process proceeds to step S27. Similar to the description in the first embodiment, the set period is a period set for collecting a certain number of data of the minimum vehicle speed Vmin and the maximum acceleration Gmaxa at each temporary stop intersection passage.

  The processing from step S26 to step S29 is not performed every time the minimum vehicle speed Vmin and the maximum acceleration Gmaxa are calculated, but may be performed after the number of data of the minimum vehicle speed Vmin and the maximum acceleration Gmaxa is obtained to some extent.

  In step S27, 75% tile value IVm and 75% tile value IGa are extracted as representative values of minimum vehicle speed Vmin and maximum acceleration Gmaxa of the driver of the host vehicle from the distribution of data of minimum vehicle speed Vmin and maximum acceleration Gmaxa. Move to S28. Fig. 8 is a graph showing the frequency distribution of scoring vehicle speed Vmin (Fig. 8 (a)) and the frequency distribution of maximum acceleration Gmaxa (Fig. 8 (b). Specifically, as shown in Fig. 8, the minimum vehicle speed Vmin is shown. From the frequency distribution of the maximum acceleration Gmaxa, the minimum vehicle speed Vmin and the maximum acceleration Gmaxa located on the 75% tile on the dangerous side are used as the driving behavior index of the driver.

In step S28, the driver's risk tendency is determined based on the representative value IVm of the minimum vehicle speed Vmin (latent risk sensitivity index IVm) and the representative value IGa of the maximum acceleration Gmaxa (avoidance ability index IGa), and the process proceeds to step S29. . FIG. 9 is a map for determining a driver's risk tendency based on the latent risk sensitivity index IVm and the avoidance ability index IGa. As shown in FIG. 9, in the second embodiment, risk trends are classified into the following three types.
・ Low risk tendency: Potential risk sensitivity index IVm or less than predetermined value IVms ・ Medium risk trend: Potential risk sensitivity index IVm is greater than or equal to the predetermined value IVms and avoidance ability index IGa is greater than or equal to the predetermined value IGas ・ High risk trend: Potential risk sensitivity index IVm Is less than the predetermined value IVms and the avoidance ability index IGa is less than the predetermined value IGas. The driver's risk tendency is determined by applying the results of the latent risk sensitivity index IVm and the avoidance ability index IGa for each driver to the above determination conditions.
In step S29, a notification command for notifying the driver of the risk tendency determination result is output to the notification unit 9.

[Driver risk tendency judgment operation]
When the host vehicle is not approaching the temporary stop intersection, the process of step S21 is repeated.
When the host vehicle approaches the temporary stop intersection, the process proceeds from step S21 to step S22. Time series data V (t) of vehicle speed V and time series data of acceleration G from time t1 when it is determined that the host vehicle has approached the temporary stop intersection to time t2 when it is determined that the vehicle has passed the temporary stop intersection Record G (t).

  After time t2, the process proceeds to step S23, and the minimum vehicle speed Vmin is calculated. After calculating the minimum vehicle speed Vmin, the maximum acceleration Gmaxa is calculated in step S24.

  When the scoring vehicle speed Vmin and the maximum acceleration Gmaxa are recorded in the set period, the process proceeds from step S25 → step S26 → step S27 → step S28 → step S29. The risk tendency of the driver is determined based on the potential risk sensitivity index IVm calculated from the minimum vehicle speed Vmin in the set period and the avoidance ability index IGa calculated from the maximum acceleration Gmaxa, and the determination result is notified by the notification unit 9.

[Action]
In the second embodiment, the potential risk sensitivity index calculation unit 7 calculates the minimum vehicle speed Vmin when entering the temporarily stopped intersection as the driver's potential risk sensitivity index IVm. The distribution of the minimum vehicle speed Vmin when entering the temporary stop intersection is highly correlated with the vehicle speed integrated value Iv when entering the temporary stop intersection of the first embodiment. Similar to the vehicle speed integrated value Iv, the accuracy of the driver's potential risk sensitivity index IVm can be increased by using the minimum vehicle speed Vmin at the time of approaching the stop intersection where the driver's potential risk sensitivity is likely to appear.

  In the second embodiment, the avoidance ability index calculation unit 8 calculates the maximum acceleration Gmaxa at the temporarily stopped intersection as the driver's avoidance ability index IGa. The distribution of the maximum acceleration Gmaxa when entering the temporarily stopped intersection is highly correlated with the maximum deceleration Gmax when entering the temporarily stopped intersection of the first embodiment. Similar to the maximum deceleration Gmax, the accuracy of the driver's avoidance ability index IGa can be increased by using the maximum acceleration Gmaxa when approaching a temporarily stopped intersection where the driver's avoidance ability tends to appear.

