JP2009070145A - Vehicle driving evaluation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a vehicle driving evaluation device which presents information easy for a driver to accept. <P>SOLUTION: A driving action index corresponding to a traveling state of an own vehicle is calculated, and a risk index is calculated for each risk form such as rear end collision risk or front-end collision risk based on the driving action index. The risk index is calculated as a risk occurrence probability per year, an income which cannot be received by lost worktime resulting from risk occurrence per year, and an increment of insurance fee raised by the risk occurrence per year. A total risk index is selected from such three kinds of risk indexes, and an evaluation result of the total risk and an advice for reducing the risk are provided to a driver. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a vehicle driving evaluation apparatus.

  The conventional device evaluates the risk from the driving situation and alerts the driver (for example, see Patent Document 1). This device is designed to learn the margin time between the vehicle at the start of deceleration and the preceding vehicle, and to issue a warning to the driver according to the learned value.

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

  The above-described conventional device simply gives an alarm to the driver when a high-risk state is detected, and varies from driver to driver (so-called individual difference) or from driver to driver (so-called individual). Depending on the difference, the presented warning may become information that is difficult for the driver to accept. Therefore, there is a problem that it is difficult to obtain an effect of changing driving behavior so as to lead to driving that enhances a sense of security by the occurrence of an alarm.

The vehicle driving evaluation apparatus according to the present invention includes a driving behavior detection unit that detects a driving behavior index that represents a driving behavior of a driver, and a risk that represents the magnitude of the risk based on the driving behavior index detected by the driving behavior detection unit. Based on the risk calculating means for calculating the index, the comprehensive risk index calculating means for calculating the total risk index based on the risk index calculated by the risk calculating means, and the total risk index calculated by the comprehensive risk index calculating means, Risk notifying means for notifying the driver of the risk state, and advice presenting means for presenting advice for reducing risk to the driver based on the comprehensive risk index calculated by the comprehensive risk index calculating means.
The vehicle driving evaluation method according to the present invention detects a driving behavior index representing the driving behavior of the driver, calculates a risk index representing the magnitude of the risk based on the detected driving behavior index, and calculates the calculated risk index. Based on the calculated total risk index, the risk status is reported to the driver based on the calculated total risk index, and advice to reduce the risk is presented to the driver based on the calculated total risk index To do.
The vehicle according to the present invention calculates a risk indicator representing the magnitude of the risk based on the driving behavior detecting means for detecting the driving behavior index representing the driving behavior of the driver and the driving behavior index detected by the driving behavior detecting means. A risk calculation means, a comprehensive risk index calculation means for calculating a total risk index based on the risk index calculated by the risk calculation means, and a risk state based on the total risk index calculated by the comprehensive risk index calculation means A vehicle driving evaluation device having risk notification means for notifying a driver and advice presentation means for presenting advice for reducing risk to the driver based on the total risk index calculated by the total risk index calculation means .

  It is possible to evaluate the driving behavior of the driver and present information according to the individual driving characteristics.

  A vehicle operation evaluation apparatus according to an embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a system diagram showing a configuration of a vehicle driving evaluation apparatus according to an embodiment of the present invention, and FIG. 2 is a configuration diagram of a vehicle equipped with a vehicle driving evaluation apparatus.

  First, the configuration of the vehicle driving evaluation apparatus will be described. The vehicle driving operation evaluation apparatus includes a steering angle sensor 1, a brake pedal sensor 2, an accelerator pedal sensor 3, an acceleration sensor 4, an own vehicle speed sensor 5, a front inter-vehicle distance sensor 6, a rear inter-vehicle distance sensor 7, and an own vehicle position sensor 8. , Road shape database 9, temporary stop line position database 10, legal speed database 11, controller 18, display device 16, sound device 17, and the like.

  The steering angle sensor 1 detects the rotation angle (steering angle) of the steering wheel and outputs the detected steering angle to the controller 18. The brake pedal sensor 2 detects whether or not the brake pedal has been depressed by the driver. The accelerator pedal sensor 3 detects whether or not the accelerator pedal is depressed by the driver. The acceleration sensor 4 measures the longitudinal acceleration of the host vehicle. The own vehicle speed sensor 5 calculates the own vehicle speed from the wheel speed.

  The front inter-vehicle distance sensor 6 is composed of, for example, a laser radar attached to the front grill or bumper of the vehicle. The front inter-vehicle distance sensor 6 measures the presence or absence of an obstacle existing ahead of the host vehicle, for example, the presence of a preceding vehicle, and the inter-vehicle distance between the host vehicle and the preceding vehicle when a preceding vehicle exists. Further, the relative speed between the host vehicle and the preceding vehicle is calculated from the differential amount of the inter-vehicle distance. The rear inter-vehicle distance sensor 7 has the same configuration as that of the front inter-vehicle distance 6, and the presence of an obstacle existing behind the host vehicle, for example, the presence of a following vehicle, and the presence of a following vehicle, the host vehicle and the following vehicle. Measure the distance between the two. Further, the relative speed between the host vehicle and the following vehicle is calculated from the differential amount of the inter-vehicle distance.

  The own vehicle position sensor 8 uses GPS to measure the current position of the own vehicle, that is, the latitude and longitude of the current position of the own vehicle.

  The road shape database 9, the temporary stop line position database 10, and the legal speed database 11 are databases constituting a navigation system (not shown) mounted on the host vehicle. The road shape database 9 stores road curvature R data. The temporary stop line position database 10 stores latitude and longitude data of the temporary stop line. The legal speed database 11 stores road legal speed data.

  The control device 18 includes a CPU and CPU peripheral components such as a ROM and a RAM, and controls the entire vehicle driving evaluation device based on input signals from each sensor and database. Based on information obtained from each sensor and database, the control device 18 calculates a driving action index 12 for calculating a driving action index, which will be described later, and a driving action index calculated by the driving action index calculation computer 12. , And a memory 13 for storing a risk calculation result, which will be described later, a risk prediction formula database 14 storing data of a risk prediction formula, which will be described later, and a driver's driving behavior index is applied to the risk prediction formula to calculate a risk. And a risk calculation computer 15.

  The display device 16 displays information related to risk and advice for reducing the risk to the driver in response to a command from the control device 18. For example, a display device of a navigation system can be used as the display device 16. The display screen of the display device 16 is configured as a touch panel, and the buttons can be operated by touching the buttons displayed on the screen. The voice device 17 presents information related to risk to the driver by voice in response to a command from the control device 18.

Next, the operation of the vehicle driving evaluation apparatus according to the embodiment will be described. FIG. 3 shows a main flowchart showing the entire operation procedure of the vehicle driving evaluation apparatus.
For example, the processing of FIG. 3 is started by turning on the ignition switch (step S21). In step S22, the driving behavior of the driver is measured. The specific processing here will be described later with reference to the flowcharts of FIGS. In step S23, a risk prediction formula is read. Specifically, the risk occurrence cumulative frequency distribution and the risk occurrence result for the driving behavior index stored in the risk prediction formula database 14 are read. Specific processing here will be described later with reference to FIGS.

  In step S24, the driver's risk index is calculated based on the driving behavior of the driver measured in step S22 and the risk prediction formula read in step S23. The risk index calculated here is a value for presenting information on the magnitude of the risk to the driver in an easy-to-understand manner, such as rear-end collision, rear-end collision, and out-of-road departure. Calculates the probability that a risk will occur and the insurance premium that the driver will pay if a risk occurs. Specific processing here will be described later with reference to FIGS.