[effect]
(5) The potential risk sensitivity index calculation unit 7 calculates the minimum vehicle speed Vmin when entering the temporarily stopped intersection as the driver's potential risk sensitivity index IVm.
Therefore, the accuracy of the driver's potential risk sensitivity index IVm can be increased.

(6) The avoidance ability index calculation unit 8 calculates the longitudinal acceleration G when entering the intersection as the avoidance ability index Gmaxa of the driver.
Therefore, the accuracy of the driver's avoidance ability index IGa can be increased.

Example 3
The driver risk tendency determination device 20 of the third embodiment will be described. The third embodiment is different from the first embodiment in the intersection safe driving degree determination process executed by the controller 4. The same components as those in the first embodiment are denoted by the same reference numerals and description thereof is omitted.

[Intersection safe driving degree judgment processing]
FIG. 10 is a flowchart showing the flow of the driver risk tendency determination process executed in the controller 4.

  In step S31, the intersection approach determination unit 6 determines whether or not the host vehicle has approached the temporarily stopped intersection based on the current position of the host vehicle detected by the vehicle position detection unit 3 and the intersection information in the map database 5. . A specific determination method is the same as step S1 in the first embodiment. If it is determined that the host vehicle is within the set range of the temporary stop intersection (Yes), the process proceeds to step S32. If it is determined that the host vehicle is outside the setting range of the temporary stop intersection (No), it is determined that the vehicle is not approaching the temporary stop intersection, and this determination is performed again.

  In step S32, the time-series data V (x) of the vehicle speed V when the host vehicle enters the temporarily stopped intersection is recorded in the potential risk sensitivity index calculating unit 7, and the host vehicle is temporarily stopped at the avoidance ability index calculating unit 8. The time series data G (t) of the acceleration G when entering the vehicle is recorded, and the process proceeds to Step S33. A specific recording method is the same as that in step S2 of the first embodiment.

  In step S33, the potential risk sensitivity index calculation unit 7 records the time series data V (x) of the vehicle speed V from the point x1 at time t1 when the host vehicle entered the temporary stop intersection to the point x2 at time t2 when the vehicle passed. From the above, it is determined whether or not the minimum vehicle speed Vmin in the intersection approach section is equal to or less than the set value Vs. If it is determined that the minimum vehicle speed Vmin is equal to or lower than the set value Vs (Yes), the process proceeds to step S34. If it is determined that the minimum vehicle speed Vmin is greater than the set value Vs (No), the process proceeds to step S35.

In step S34, the potential risk sensitivity index calculation unit 7 calculates the integrated value Iv (vehicle speed integrated value Iv) of the time series data V (x) from the point x1 to the point x2, and the process proceeds to step S36. FIG. 11 is a diagram showing a calculation method of the vehicle speed integral value Iv. FIG. 11 (a) is a graph showing time-series data V (x) of the vehicle speed V when the minimum vehicle speed Vmin is equal to or lower than the set value Vs. FIG. 11 (b) is a graph showing time-series data V (x) of the vehicle speed V when the minimum vehicle speed Vmin is larger than the set value Vs. Specifically, data that is equal to or lower than the set value Vs is extracted from the time-series data V (x) from the point x1 to the point x2. The difference between the extracted time series data V (x) and the set value Vs is obtained, and this integral value is calculated. The vehicle speed integral value Iv is represented by the filled area in FIG. 11 (a) and is obtained by the following equation (1).

In step S35, the potential risk sensitivity index calculation unit 7 calculates the integrated value Iv (vehicle speed integrated value Iv) of the time series data V (x) from the point x1 to the point x2, and the process proceeds to step S36. Specifically, the time series data V (s) is inverted as indicated by the broken line in FIG. 11 (b) with the minimum vehicle speed Vmin as a reference, and the data in which the time series data V (x) is larger than the set value Vs is obtained. Extract. The difference between the extracted inverted time series data V (x) and the set value Vs is obtained, and the integral value is calculated. The vehicle speed integral value Iv is represented by the filled area in FIG. 11 (b) and is obtained by the following equation (2).

  In step S36, the avoidance ability index calculation unit 8 calculates the maximum deceleration Gmax in the temporary stop intersection approach section, and the process proceeds to step S37. The calculation method of the maximum deceleration Gmax is the same as that in the first embodiment.

  In step S37, the index calculation value recording unit 9 stores the vehicle speed integrated value Iv calculated in step S34 or step S35 and the maximum deceleration Gmax calculated in step S36, and the process proceeds to step S38.

The process of step S38 to step S41 is performed in the risk tendency determination unit 10.
In step S38, data of the vehicle speed integral value Iv and the maximum deceleration Gmax for the set period recorded in the index calculation value recording unit 9 is extracted, and the process proceeds to step S39. Similar to the description in the first embodiment, the set period is a period set to collect a certain number of data of the vehicle speed integral value Iv and the maximum deceleration Gmax for each passage through the temporarily stopped intersection.

  The processing of step S38 to step S40 is not performed every time the vehicle speed integrated value Iv and the maximum deceleration Gmax are calculated, but may be performed after the number of data of the vehicle speed integrated value Iv and the maximum deceleration Gmax is uniform to some extent. good.

  In step S39, 75% tile value IV and 75% tile value IG are represented as representative values of the vehicle speed integral value Iv and the maximum deceleration Gmax of the driver of the host vehicle from the distribution of the vehicle speed integral value Iv and the maximum deceleration Gmax. Extract and move to step S10.

In step S30, the driver's risk tendency is determined based on the representative value IV (potential risk sensitivity index IV) of the vehicle speed integral value Iv and the representative value IG (avoidance ability index IG) of the maximum deceleration Gmax, and the process proceeds to step S31. Transition. The risk tendency of the driver is determined in the same manner as in the first embodiment.
In step S31, a notification command for notifying the driver of the risk tendency determination result is output to the notification unit 9.

[Driver risk tendency judgment operation]
When the host vehicle is not approaching the temporary stop intersection, the process of step S31 is repeated.
When the host vehicle approaches the temporary stop intersection, the process proceeds from step S31 to step S32. Time series data V (x) of vehicle speed V and time series data of acceleration G from time t1 when it is determined that the host vehicle approaches the temporary stop intersection to time t2 when it is determined that the vehicle has passed the temporary stop intersection Record G (t).

  After time t2, the process proceeds to step S33, and when the minimum vehicle speed Vmin in the intersection approach section is equal to or lower than the set value Vs, the vehicle speed integrated value Iv is calculated in step S34. When the minimum vehicle speed Vmin in the intersection approach section is larger than the set value Vs, a vehicle speed integrated value Iv is calculated in step S35.

  After calculating the vehicle speed integral value Iv, the maximum deceleration Gmax is calculated in step S36.

  When the vehicle speed integral value Iv and the maximum deceleration Gmax are recorded in the set period, the process proceeds from step S37 to step S38 to step S39 to step S40 to step S41. The risk tendency of the driver is determined based on the potential risk sensitivity index IV obtained from the vehicle speed integral value Iv in the set period and the avoidance ability index IG obtained from the maximum deceleration Gmax, and the determination result is notified by the notification unit 9.

[Action]
In Example 3, the time-series data V (x) is integrated even when the minimum vehicle speed Vmin at the time of entering the temporary stop intersection is larger than the set value Vs, and the vehicle speed integrated value Iv is obtained, and this vehicle speed integrated value Iv is negative. It was made to become the value of. As a result, the vehicle speed integral value Iv is calculated as a larger value on the negative side as the vehicle speed at the time of approaching the temporary stop intersection increases, so the driver's potential risk sensitivity will shift to the dangerous side, and the driver's potential risk sensitivity The accuracy of the index IV can be increased.

[effect]
(7) Even when the minimum vehicle speed Vmin at the time of approaching the temporary stop intersection is larger than the set value Vs, the time series data V (x) is integrated to obtain the vehicle speed integrated value Iv, and this vehicle speed integrated value Iv is a negative value. It was made to become.
Therefore, the accuracy of the driver's potential risk sensitivity index IV can be increased.

(Example 4)
A driver risk tendency determination device 20 according to the fourth embodiment will be described. The fourth embodiment differs from the first embodiment in the driver risk tendency determination process executed in the controller 4. The same components as those in the first embodiment are denoted by the same reference numerals and description thereof is omitted.