  In step S25, a comprehensive risk index is created based on the plurality of risk indexes calculated in step S24. The specific process here is later mentioned using FIGS. In step S26, the total risk index calculated in step S25 is notified to the driver by display or voice. The specific process here is later mentioned using FIGS. 34-39. In step S27, advice for reducing the risk is presented to the driver by display or voice. The specific process here is later mentioned using FIGS. 40-42. Thereby, the process shown in the flowchart of FIG. 3 is terminated (step S28).

  Next, the driving behavior measurement process of the driver in step S22 will be described using the flowchart of FIG. When the driving behavior measurement process is started in step S31, it is determined in step S32 whether the traveling state of the host vehicle is a specific traveling scene based on information from each sensor and database. Then, the driving action index related to the risk corresponding to the driving scene determined in step S33 is measured. The measured driving behavior index is recorded in the memory 13.

Specifically, as shown below, when a specific driving scene is detected, a driving action index corresponding to the driving scene is measured.
-When following the preceding vehicle: Inter-vehicle time when following the preceding vehicle-When approaching the preceding vehicle: Allowance time when approaching the preceding vehicle-When traveling alone: Difference between the vehicle speed and legal speed when traveling alone When stopping at the temporary stop line: Starting acceleration from the temporary stop line, when approaching the temporary stop line: Distance between the host vehicle and the temporary stop line at the start of brake operation when decelerating at the temporary stop line When following: Follow-up time following vehicle with following vehicle ・ When driving on straight road: Steering entropy when driving on straight road

In step S34, the average value of the day of the driving behavior index measured and accumulated in step S33 is calculated. Specifically, the average value of the following driving behavior index for the day is calculated.
・ Average value of inter-vehicle time when following vehicle on that day: THWf_D
・ Average TTC time margin when approaching the vehicle on that day: TTC_D
・ Average value of difference between own vehicle speed and legal speed when traveling alone on that day: Δv_D
・ Average start acceleration from the stop line on that day: a_D
・ Average distance at the start of brake operation when decelerating on the stop line on that day: m_D
・ Average value of inter-vehicle time with following vehicle when following vehicle is following that day: THWb_D
・ The average value of steering entropy when driving on a straight road that day: Hp_D

  After calculating the average value of the driving behavior index for the day in step S34, this process is terminated (step S35).

  Here, the calculation procedure of the driving behavior index and the average value of the driving behavior index for the day will be described with reference to the flowcharts of FIGS. First, a method for calculating a driving action index in a preceding vehicle following scene, a preceding vehicle approaching scene, and a single traveling scene will be described with reference to FIG.

  When the process starts in step S41, it is determined in step S42 whether or not a preceding vehicle ahead of the host vehicle is detected by the front inter-vehicle distance sensor 6. When the preceding vehicle is detected, the process proceeds to step S43, and it is determined whether or not the absolute value of the relative speed Vr between the host vehicle and the preceding vehicle is 2 km / h or less. When the absolute value of the relative speed is 2 km / h or less, it is determined that the host vehicle is following the vehicle with a substantially constant distance from the preceding vehicle, and the process proceeds to step S44 to determine the difference between the host vehicle and the preceding vehicle. Calculate inter-vehicle time (front inter-vehicle time) THWf. The front inter-vehicle time THWf is a value representing the time until the host vehicle reaches the current position of the preceding vehicle, and can be calculated by dividing the inter-vehicle distance between the host vehicle and the preceding vehicle by the host vehicle speed. The calculated front inter-vehicle time THWf is recorded and stored in the memory 13.

  In step S45, the frequency distribution of the front inter-vehicle time THWf is calculated from the inter-vehicle time THWf data for the preceding vehicle of the day stored in the memory 13. In step S46, the average inter-vehicle time THWf_D of the day is calculated.

  If it is determined in step S43 that the absolute value of the relative speed is greater than 2 km / h, the process proceeds to step S47 to determine whether or not the relative speed Vr between the host vehicle and the preceding vehicle is less than -2 km / h. . If the relative speed Vr is less than -2 km / h, it is determined that the host vehicle is approaching the preceding vehicle, and the process proceeds to step S48 to calculate a margin time TTC between the host vehicle and the preceding vehicle. The margin time TTC is a value representing the time until the host vehicle contacts the preceding vehicle when the relative speed Vr is constant, and can be calculated by dividing the inter-vehicle distance between the host vehicle and the preceding vehicle by the relative speed Vr. The calculated margin time TTC is recorded and stored in the memory 13.

  In step S49, the frequency distribution of the margin time TTC is calculated from the margin time TTC data for the preceding vehicle of the day stored in the memory 13. In step S50, an average margin time TTC_D for the day is calculated. On the other hand, if a negative determination is made in step S47, the process proceeds to step S56, where it is determined that the current traveling state of the host vehicle does not correspond to a specific traveling scene, and no scene is recorded.

  If it determines with the preceding vehicle not being detected by step S42, it will progress to step S51. In the process after step S51, since the driving action index in the single traveling scene is measured, it is assumed that the following vehicle is not detected by the rear inter-vehicle distance sensor 7. In step S51, the current position of the host vehicle is detected based on the signal from the host vehicle position sensor 8. In step S <b> 52, legal speed data of the traveling road corresponding to the current position of the host vehicle is acquired from the legal speed database 11. In step S53, a difference amount Δv (= own vehicle speed−legal speed) between the own vehicle speed and the legal speed in the single traveling scene is calculated. Specifically, a difference amount (speed difference) between the recommended speed set based on the legal speed and the own vehicle speed is calculated and recorded in the memory 13.

  In step S54, the frequency difference frequency distribution is calculated from the speed difference Δv data stored in the memory 13 for the day. In step S55, the average speed difference Δv_D for the day is calculated. This is the end of this process (step S57).

  Next, a method for calculating the driving action index in the pause line start scene and the pause deceleration scene will be described with reference to FIG.

  First, when this process is started in step S61, it is confirmed in step S62 that the preceding vehicle is not detected by the front inter-vehicle distance sensor 6. In step S63, the current position of the host vehicle is detected based on the signal from the host vehicle position sensor 8. In step S64, the distance from the own vehicle to the temporary stop line in front of the travel path corresponding to the current position of the own vehicle is acquired from the temporary stop line position database 10. In step S65, it is determined whether the distance from the current position of the host vehicle to the temporary stop line is ± 3 m or less and the vehicle speed of the host vehicle detected by the host vehicle speed sensor 5 is 0 km / h. If a positive determination is made in step S65, the process proceeds to step S66. In step S 66, it is determined that the scene is a stop line start scene, and the maximum value a (maximum acceleration) of the vehicle's acceleration detected by the acceleration sensor 4 within 3 seconds after starting from the stop line is recorded in the memory 13. To do.

  In step S67, the frequency distribution of the maximum acceleration at the start is calculated from the data of the maximum acceleration a of the day stored in the memory 13. In step S68, an average maximum acceleration a_D at the start of the day is calculated.

  On the other hand, if a negative determination is made in step S65, the process proceeds to step S69, in which it is determined whether the distance from the current position of the host vehicle to the temporary stop line is 50 m or less. When the distance to the temporary stop line is 50 m or less, the process proceeds to step S70. In step S70, it is determined that the scene is a temporary stop line deceleration scene, and the distance from the own vehicle position to the temporary stop line at the start of the brake operation is measured and recorded in the memory 13. Specifically, the distance from the own vehicle position to the temporary stop line is detected when the brake pedal sensor 2 detects the depression of the brake pedal after the distance to the temporary stop line becomes 50 m or less. To do.