[overall structure]
FIG. 12 is a block diagram of the driver risk tendency determination device 20. In the fourth embodiment, a yaw rate detection unit 14 is added. The yaw rate detection unit 14 detects the current yaw rate Y of the host vehicle, and outputs information on the detected current yaw rate Y to the controller 4. As the yaw rate detection unit 14, for example, a yaw rate sensor that can detect the behavior of the host vehicle in the turning direction is used.

[Driver risk tendency judgment process]
FIG. 13 is a flowchart showing the flow of the driver risk tendency determination process executed in the controller 4.

  In step S51, the intersection approach determination unit 6 determines whether the host vehicle has approached the temporarily stopped intersection based on the current position of the host vehicle detected by the vehicle position detection unit 3 and the intersection information in the map database 5. . A specific determination method is the same as step S1 in the first embodiment. If it is determined that the host vehicle is within the set range of the temporary stop intersection (Yes), the process proceeds to step S52. If it is determined that the host vehicle is outside the setting range of the temporary stop intersection (No), it is determined that the vehicle is not approaching the temporary stop intersection, and this determination is performed again.

  In step S52, time series data V (x) of the vehicle speed V when the host vehicle enters the temporarily stopped intersection is recorded in the potential risk sensitivity index calculating unit 7, and the host vehicle is temporarily stopped at the avoidance ability index calculating unit 8. The time series data G (t) of the acceleration G when entering the vehicle is recorded, and the process proceeds to step S53.

  Specifically, the potential risk sensitivity index calculation unit 7 and the avoidance capability index calculation unit 8 first determine the time series data V (V) of the vehicle speed V from the time t1 when it is determined in step S51 that the host vehicle has approached the temporary stop intersection. Recording of time series data Y (t) of x) and yaw rate Y is started. The sampling time of the time series data V (x) and the time series data Y (t) is, for example, 10 [msec].

  Subsequently, the potential risk sensitivity index calculation unit 7 and the avoidance capability index calculation unit 8 determine whether or not the host vehicle has passed the temporary stop intersection, and at time t2 when it is determined that the vehicle has passed, the time series data V ( The recording of x) and time series data G (t) is terminated, and the process proceeds to step S53. When it is determined that the host vehicle has not passed the temporary stop intersection, the time series data V (x) and time series data G (t) are continuously recorded, and this determination is performed again.

  In step S53, the potential risk sensitivity index calculation unit 7 records the time series data V (x) of the vehicle speed V from the point x1 at time t1 when the host vehicle entered the temporary stop intersection to the point x2 at time t2 when the vehicle passed. From the above, it is determined whether or not the minimum vehicle speed Vmin in the intersection approach section is equal to or less than the set value Vs. When it is determined that the minimum vehicle speed Vmin is equal to or lower than the set value Vs (Yes), the process proceeds to step S54. If it is determined that the minimum vehicle speed Vmin is greater than the set value Vs (No), the process proceeds to step S55.

  In step S54, the potential risk sensitivity index calculation unit 7 calculates the integrated value Iv (vehicle speed integrated value Iv) of the time series data V (x) from the point x1 to the point x2, and the process proceeds to step S56. A specific method for calculating the vehicle speed integral value Iv is the same as that in step S4 of the first embodiment.

  In step S55, the potential risk sensitivity index calculation unit 7 simply calculates the vehicle speed integrated value Iv as the difference between the set value Vs and the minimum vehicle speed Vmin (Iv = Vs-Vmin), and the vehicle speed integrated value Iv is a negative value. And the process proceeds to step S56.

  In step S56, the avoidance capability index calculation unit 8 calculates the maximum yaw rate Ymax in the temporary stop intersection approach section, and the process proceeds to step S57. FIG. 14 is a diagram showing a method for calculating the maximum yaw rate Ymax and the maximum lateral acceleration amax described later. FIG. 14 (a) is a diagram illustrating a situation in which the host vehicle enters a temporarily stopped intersection. FIG. 14 (b) is a graph showing time series data Y (t) of the yaw rate Y. FIG. 14 (c) is a graph showing time series data a (t) of the lateral acceleration a. As shown in FIG. 14 (b), the specific calculation method is a predetermined value Ys or more from the time series data Y (t) of the yaw rate Y from the time t1 when the host vehicle entered the temporary stop intersection to the time t2 that passed. The section where the yaw rate is generated is cut out as a turning section. Then, the maximum value of the time series data Y (t) in the turning section is extracted as the maximum yaw rate Ymax. The yaw rate is shown as a positive or negative value, distinguishing between right and left turns.