  In step S71, the frequency distribution of the distance at the start of the brake operation is calculated from the data of the distance to the temporary stop line for the day stored in the memory 13. In step S72, the average distance m_D for the day is calculated. On the other hand, if a negative determination is made in step S69, the process proceeds to step S73, where it is determined that the current traveling state of the host vehicle does not correspond to a specific traveling scene, and no scene is recorded. This is the end of this process (step S74).

  Next, a method for calculating the driving action index in the following vehicle following scene followed by the following vehicle will be described with reference to FIG.

  First, when this process is started in step S81, it is determined in step S82 whether or not a succeeding vehicle is detected by the rear inter-vehicle distance sensor 7. When the succeeding vehicle is detected, the process proceeds to step S83, and the inter-vehicle time (rear inter-vehicle time) between the own vehicle and the succeeding vehicle is calculated and recorded in the memory 13 by determining that it is a following vehicle following scene. Here, the rear inter-vehicle time is a value obtained by dividing the inter-vehicle distance between the own vehicle and the following vehicle by the own vehicle speed or the vehicle speed of the following vehicle.

  In step S84, the frequency distribution of the rear inter-vehicle time is calculated from the data of the rear inter-vehicle time of the day stored in the memory 13. In step S85, an average rear inter-vehicle time THWb_D of the day is calculated. On the other hand, if a negative determination is made in step S82 and no subsequent vehicle is detected, the process proceeds to step S86, where it is determined that the current traveling state of the host vehicle does not correspond to a specific traveling scene, and no scene is recorded. This is the end of this process (step S87).

Next, a method for calculating a driving action index in a straight road running scene will be described with reference to FIG.
First, when this process is started in step S91, the current position of the host vehicle is detected based on a signal from the host vehicle position sensor 8 in step S92. In step S93, road shape data corresponding to the current position of the host vehicle is acquired from the road shape database 9. In step S94, based on the road shape data acquired in step S93, it is determined whether or not the curvature R of the road on which the host vehicle is traveling is greater than 1000 m. If the road curvature R is greater than 1000 m, the process proceeds to step S95.

  In step S95, it is determined that the scene is a straight road, and the steering entropy is calculated based on the steering angle detected by the steering angle sensor 1 and recorded in the memory 13. The steering entropy method will be briefly described.

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

  The α value is a steering error within a fixed time based on the time series data of the steering angle, that is, the difference between the estimated value of the steering angle when the steering is operated smoothly and the actual steering angle, A steering error distribution (variation) is measured to calculate a 90 percent tile value (a distribution range including 90% of the steering error). The steering entropy value, that is, the Hp value represents the ambiguity (uncertainty) of the steering error distribution. Similar to the α value, the Hp value decreases when the steering operation is smooth and stable, and increases when it is unstable and unstable.

  In the next step S96, the steering entropy frequency distribution is calculated from the steering entropy data of the day stored in the memory 13. In step S97, an average steering entropy Hp_D of the day is calculated. On the other hand, when a negative determination is made in step S94 and the road curvature R is 1000 m or less, it is determined that the current traveling state of the host vehicle does not correspond to a specific traveling scene, and no scene is recorded. Thereby, this process is completed (step S99).

  Next, the risk prediction formula reading process in step S23 will be described with reference to the flowchart of FIG. When the process of reading the risk prediction formula is started in step S101, the risk occurrence result data for the risk form is read in step S102. In the memory 13, a table indicating the relationship between each risk form and the risk occurrence result as shown in FIG. 10 is defined and stored in advance.

  Various types of risk can be considered, but here, the risk of a rear-end collision (front-end collision risk) with a forward obstacle, the risk of a collision with a subsequent vehicle (rear-end collision risk), and the own vehicle deviate from the road. It is classified into risk (road departure risk), risk at intersections (for example, encounter accidents), and risk to traffic weak persons (for example, contact with pedestrians), and the results when each risk form actually occurs It is stored in association. As a result of the risk occurrence, for example, the average number of days YA to YE that the driver must take a job off when each risk form actually occurs, and the insurance premium that the driver pays when each risk form actually occurs Read the rising amount of ZA to ZE.

  In step S103, data of risk occurrence cumulative frequency distribution for the driving behavior index is read. In the memory 13, driving behavior indexes and risk occurrence cumulative frequency distributions as shown in FIGS. 11 and 12 are defined and stored in advance for each risk form. Here, the following driving behavior index is defined as a variable, and the risk occurrence cumulative frequency distribution in each risk mode is set based on the driving behavior index data of many drivers related to each risk mode.

・ The following inter-vehicle time THWf in the scene following the preceding vehicle (Figure 11)
・ TTC margin time for approaching the preceding vehicle (Figure 11)
・ Amount of difference Δv from the legal speed of a single driving scene (Fig. 11)
・ Maximum acceleration a of the stop line start scene (Fig. 12)
-Brake operation start timing (distance at the start of brake operation) m in the pause line deceleration scene (Fig. 12)
・ Rear inter-vehicle time THWb in the following vehicle following scene (Fig. 12)
-Steering entropy Hp in a straight road running scene (Figure 12)

  In step S104, a risk potential prediction formula is constructed based on the data read in steps S102 and S103. Specifically, three types of prediction formulas for risk occurrence for each risk form (corresponding to comprehensive risk indicators Risk1, Risk2, Risk3) are constructed for each driving behavior indicator. The overall risk index Risk1 is the risk probability per year, the overall risk index Risk2 is the driver's revenue per year that cannot be received due to a risk outage, and the overall risk index Risk3 is per year that increases due to the risk occurrence Shows the increase in insurance premiums. Note that P in the following expression is an average daily allowance of the driver, and is set and input in advance.

・ Rear-end collision risk
-Risk prediction formula based on the preceding vehicle following scene
Risk1_A_1 [times / year] = F_A_1 (THWf) [times / day] x 365 [days / year]
Risk2_A_1 [yen / year] = F_A_1 (THWf) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_1 [yen / year] = F_A_1 (THWf) [times / day] x ZA [yen / times] x 365 [day / year]

-Risk prediction formula based on the approaching vehicle scene
Risk1_A_2 [times / year] = F_A_2 (TTC) [times / day] x 365 [days / year]
Risk2_A_2 [yen / year] = F_A_2 (TTC) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_2 [yen / year] = F_A_2 (TTC) [times / day] × ZA [yen / times] × 365 [day / year]

-Risk prediction formula based on a single driving scene
Risk1_A_3 [times / year] = F_A_3 (THW) [times / day] × 365 [days / year]
Risk2_A_3 [yen / year] = F_A_3 (THW) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_3 [yen / year] = F_A_3 (THW) [times / day] x ZA [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line start scene
Risk1_A_4 [times / year] = F_A_4 (a) [times / day] x 365 [days / year]
Risk2_A_4 [yen / year] = F_A_4 (a) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_4 [yen / year] = F_A_4 (a) [times / day] x ZA [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line deceleration scene
Risk1_A_5 [times / year] = F_A_5 (m) [times / day] x 365 [days / year]
Risk2_A_5 [yen / year] = F_A_5 (m) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_5 [yen / year] = F_A_5 (m) [times / day] x ZA [yen / times] x 365 [day / year]

-Risk prediction formula based on the following scene from the following vehicle
Risk1_A_6 [times / year] = F_A_6 (THWb) [times / day] × 365 [days / year]
Risk2_A_6 [yen / year] = F_A_6 (THWb) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_6 [yen / year] = F_A_6 (THWb) [times / day] x ZA [yen / times] x 365 [day / year]

-Risk prediction formula based on steering entropy of straight road scene
Risk1_A_7 [times / year] = F_A_7 (Hp) [times / day] x 365 [days / year]
Risk2_A_7 [yen / year] = F_A_7 (Hp) [times / day] x YA [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_A_7 [yen / year] = F_A_7 (Hp) [times / day] x ZA [yen / times] x 365 [day / year]

・ Rear impact risk
-Risk prediction formula based on the preceding vehicle following scene
Risk1_B_1 [times / year] = F_B_1 (THWf) [times / day] x 365 [days / year]
Risk2_B_1 [yen / year] = F_B_1 (THWf) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_1 [yen / year] = F_B_1 (THWf) [times / day] x ZB [yen / times] x 365 [day / year]

-Risk prediction formula based on the approaching vehicle scene
Risk1_B_2 [times / year] = F_B_2 (TTC) [times / day] x 365 [days / year]
Risk2_B_2 [yen / year] = F_B_2 (TTC) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_2 [yen / year] = F_B_2 (TTC) [times / day] × ZB [yen / times] × 365 [day / year]

-Risk prediction formula based on a single driving scene
Risk1_B_3 [times / year] = F_B_3 (THW) [times / day] x 365 [days / year]
Risk2_B_3 [yen / year] = F_B_3 (THW) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_3 [yen / year] = F_B_3 (THW) [times / day] x ZB [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line start scene
Risk1_B_4 [times / year] = F_B_4 (a) [times / day] x 365 [days / year]
Risk2_B_4 [yen / year] = F_B_4 (a) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_4 [yen / year] = F_B_4 (a) [times / day] x ZB [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line deceleration scene
Risk1_B_5 [times / year] = F_B_5 (m) [times / day] x 365 [days / year]
Risk2_B_5 [yen / year] = F_B_5 (m) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_5 [yen / year] = F_B_5 (m) [times / day] x ZB [yen / times] x 365 [day / year]

-Risk prediction formula based on the following scene from the following vehicle
Risk1_B_6 [times / year] = F_B_6 (THWb) [times / day] x 365 [days / year]
Risk2_B_6 [yen / year] = F_B_6 (THWb) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_6 [yen / year] = F_B_6 (THWb) [times / day] × ZB [yen / times] × 365 [day / year]

-Risk prediction formula based on driving scene on straight road
Risk1_B_7 [times / year] = F_B_7 (Hp) [times / day] x 365 [days / year]
Risk2_B_7 [yen / year] = F_B_7 (Hp) [times / day] x YB [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_B_7 [yen / year] = F_B_7 (Hp) [times / day] × ZB [yen / times] × 365 [day / year]

・ Road departure risk
-Risk prediction formula based on the preceding vehicle following scene
Risk1_C_1 [times / year] = F_C_1 (THWf) [times / day] x 365 [days / year]
Risk2_C_1 [yen / year] = F_C_1 (THWf) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_1 [yen / year] = F_C_1 (THWf) [times / day] × ZC [yen / times] × 365 [day / year]

-Risk prediction formula based on the approaching vehicle scene
Risk1_C_2 [times / year] = F_C_2 (TTC) [times / day] x 365 [days / year]
Risk2_C_2 [yen / year] = F_C_2 (TTC) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_2 [yen / year] = F_C_2 (TTC) times / day] × ZC [yen / times] × 365 [day / year]

-Risk prediction formula based on a single driving scene
Risk1_C_3 [times / year] = F_C_3 (THW) [times / day] x 365 [days / year]
Risk2_C_3 [yen / year] = F_C_3 (THW) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_3 [yen / year] = F_C_3 (THW) [times / day] x ZC [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line start scene
Risk1_C_4 [times / year] = F_C_4 (a) [times / day] x 365 [days / year]
Risk2_C_4 [yen / year] = F_C_4 (a) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_4 [yen / year] = F_C_4 (a) [times / day] x ZC [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line deceleration scene
Risk1_C_5 [times / year] = F_C_5 (m) [times / day] x 365 [days / year]
Risk2_C_5 [yen / year] = F_C_5 (m) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_5 [yen / year] = F_C_5 (m) [times / day] x ZC [yen / times] x 365 [day / year]

-Risk prediction formula based on the following scene from the following vehicle
Risk1_C_6 [times / year] = F_C_6 (THWb) [times / day] x 365 [days / year]
Risk2_C_6 [yen / year] = F_C_6 (THWb) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_6 [yen / year] = F_C_6 (THWb) [times / day] × ZC [yen / times] × 365 [day / year]

-Risk prediction formula based on driving scene on straight road
Risk1_C_7 [times / year] = F_C_7 (Hp) [times / day] x 365 [days / year]
Risk2_C_7 [yen / year] = F_C_7 (Hp) [times / day] x YC [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_C_7 [yen / year] = F_C_7 (Hp) [times / day] × ZC [yen / times] × 365 [day / year]

・ Risk at intersection
-Risk prediction formula based on the preceding vehicle following scene
Risk1_D_1 [times / year] = F_D_1 (THWf) [times / day] x 365 [days / year]
Risk2_D_1 [yen / year] = F_D_1 (THWf) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_1 [yen / year] = F_D_1 (THWf) [times / day] × ZD [yen / times] × 365 [day / year]

-Risk prediction formula based on the approaching vehicle scene
Risk1_D_2 [times / year] = F_D_2 (TTC) [times / day] x 365 [days / year]
Risk2_D_2 [yen / year] = F_D_2 (TTC) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_2 [yen / year] = F_D_2 (TTC) [times / day] × ZD [yen / times] × 365 [day / year]

-Risk prediction formula based on a single driving scene
Risk1_D_3 [times / year] = F_D_3 (THW) [times / day] x 365 [days / year]
Risk2_D_3 [yen / year] = F_D_3 (THW) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_3 [yen / year] = F_D_3 (THW) [times / day] x ZD [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line start scene
Risk1_D_4 [times / year] = F_D_4 (a) [times / day] x 365 [days / year]
Risk2_D_4 [yen / year] = F_D_4 (a) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_4 [yen / year] = F_D_4 (a) [times / day] x ZD [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line deceleration scene
Risk1_D_5 [times / year] = F_D_5 (m) [times / day] x 365 [days / year]
Risk2_D_5 [yen / year] = F_D_5 (m) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_5 [yen / year] = F_D_5 (m) [times / day] x ZD [yen / times] x 365 [day / year]

-Risk prediction formula based on the following scene from the following vehicle
Risk1_D_6 [times / year] = F_D_6 (THWb) [times / day] x 365 [days / year]
Risk2_D_6 [yen / year] = F_D_6 (THWb) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_6 [yen / year] = F_D_6 (THWb) [times / day] × ZD [yen / times] × 365 [day / year]

-Risk prediction formula based on driving scene on straight road
Risk1_D_7 [times / year] = F_D_7 (Hp) [times / day] × 365 [days / year]
Risk2_D_7 [yen / year] = F_D_7 (Hp) [times / day] x YD [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_D_7 [yen / year] = F_D_7 (Hp) [times / day] × ZD [yen / times] × 365 [day / year]