  In step S57, the index calculation value recording unit 9 stores the vehicle speed integral value Iv calculated in step S54 or step S55 and the maximum yaw rate Ymax calculated in step S56, and the process proceeds to step S58.

The process of step S58 to step S61 is performed in the risk tendency determination unit 10.
In step S58, the vehicle speed integrated value Iv and the maximum yaw rate Ymax for the set period recorded in the index calculation value recording unit 9 are extracted, and the process proceeds to step S59. Similar to the description in the first embodiment, the set period is a period set in order to collect a certain amount of data of the vehicle speed integral value Iv and the maximum yaw rate Ymax at each temporary stop intersection passage.

  The processing from step S58 to step S60 is not performed every time the vehicle speed integrated value Iv and the maximum yaw rate Ymax are calculated, but may be performed after the data numbers of the vehicle speed integrated value Iv and the maximum yaw rate Ymax are aligned to some extent.

  In step S59, 75% tile value IV and 75% tile value IG are extracted as representative values of the vehicle speed integral value Iv and maximum yaw rate Ymax of the driver of the vehicle from the distribution of the vehicle speed integral value Iv and maximum yaw rate Ymax. To step S60.

In step S60, the driver's risk tendency is determined based on the representative value IV of the vehicle speed integral value Iv (latent risk sensitivity index IV) and the representative value IY of the maximum yaw rate Ymax (avoidance ability index IY), and the process proceeds to step S61. To do. In Example 4, risk trends are classified into the following three types.
・ Low risk tendency: Potential risk sensitivity index IV or above predetermined value IVs ・ Medium risk tendency: Potential risk sensitivity index IV is below predetermined value IVs and avoidance ability index IY is above predetermined value IYs ・ High risk tendency: Potential risk sensitivity index IV Is less than the predetermined value IVs and the avoidance ability index IY is less than the predetermined value IYs. The results of the potential risk sensitivity index IV and the avoidance ability index IY for each driver are applied to the above determination conditions to determine the risk tendency of the driver.
In step S61, a notification command for notifying the driver of the risk tendency determination result is output to the notification unit 9.

[Driver risk tendency judgment operation]
When the host vehicle is not approaching the temporary stop intersection, the process of step S51 is repeated.
When the host vehicle approaches the temporary stop intersection, the process proceeds from step S51 to step S52. Time series data V (x) of vehicle speed V and time series data of yaw rate Y from time t1 when it is determined that the host vehicle approaches the temporary stop intersection until time t2 when it is determined that the vehicle has passed the temporary stop intersection Record Y (t).

After time t2, the process proceeds to step S53, and when the minimum vehicle speed Vmin in the intersection approach section is equal to or lower than the set value Vs, the vehicle speed integrated value Iv is calculated in step S54. When the minimum vehicle speed Vmin in the intersection approach section is larger than the set value Vs, a vehicle speed integrated value Iv is calculated in step S55.
After calculating the vehicle speed integral value Iv, the maximum yaw rate Ymax is calculated in step S56.

  When the vehicle speed integral value Iv and the maximum yaw rate Ymax are recorded in the set period, the process proceeds from step S57 → step S58 → step S59 → step S60 → step S61. The risk tendency of the driver is determined based on the potential risk sensitivity index IV obtained from the vehicle speed integral value Iv in the set period and the avoidance ability index IY obtained from the maximum yaw rate Ymax, and the determination result is notified by the notification unit 9.

[Action]
In the fourth embodiment, the avoidance capability index calculation unit 8 calculates the maximum yaw rate Ymax at the temporarily stopped intersection as the driver's avoidance capability index IY. The distribution of the maximum yaw rate Ymax when approaching the temporarily stopped intersection is highly correlated with the maximum deceleration Gmax when entering the temporarily stopped intersection of the first embodiment. Similar to the maximum deceleration Gmax, the accuracy of the driver's avoidance ability index IY can be increased by using the maximum yaw rate Ymax when approaching the temporary stop intersection where the driver's avoidance ability tends to appear.

  In addition, since the avoidance ability index IY can be obtained by the driving action at the time of turning right or left at an intersection where the risk is particularly high, the accuracy of the avoidance ability index IY of the driver can be further increased.