・ Risk for vulnerable traffic
-Risk prediction formula based on the preceding vehicle following scene
Risk1_E_1 [times / year] = F_E_1 (THWf) [times / day] x 365 [days / year]
Risk2_E_1 [yen / year] = F_E_1 (THWf) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_1 [yen / year] = F_E_1 (THWf) [times / day] x ZE [yen / times] x 365 [day / year]

-Risk prediction formula based on the approaching vehicle scene
Risk1_E_2 [times / year] = F_E_2 (TTC) [times / day] x 365 [days / year]
Risk2_E_2 [yen / year] = F_E_2 (TTC) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_2 [yen / year] = F_E_2 (TTC) [times / day] x ZE [yen / times] x 365 [day / year]

-Risk prediction formula based on a single driving scene
Risk1_E_3 [times / year] = F_E_3 (THW) [times / day] x 365 [days / year]
Risk2_E_3 [yen / year] = F_E_3 (THW) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_3 [yen / year] = F_E_3 (THW) [times / day] x ZE [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line start scene
Risk1_E_4 [times / year] = F_E_4 (a) [times / day] x 365 [days / year]
Risk2_E_4 [yen / year] = F_E_4 (a) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_4 [yen / year] = F_E_4 (a) [times / day] x ZE [yen / times] x 365 [day / year]

-Risk prediction formula based on the stop line deceleration scene
Risk1_E_5 [times / year] = F_E_5 (m) [times / day] x 365 [days / year]
Risk2_E_5 [yen / year] = F_E_5 (m) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_5 [yen / year] = F_E_5 (m) [times / day] x ZE [yen / times] x 365 [day / year]

-Risk prediction formula based on the following scene from the following vehicle
Risk1_E_6 [times / year] = F_E_6 (THWb) [times / day] × 365 [days / year]
Risk2_E_6 [yen / year] = F_E_6 (THWb) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_6 [yen / year] = F_E_6 (THWb) [times / day] x ZE [yen / times] x 365 [day / year]

-Risk prediction formula based on driving scene on straight road
Risk1_E_7 [times / year] = F_E_7 (Hp) [times / day] × 365 [days / year]
Risk2_E_7 [yen / year] = F_E_7 (Hp) [times / day] x YE [closed days / times] x P [yen / closed days] x 365 [days / year]
Risk3_E_7 [yen / year] = F_E_7 (Hp) [times / day] x ZE [yen / times] x 365 [day / year]

  Thus, after constructing | assembling a risk prediction formula by step S104, this process is complete | finished (step S105).

  Next, the risk index calculation process in step S24 will be described using the flowchart of FIG. When the risk index calculation process is started in step S106, the driver's various driving action indices measured in step S22 are read in step S107. Specifically, the average value of the following driving action index is read.

・ The average value THWf_D of the following inter-vehicle time when following the preceding vehicle
・ Average TTC_D of spare time TTC when approaching the vehicle on that day
・ Average value Δv_D of the difference between the vehicle speed and the legal speed when traveling alone on that day
・ Average maximum acceleration a_D at the time of departure from the stop line on that day
・ Average distance m_D at the start of brake operation when decelerating on the temporary stop line on that day
・ The average value THWb_D of the following inter-vehicle time with the following vehicle when the vehicle is following the vehicle on that day
・ Average steering entropy Hp_D when driving on a straight road on that day

  In step S108, the driver's driving behavior index read in step S107 is applied to the risk prediction formula read in step S23, and risk indexes Risk1, Risk2, Risk3 are calculated. Thereby, this process is completed (step S109). FIG. 14 to FIG. 16 show three types of risk indices Risk1, Risk2, Risk3 for each driving behavior index for each risk form.

  Next, the comprehensive risk index creation process in step S25 will be described. Here, a comprehensive risk index is calculated based on the individual risk indexes calculated in step S24. There are the following four types of comprehensive risk index calculation methods, and which method is used for the calculation is set in advance. That is, any one of the creation method programs may be stored in the memory 13 in advance, or all four types may be stored in the memory 13 so that the calculation method can be selected when necessary.

Method 1: A comprehensive risk index is calculated from a risk index calculation result based on a plurality of driving behavior indexes for a specific risk form. The total risk index calculation procedure according to method 1 will be described with reference to the flowchart of FIG.

  When the calculation process of the total risk index is started in step S111, the calculation result of the risk index in various driving action indexes for a specific risk form is read in step S112. In step S113, the maximum value is selected as the comprehensive risk index from the calculation results of the risk index based on the various driving behavior indexes, and this process is terminated (step S114).

  18 to 20 show, as an example, comprehensive risk indices Risk1, Risk2, Risk3 calculated when the rear-end collision risk is a specific risk form. As shown in FIG. 18, the overall risk index Risk1 indicating the risk occurrence probability per year is calculated as Risk1 = MAX (Risk1_A_1, Risk1_A_2, Risk1_A_3, Risk1_A_4, Risk1_A_5, Risk1_A_6, Risk1_A_7). As shown in FIG. 19, the overall risk index Risk2 indicating the forecast of income that cannot be received due to lost work due to risk per year is calculated as Risk2 = MAX (Risk2_A_1, Risk2_A_2, Risk2_A_3, Risk2_A_5, Risk2_A_6, Risk2_A_7) . As shown in FIG. 20, the risk index Risk3 indicating the increase in insurance premiums predicted by the risk occurrence per year is calculated as Risk3 = MAX (Risk3_A_1, Risk3_A_2, Risk3_A_3, Risk3_A_4, Risk3_A_5, Risk3_A_6, Risk3_A_7) .

Method 2: A comprehensive risk index is calculated from risk index calculation results for a plurality of risk forms in a specific driving behavior index. The calculation procedure of the comprehensive risk index by the method 2 will be described using the flowchart of FIG.

  When the calculation process of the comprehensive risk index is started in step S121, the calculation result of the risk index of various risk forms for the specific driving action index is read in step S122. In step S123, the maximum value is selected as the total risk index from the calculation results of the risk index in various risk forms, and this process ends (step S124).

  22 to 24 show, as an example, total risk indexes Risk1, Risk2, Risk3 calculated when the inter-vehicle time of the preceding vehicle following scene is set as a specific driving action index. As shown in FIG. 22, the overall risk index Risk1 indicating the risk occurrence probability per year is calculated as Risk1 = MAX (Risk1_A_1, Risk1_B_1, Risk1_C_1, Risk1_D_1, Risk1_E_1). As shown in FIG. 23, the overall risk index Risk2 indicating the income prediction that cannot be received due to the absence of work due to risk per year is calculated as Risk2 = MAX (Risk2_A_1, Risk2_B_1, Risk2_C_1, Risk2_D_1, Risk2_E_1). As shown in FIG. 24, a risk index Risk3 indicating an increase in insurance premium predicted by risk occurrence per year is calculated as Risk3 = MAX (Risk3_A_1, Risk3_B_1, Risk3_C_1, Risk3_D_1, Risk3_E_1).

Method 3: For each risk form, select a risk index calculation result of a representative driving behavior index, and calculate an overall risk index among them. The calculation procedure of the comprehensive risk index by the method 3 will be described using the flowchart of FIG. In FIG. 26, an example of the typical driving | operation index of each risk form is shown. If there are a plurality of typical driving behavior indicators for a certain risk form, they are all picked up.

  As shown in FIG. 26, for rear-end collision risk, an inter-vehicle time in the preceding vehicle following scene and a margin time in the preceding vehicle approaching scene are selected. For the rear-end collision risk, the rear inter-vehicle time in the following scene from the following vehicle is selected. For out-of-road departure risk, the speed difference of the single traveling scene and the steering entropy of the straight road traveling scene are selected. For the risk at the intersection, the brake operation start distance (timing) in the temporary stop line deceleration scene is selected. For the risk to traffic weak people, the maximum acceleration of the stop line start scene is selected.

  When the calculation process of the total risk index is started in step S131, the risk index calculation result of the representative driving action index in each risk form is read in step S132. In step S133, the maximum value is selected as the total risk index from the calculation results of all the read risk indices, and this process is terminated (step S134).

  27 to 29 show total risk indices Risk1, Risk2, Risk3 calculated when the driving action index shown in FIG. 26 is selected as a representative driving action index. As shown in FIG. 27, the overall risk index Risk1 indicating the risk occurrence probability per year is calculated as Risk1 = MAX (Risk1_A_1, Risk1_A_2, Risk1_B_6, Risk1_C_3, Risk1_C_7, Risk1_D_5, Risk1_E_4). As shown in FIG. 28, the overall risk index Risk2 indicating the income prediction that cannot be received due to the absence of work due to risk per year is calculated as Risk2 = MAX (Risk2_A_1, Risk2_A_2, Risk2_B_6, Risk2_C_7, Risk2_D_5, Risk2_E_4) . As shown in FIG. 29, the risk index Risk3 indicating the increase in premiums predicted by the risk occurrence per year is calculated as Risk3 = MAX (Risk3_A_1, Risk3_A_2, Risk3_B_6, Risk3_C_3, Risk3_C_7, Risk3_D_5, Risk3_E_4) .

Method 4: A comprehensive risk index is calculated from the risk index calculation results for a plurality of driving behavior indices and a plurality of risk carriers. The calculation procedure of the comprehensive risk index by the method 4 will be described using the flowchart of FIG.

  When the calculation process of the comprehensive risk index is started in step S141, risk index calculation results of various risk forms for a plurality of driving action indexes are read in step S142. In step S143, the maximum value is selected as the comprehensive risk index from the calculation results of all the read risk indices, and this process is terminated (step S144). The comprehensive risk index is an evaluation of the driver's driving behavior, and represents the risk that can be predicted from the driving behavior as a quantitative index such as risk occurrence frequency, decrease in income, or increase in insurance premium.

  In FIG. 31 to FIG. 33, the total risk index Risk1, Risk2, Risk3 calculated by the method 4 is shown. As shown in FIG. 31, the overall risk index Risk1 indicating the risk occurrence probability per year is calculated as Risk1 = MAX (Risk1_A_1 to 7, Risk1_B_1 to 7, Risk1_C_1 to 7, Risk1_D_1 to 7, Risk1_E_1 to 7). . As shown in FIG. 32, the overall risk index Risk2 indicating the income forecast that cannot be received due to the absence of work due to risk per year is Risk2 = MAX (Risk2_A_1 to 7, Risk2_B_1 to 7, Risk2_C_1 to 7, Risk2_D_1 to 7, Risk2_E_1 to Calculated as 7). As shown in FIG. 33, the risk index Risk3 indicating the increase in premiums predicted by the risk occurrence per year is Risk3 = MAX (Risk3_A_1˜7, Risk3_B_1˜7, Risk3_C_1˜7, Risk3_D_1˜7, Risk3_E_1˜ Calculated as 7).

  Next, the notification process to the driver in step S26 will be described with reference to the flowchart of FIG. When the notification process to the driver is started in step S151, a comprehensive risk index to be presented to the driver is selected in step S152. Specifically, one of the three types of total risk index Risk1, Risk2, Risk3 calculated in step S25 is selected as the total risk index to be presented to the driver. This enables the driver to select a desired overall risk index by operating a touch panel displayed on the screen of the display device 16, for example. Alternatively, a comprehensive risk index that is set in advance for presentation can also be displayed.

In step S153, the average value Av and standard deviation σ of the past three months of the comprehensive risk index selected in step S152 are calculated. In step S154, the total risk index is compared with the specified value C1. The specified value C1 is calculated from the following (Equation 1) using the average value Av and the standard deviation σ of the total risk index calculated in step S153.
C1 = Av + 2σ (Formula 1)
When the total risk index is larger than the specified value C1, the process proceeds to step S155.

  In step S155, the comprehensive risk index is presented to the driver. Here, notification by voice is automatically performed, and notification by display is performed manually. For example, if Risk3 is selected as the overall risk index for presentation, the voice device 17 will say "Please be careful. You are in a high-risk state. If you continue driving now, the premium for the next year will increase by 30,000 yen. Audio information is output. Further, for example, as shown in FIG. 35, the display device 16 displays a touch panel button for displaying and selecting “risk diagnosis result” superimposed on the display screen of the navigation system.

If a negative determination is made in step S154, the process proceeds to step S156, and the comprehensive risk index comprehensive risk index is compared with the specified value C2. The specified value C1 is calculated from the following (Equation 2) using the average value Av and the standard deviation σ of the total risk index calculated in step S153.
C2 = Av + σ (Formula 2)
When the total risk index is larger than the specified value C2, the process proceeds to step S157. If the total risk index is less than or equal to the specified value C2, the process proceeds to step S158, and this process is terminated without notifying the driver.

  In step S157, the comprehensive risk index is presented to the driver. Here, notification by display is performed manually. As shown in FIG. 36, for example, the display device 16 displays a touch panel button for selecting and displaying “risk diagnosis result” superimposed on the display screen of the navigation system. This is the end of this process (step S159).

  When the “risk diagnosis result” button displayed in step S155 or S157 is operated, the display screen shown in FIGS. 37 to 39 is displayed. However, the “risk diagnosis result” button can be operated only when the vehicle is stopped. When the button is operated during driving, the voice device 17 gives a voice notification “cannot display during driving” and displays the display screen. Will not be transferred.

  37 (a) and 37 (b) are display screens when Risk1 indicating a risk occurrence probability per year is selected as a comprehensive risk index for presentation, and FIGS. 38 (a) and 38 (b) are comprehensive risks for presentation. The display screen when Risk2 is selected to indicate the income forecast that cannot be received due to absence from work due to risk per year, and Figures 39 (a) and 39 (b) are the risk occurrence per year as a comprehensive risk index for presentation. Examples of display screens when Risk3 indicating incremental prediction of insurance premiums that rise in price are selected are shown.

  When the “risk diagnosis result” button is operated, the display screen of the display device 16 relates to a comprehensive risk index for presentation as shown in FIG. 37 (a), FIG. 38 (a), or FIG. 39 (a). Information is displayed. When Risk1 is selected, as shown in FIG. 37 (a), for example, the text “If you continue driving now, you may encounter up to 0.8 risks in one year” is displayed. When Risk2 is selected, as shown in FIG. 38A, for example, the text “If the current driving is continued, there is a possibility that annual income may decrease by 120,000 yen at the maximum” is displayed. When Risk3 is selected, as shown in FIG. 39 (a), for example, the text “If the current driving is continued, there is a possibility that the premium for the next year will increase by up to 30,000 yen” is displayed.

  A “return to navigation button” is displayed in the lower area of these display screens. Further, when the total risk index is calculated by any one of the methods 2 to 4 described above, a “go to details” button as shown in FIG. 37 (a), FIG. 38 (a), and FIG. 39 (a) is displayed. Is done. When the total risk index is calculated by the method 1, the “go to details” button is not displayed.