  Instead of calculating the avoidance capability index IY using the maximum yaw rate Ymax at the temporary stop intersection, the avoidance capability index IA may be calculated using the maximum lateral acceleration amax at the temporary stop intersection. As shown in FIG. 14 (c), the specific method for calculating the maximum lateral acceleration amax is the time-series data a (t of the lateral acceleration a from the time t1 when the host vehicle enters the temporary stop intersection to the time t2 when it passes. ), A section where a lateral acceleration greater than or equal to a predetermined value as is generated as a turning section. Then, the maximum value of the time series data a (t) in the turning section is extracted as the maximum lateral acceleration amax. The lateral acceleration is shown as a positive or negative value, distinguishing between right and left turns.

[effect]
(8) The avoidance ability index calculation unit 8 calculates the yaw rate Y when the vehicle turns right and left as the avoidance ability index IY of the driver.
Therefore, the accuracy of the driver's avoidance ability index IY can be increased.

(9) The avoidance ability index calculation unit 8 calculates the lateral acceleration a when turning right or left at the intersection as the avoidance ability index IA of the driver.
Therefore, the accuracy of the driver's avoidance ability index IY can be increased.

Example 5
A driver risk tendency determination device 20 of Example 5 will be described. The fifth embodiment is different from the first embodiment in the driver risk tendency determination process executed in the controller 4. The same components as those in the first embodiment are denoted by the same reference numerals and description thereof is omitted.

[overall structure]
FIG. 12 is a block diagram of the driver risk tendency determination device 20. In the fourth embodiment, an obstacle detection unit 15 is added. The obstacle detection unit 15 detects the distance L to the obstacle existing in front of the course of the host vehicle, and outputs information on the detected distance L to the controller 4 to the controller 4. As the obstacle detection unit 15, for example, a sensor that detects a positional relationship with a vehicle ahead of the host vehicle such as a camera or a radar is employed.

[Driver risk tendency judgment process]
FIG. 16 is a flowchart showing the flow of the driver risk tendency determination process executed in the controller 4.

  In step S71, the intersection approach determination unit 6 determines whether or not the host vehicle has approached the temporarily stopped intersection based on the current position of the host vehicle detected by the vehicle position detection unit 3 and the intersection information in the map database 5. . A specific determination method is the same as step S1 in the first embodiment. If it is determined that the host vehicle is within the setting range of the temporary stop intersection (Yes), the process proceeds to step S72. If it is determined that the host vehicle is outside the setting range of the temporary stop intersection (No), it is determined that the vehicle is not approaching the temporary stop intersection, and this determination is performed again.

  In step S72, the potential risk sensitivity index calculation unit 7 records time-series data V (x) of the vehicle speed V when the host vehicle enters the temporary stop intersection, and the process proceeds to step S73. A specific method for recording time-series data V (x) is the same as that in step S2 of the first embodiment.

  In step S73, the potential risk sensitivity index calculation unit 7 records the time series data V (x) of the vehicle speed V from the point x1 at time t1 when the host vehicle entered the temporary stop intersection to the point x2 at time t2 when the vehicle passed. From the above, it is determined whether or not the minimum vehicle speed Vmin in the intersection approach section is equal to or less than the set value Vs. If it is determined that the minimum vehicle speed Vmin is equal to or lower than the set value Vs (Yes), the process proceeds to step S74. If it is determined that the minimum vehicle speed Vmin is greater than the set value Vs (No), the process proceeds to step S75.

  In step S74, the potential risk sensitivity index calculation unit 7 calculates the integrated value Iv (vehicle speed integrated value Iv) of the time series data V (x) from the point x1 to the point x2, and the process proceeds to step S76. A specific method for calculating the vehicle speed integral value Iv is the same as that in step S4 of the first embodiment.

  In step S75, the potential risk sensitivity index calculation unit 7 simply calculates the vehicle speed integrated value Iv as a difference between the set value Vs and the minimum vehicle speed Vmin (Iv = Vs-Vmin), and the vehicle speed integrated value Iv is a negative value. And the process proceeds to step S76.

  In step S76, the obstacle detection unit 13 detects the distance L to the obstacle existing ahead of the course of the host vehicle, and the distance L is within the predetermined distance Ls and the relative speed is equal to or greater than the predetermined value. The approach state with an obstacle is judged by no. If it is determined that the vehicle is approaching the obstacle (Yes), the process proceeds to step S77. If it is determined that the vehicle is not approaching the obstacle (No), the process returns to S71.

  In step S77, the maximum deceleration Gmax when approaching the obstacle is calculated, and the process proceeds to step S78.