  When the “go to details” button is operated, the display screen of the display device 16 includes information on the overall risk index for presentation as shown in FIG. 37 (b), FIG. 38 (b), or FIG. 39 (b). Is displayed. In this case, the above-described plurality of risk forms are classified into a forward risk, a backward risk, and a lateral risk, and information about each is presented. For the risk ahead, the largest risk index for the rear-end collision risk, the risk at the intersection, and the risk to the traffic weak is selected and displayed. The risk at the rear shows the risk index for the rear-end collision risk. Side risk displays a risk indicator for off-road departure risk.

  When the total risk index is calculated by the method 3 or method 4 described above, as shown in FIGS. 37 (b), 38 (b), and 39 (b), the detailed information is displayed in the lower area of the display screen. , "Show advice" button is displayed. When the “display advice” button is operated, the screen shifts to an advice display screen for risk reduction as described later. When the total risk index is calculated by the method 1 or the method 2, the “display advice” button is not displayed. The display of advice corresponds to the process of presenting advice to the driver in step S27 in the main flowchart of FIG. That is, when the total risk index is calculated by method 3 or method 4, after notifying the driver in step S26, the process proceeds to step S27 and proceeds to the process of presenting advice to the driver. When the total risk index is calculated by step S26, after notifying the driver in step S26, step S27 is skipped and the process is terminated.

  The advice presentation process to the driver in step S27 will be described with reference to the flowchart of FIG. When the process of presenting advice to the driver is started in step S161, the driving action for presenting the advice to the driver is extracted in step S162. Specifically, it is determined whether the maximum value of the risk index calculated for each driving action index (the value in method 3 is only one) exceeds the aforementioned discriminant value C2 (= Av + σ). To do. Then, the driving behavior index corresponding to the risk index exceeding the discriminant C2 is stored.

  In step S163, an advice screen is displayed on the display device 16. Specifically, for the driving action index corresponding to the risk index determined to exceed the discriminant value C2 in step S162, a message as shown in the table of FIG. 41 is read and an advice screen is displayed. FIG. 42 shows a display example of the advice screen when the inter-vehicle time in the preceding vehicle following scene and the steering entropy in the straight road traveling scene each exceed the discriminant value C2. This is the end of this process (step S164).

  As shown in FIG. 41, when the inter-vehicle time in the preceding vehicle following scene exceeds the discriminant value C2, a message “The inter-vehicle time is short. Let's take a wide inter-vehicle space.” Is displayed. When the margin time of the preceding vehicle approaching scene exceeds the discriminant value C2, a message “Brake timing is late. Let's try early brake operation” is displayed. When the speed difference in the single driving scene exceeds the discriminant value C2, a message “Speed is high. Let's drive at a speed according to the traffic environment” is displayed. When the maximum acceleration of the pause line start scene exceeds the discriminant value C2, a message “Start is sudden. Try to start calmly” is displayed.

  If the brake operation start distance (timing) in the pause line deceleration scene exceeds the discriminant value C2, the message "Brake timing on the pause line is late. Try to brake early." . When the inter-vehicle time in the following vehicle following scene exceeds the discriminant value C2, a message “The following vehicle is approaching. When the steering entropy of the straight road running scene exceeds the discriminant value C2, the message “The steering wheel operation is steep. Let's drive while paying attention to the traffic environment” is displayed.

Thus, according to the embodiment described above, the following operational effects can be achieved.
(1) A driving behavior index representing the driving behavior of the driver is detected, and a risk index representing the magnitude of the risk is calculated based on the driving behavior index. Furthermore, a comprehensive risk index is calculated based on the risk index, the risk state is notified to the driver, and advice for reducing the risk is presented to the driver as necessary. Since the risk index is calculated based on the driving behavior of the driver, it is possible to present information according to the driving characteristics of the individual driver. Moreover, since a comprehensive risk index is presented, it is possible to present information that is concise and easy to understand.
(2) As a driving action index, a physical index that represents the running state of the vehicle and a physical index that represents the operating state of the vehicle by the driver are detected. Specifically, inter-vehicle time when following the preceding vehicle, margin time when approaching the preceding vehicle, amount of difference between own vehicle speed and legal speed when traveling alone, starting acceleration from the stop line, and deceleration at the stop line The distance between the own vehicle and the temporary stop line at the start of braking operation at the time, the inter-vehicle time with the following vehicle when following the following vehicle, and the steering entropy when traveling on a straight road are acquired. As a result, the driving characteristics of the driver appearing in the vehicle running state and the driver operating state can be accurately grasped.
(3) Since the physical index is detected in the driving scene specified in advance, the vehicle driving state and the driver operating state can be detected under certain conditions, and the driving characteristics of the driver can be accurately grasped.
(4) Since the risk index is calculated by applying the driving action index to a preset risk prediction formula, the risk index can be calculated quickly.
(5) The risk prediction formula is set so as to calculate risk indicators for rear-end collision risk, rear-end collision risk, off-road departure risk, intersection risk, and risk to vulnerable road users. Therefore, a risk index corresponding to various risks can be calculated.
(6) As the risk index, the probability of occurrence of a specific risk and the result of the actual occurrence of that risk are used. For example, the probability of occurrence of risk per year, the prediction of income that cannot be received due to leave due to the occurrence of risk per year, and the incremental prediction of premiums that increase due to the occurrence of risk per year are calculated as risk indicators. By using such a risk index, it is easy to imagine the result of the driving behavior of the driver, and information that the driver can easily sympathize with can be presented.
(7) The risk prediction formula is set based on the relationship of the risk occurrence frequency with respect to the driving behavior indexes of many drivers. Specifically, a risk prediction formula is set using a risk occurrence cumulative frequency distribution corresponding to each driving behavior index as shown in FIGS. 11 and 12. Thereby, the driving behavior of the driver can be objectively evaluated.
(8) When calculating the total risk index by Method 1, a total risk index for a specific risk form is calculated using a plurality of risk indexes based on a plurality of driving behavior indexes. This is effective when it is desired to obtain information on a specific risk form.
(9) When calculating the total risk index by the method 2, the total risk index for a plurality of risk forms is calculated using a plurality of risk indexes based on a single driving action index. This is effective when it is desired to perform driving evaluation on a specific driving action index.
(10) When calculating the total risk index by the method 3, the total risk index for a plurality of risk forms respectively represented by the plurality of driving action indexes is calculated using the plurality of risk indexes. Since the comprehensive risk index is calculated for risk forms that may occur due to the driving behavior of the driver, it becomes possible to provide information that is easy to understand for the driver.
(11) When calculating the total risk index by the method 4, the total risk index for a plurality of risk forms is calculated using a plurality of risk indexes based on the plurality of driving behavior indexes. Thereby, it is possible to present comprehensive risk information in consideration of all driving behaviors and risk forms.
(12) When the risk state notification instruction is output, the risk state is notified. Specifically, when the “risk potential diagnosis result” button displayed on the display device 16 is operated, the driver is notified, so that information can be provided at a desired timing when the driver needs it. it can.
(13) Since the risk state is automatically notified when the total risk index exceeds a predetermined value, necessary information can be provided promptly.
(14) When the risk status is notified, the hierarchical display is performed from the comprehensive risk index to the individual risk index, so that necessary information can be provided to the driver in an easy-to-understand manner.
(15) When the advice presentation instruction is output, the advice is presented. Specifically, since the advice is presented when the “display advice” button displayed on the display device 16 is operated, information can be provided at a desired timing when the driver needs it. By presenting the advice, the driver can clearly know what to do to reduce the risk, and can guide the driver to improve his driving skills.