  In step S78, the index calculation value recording unit 9 stores the vehicle speed integral value Iv calculated in step S74 or step S75 and the maximum yaw rate Ymax calculated in step S76, and the process proceeds to step S79.

The process of step S79 to step S82 is performed in the risk tendency determination unit 10.
In step S79, the vehicle speed integral value Iv and the maximum deceleration Gmax for the set period recorded in the index calculation value recording unit 9 are extracted, and the process proceeds to step S80. Similar to the description in the first embodiment, the set period is a period set to collect a certain number of data of the vehicle speed integral value Iv and the maximum deceleration Gmax for each passage through the temporarily stopped intersection.

  The processing of step S79 to step S81 is not performed every time the vehicle speed integrated value Iv and the maximum deceleration Gmax are calculated, but may be performed after the number of data of the vehicle speed integrated value Iv and the maximum deceleration max is uniform to some extent. good.

  In step S80, 75% tile value IV and 75% tile value IG are represented as representative values of the vehicle speed integral value Iv and the maximum deceleration Gmax of the driver of the host vehicle from the distribution of the vehicle speed integral value Iv and the maximum deceleration Gmax. Extract and move to step S81.

  In step S81, the risk tendency of the driver is determined based on the representative value IV (potential risk sensitivity index IV) of the vehicle speed integral value Iv and the representative value IG (avoidance ability index IG) of the maximum deceleration Gmax, and the process proceeds to step S82. Transition. The classification of risk trends is the same as step S10 in the first embodiment.

  In step S82, a notification command for notifying the driver of the risk tendency determination result is output to the notification unit 9.

[Driver risk tendency judgment operation]
When the host vehicle is not approaching the temporary stop intersection, the process of step S71 is repeated.
When the host vehicle approaches the temporary stop intersection, the process proceeds from step S71 to step S72. Time series data V (x) of the vehicle speed V is recorded from time t1 when it is determined that the host vehicle has approached the temporary stop intersection to time t2 when it is determined that the vehicle has passed the temporary stop intersection.

  After time t2, the process proceeds to step S73, and when the minimum vehicle speed Vmin in the intersection approach section is equal to or less than the set value Vs, the vehicle speed integrated value Iv is calculated in step S74. When the minimum vehicle speed Vmin in the intersection approach section is larger than the set value Vs, the vehicle speed integrated value Iv is calculated in step S75.

  When the vehicle speed integral value Iv is calculated and the vehicle approaches the obstacle, the process proceeds from step S76 to step S77, and the maximum deceleration Gmax is calculated.

  When the vehicle speed integral value Iv and the maximum acceleration Gmax are recorded in the set period, the process proceeds from step S78 → step S79 → step S80 → step S81 → step S82. The risk tendency of the driver is determined based on the potential risk sensitivity index IV obtained from the vehicle speed integrated value Iv in the set period and the avoidance ability index IG obtained from the maximum acceleration Gmax, and the determination result is notified by the notification unit 9.

[Action]
In the fifth embodiment, the avoidance capability index calculation unit 8 calculates the deceleration when the obstacle approaches as the driver's avoidance capability index IG. By using the driving action in a state where the obstacle is actually in front of the eyes, the risk avoidance ability of the driver can be determined more accurately.

[effect]
(10) The avoidance ability index calculation unit 8 calculates the deceleration when the obstacle approaches as the driver's avoidance ability index IG.
Therefore, the accuracy of the driver's avoidance ability index IG can be increased.

[Other Examples]
As described above, the present invention is not limited to the configuration of the above embodiment, and may have other configurations.
The driver's safe driving determination method may be performed by combining the methods shown in the first to fifth embodiments.

  In Example 1 to Example 5, the avoidance ability index is calculated from the acceleration when approaching and approaching the temporarily stopped intersection. However, the acceleration is always monitored without being limited to the acceleration when approaching and approaching the temporarily stopped intersection. You may do it.

  In the first to fifth embodiments, the risk tendency determination unit 10 is installed inside the controller 4 mounted on the vehicle. However, the risk tendency determination unit 10 can be obtained by replacing the index calculation value recording unit 9 with a removable hard disk or the like. Can also be installed outside the vehicle. In this case, the manager can grasp the determination result of the driver's degree of safe driving and use it for the driver's safe driving education.

  Further, the index calculation value recording unit 9 and the risk tendency determination unit 10 can be installed in the data center. In this case, the calculation results of the potential risk sensitivity index calculation unit 7 and the avoidance capability index calculation unit 8 are transmitted from the vehicle to the data center via the communication means. The determination result of the driver's risk tendency determined by the risk tendency determination unit 10 installed in the data center is transmitted from the data center to the vehicle together with the positioning compared with other drivers through the communication means again.

  The judgment result of the risk tendency and the position compared to other drivers can be notified to the driver himself, but the administrator or data center provides this to the insurance company, setting the insurance rate of the contracted vehicle, It can also be used for discounts.

  The potential risk sensitivity index and the avoidance ability index described in Examples 1 to 5 use Japan's largest driving behavior database provided by the National Institute of Advanced Industrial Science and Technology, and are low-risk groups (there are few accidents and near-misses) Driver), medium risk group (prone to frequent near-miss drivers), and high risk group (drivers with accident experience) have been confirmed. Therefore, it can be said that the vehicle speed integral value IV and the minimum vehicle speed Vmin used as the potential risk sensitivity index are driving behavior indices related to the likelihood of an unsafe event when entering an intersection. In addition, the maximum deceleration Gmax, maximum acceleration Gmaxa, and maximum yaw rate Ymax used as avoidance ability indicators are driving behavior indicators that distinguish between near-miss situations and other serious unsafe situations with others. I can say that. By grasping these risk trends, appropriate driving support, driver education, risk management, and the like become possible.

1 Vehicle speed detector (travel information detector)
2 Acceleration speed detector (travel information detector)
7 Potential risk sensitivity index calculator
8 Avoidance ability index calculator
9 Index calculation value recording section
10 Risk trend judgment section
14 Yaw rate detector (running information detector)

Claims (9)

A travel information detection unit for detecting travel information of the vehicle;
A potential risk sensitivity index calculation unit that calculates an index representing the level of potential risk sensitivity for predicting a risk that the driver cannot see based on the vehicle speed at the time of approaching the temporary stop intersection among the travel information at the time of approaching the temporary stop intersection;
An avoidance capability index calculation unit that calculates an index indicating the level of avoidance capability for the risk of approaching the driver from the degree of sudden operation of the driver obtained based on the travel information;
An index calculation value recording unit for recording the calculated latent risk sensitivity and an index of the avoidance ability;
A risk trend determination unit that determines a driver's risk trend based on an index calculation value within a set period recorded by the index calculation value recording unit;
A safe driving diagnosis device comprising:
In the safe driving diagnostic apparatus according to claim 1,
The said risk tendency determination part determines the high risk tendency, the medium risk tendency, and the low risk tendency as said driver's risk tendency, The safe driving diagnosis apparatus characterized by the above-mentioned. In the safe driving diagnostic apparatus according to claim 1 or claim 2,
The risk potential sensitivity index calculating unit, safe driving diagnostic apparatus characterized by calculating a vehicle speed integrated value at the time of temporary stop intersection approach as an index representing the height of the potential risks sensitivity of the driver. In the safe driving diagnostic apparatus according to any one of claims 1 to 3,
The risk potential sensitivity index calculating unit, safe driving diagnostic apparatus characterized by computing the minimum vehicle speed at the time of temporary stop intersection approach as an index representing the height of the potential risks sensitivity of the driver. In the safe driving diagnostic apparatus according to any one of claims 1 to 4,
The said avoidance ability parameter | index calculation part calculates the longitudinal acceleration at the time of the intersection approach as an index showing the level of the avoidance capability of the said driver, The safe driving diagnostic apparatus characterized by the above-mentioned. In the safe driving diagnostic apparatus according to any one of claims 1 to 5,
The avoidance ability index calculating unit calculates a deceleration at the time of approaching an intersection as an index indicating a level of a driver's avoidance ability. In the safe driving diagnostic apparatus according to any one of claims 1 to 6,
The avoidance ability index calculating unit calculates a deceleration when an obstacle approaches as an index indicating the level of the avoidance ability of the driver. In the safe driving diagnostic apparatus according to any one of claims 1 to 7,
The avoidance ability index calculating unit calculates a lateral acceleration at the time of turning right or left at an intersection as an index indicating the level of a driver's avoidance ability. In the safe driving diagnostic apparatus according to any one of claims 1 to 8,
The avoidance capability index calculation unit is a safe driving diagnosis device that calculates a yaw rate at the time of turning right and left at an intersection as an index indicating the level of avoidance capability of the driver.

Images