  In the above-described embodiment, various physical quantities such as the forward inter-vehicle time and the margin time are acquired as the driving action index, but the driving action index to be detected is not limited to the above. Other indicators may be added to the driving behavior indicator, or any of the indicators may be omitted. Also, the running scene and risk form are not limited to those described above.

  In the embodiment described above, the steering angle sensor 1, the brake pedal sensor 2, the accelerator pedal sensor 3, the acceleration sensor 4, the own vehicle speed sensor 5, the front inter-vehicle distance sensor 6, the rear inter-vehicle distance sensor 7, the own vehicle position sensor. 8, road shape database 9, stop line position database 10, statutory speed database 11, and driving behavior index calculation computer 12 function as driving behavior detection means, and risk calculation computer 15 calculates risk calculation means and total risk index. The display device 16 and the audio device 17 can function as a risk notification unit and an advice presentation unit. For example, the driving behavior detection means is not limited to these, and a suitable driving behavior can be used according to the driving behavior to be evaluated and the risk form. It is also possible to use the display device 16 or the audio device 17 for risk notification and advice presentation. The above description is merely an example, and when interpreting the invention, there is no limitation or restriction on the correspondence between the items described in the above embodiment and the items described in the claims.

The figure which shows the system configuration | structure of the driving evaluation apparatus for vehicles by one embodiment of this invention. The block diagram of the vehicle carrying the driving evaluation apparatus for vehicles shown in FIG. The main flowchart which shows the operation | movement by the driving evaluation apparatus for vehicles. The flowchart which shows the process sequence of a driver | operator's driving behavior measurement process. The flowchart explaining the driving | running | working action measurement process in a preceding vehicle follow-up scene, a preceding vehicle approach scene, and an independent driving | running | working scene. The flowchart explaining the driving action measurement process in a temporary stop line start scene and a temporary stop line deceleration scene. The flowchart explaining the driving | running | working action measurement process in the to-be-followed scene from a following vehicle. The flowchart explaining the driving action measurement process in a straight road running scene. The flowchart which shows the process sequence of a risk prediction type reading process. The figure which shows the risk generation result for every risk form. The figure which shows the risk generation | occurrence | production cumulative frequency distribution with respect to the driving action parameter | index for every risk form. The figure which shows the risk generation | occurrence | production cumulative frequency distribution with respect to the driving action parameter | index for every risk form. The flowchart which shows the process sequence of a risk calculation process. The figure which shows the calculation result of the risk parameter | index Risk1 showing the risk occurrence probability per year. The figure which shows the calculation result of the risk index Risk2 showing the income prediction which cannot be received by the absence of work due to the risk per year. The figure which shows the calculation result of risk index Risk3 showing the incremental prediction of the premium which rises with the risk occurrence per year. 9 is a flowchart showing a processing procedure of a comprehensive risk index calculation process according to Method 1. The figure which shows the example of calculation of the comprehensive risk index Risk1 by the method 1. The figure which shows the example of calculation of the comprehensive risk index Risk2 by the method 1. The figure which shows the example of calculation of the comprehensive risk index Risk3 by the method 1. 9 is a flowchart showing a processing procedure of a comprehensive risk index calculation process according to method 2. The figure which shows the example of calculation of the comprehensive risk index Risk1 by the method 2. The figure which shows the example of calculation of the comprehensive risk index Risk2 by the method 2. The figure which shows the calculation example of the comprehensive risk index Risk3 by the method 2. 10 is a flowchart showing a processing procedure of a comprehensive risk index calculation process according to Method 3. The figure which shows the driving action parameter | index representative of the various risk forms by the method 3. The figure which shows the calculation example of the comprehensive risk index Risk1 by the method 3. The figure which shows the example of calculation of the comprehensive risk index Risk2 by the method 3. The figure which shows the example of calculation of the comprehensive risk index Risk3 by the method 3. 9 is a flowchart showing a processing procedure of a comprehensive risk index calculation process according to method 4. The figure which shows the example of calculation of the comprehensive risk index Risk1 by the method 4. The figure which shows the example of calculation of the comprehensive risk index Risk2 by the method 4. The figure which shows the example of calculation of the comprehensive risk index Risk3 by the method 4. The flowchart which shows the process sequence of the alerting | reporting process to a driver. The figure explaining the audio | voice alerting | reporting of a comprehensive risk parameter | index. The figure which shows the manual display selection screen of a comprehensive risk parameter | index. (A) (b) The figure which shows the example of a display of the comprehensive risk parameter | index Risk1 showing the risk occurrence probability per year. (A) (b) The figure which shows the example of a display of comprehensive risk parameter | index Risk2 showing the income prediction which cannot be received by the absence of work due to the risk generation per year. (A) (b) The figure which shows the example of a display of comprehensive risk parameter | index Risk3 showing the incremental prediction of the premium which raises with the risk generation per year. The flowchart which shows the process sequence of the advice presentation process to a driver. The figure which shows the advice shown to a driver corresponding to each driving action parameter | index. The figure which shows an example of an advice display screen.

Explanation of symbols

1: steering angle sensor, 2: brake pedal sensor, 3: accelerator pedal sensor 3, 4: acceleration sensor, 5: own vehicle speed sensor, 6: front inter-vehicle distance sensor, 7: rear inter-vehicle distance sensor, 8: own vehicle position Sensor: 9: Road shape database, 10: Temporary stop line position database, 11: Legal speed database, 12: Driving behavior index calculation computer, 13: Memory, 14: Risk prediction formula database, 15: Risk calculation computer, 16 : Display device, 17: Audio device, 18: Controller

Claims (17)

Driving behavior detecting means for detecting a driving behavior index representing the driving behavior of the driver;
Risk calculating means for calculating a risk index representing the magnitude of the risk based on the driving action index detected by the driving action detecting means;
Total risk index calculation means for calculating a total risk index based on the risk index calculated by the risk calculation means;
Risk notification means for notifying the driver of the state of the risk based on the total risk index calculated by the total risk index calculation means;
A vehicle driving evaluation apparatus comprising: advice presenting means for presenting advice for reducing the risk to the driver based on the comprehensive risk index calculated by the comprehensive risk index calculating means. The vehicle operation evaluation apparatus according to claim 1,
The vehicle driving evaluation device is characterized in that the driving behavior detecting means detects a physical index representing a running state of a vehicle and a physical index representing an operation state of the vehicle by the driver as the driving behavior index. In the vehicle driving evaluation apparatus according to claim 2,
The vehicle driving evaluation device, wherein the driving behavior detecting means detects the physical index in a specific running scene set in advance. In the vehicle driving evaluation apparatus according to any one of claims 1 to 3,
The vehicle risk evaluation unit is characterized in that the risk index is calculated by applying the driving action index to a preset risk prediction formula. In the vehicle driving evaluation apparatus according to claim 4,
The risk prediction formula is set so as to calculate a risk index relating to a rear-end collision risk, a rear-end collision risk, an out-of-road departure risk, a risk at an intersection, and a risk to a traffic weak person, respectively. Evaluation device. In the vehicle driving evaluation apparatus according to claim 5,
The risk calculation means uses a probability of occurrence of a specific risk and a result of the occurrence of the risk as the risk index. In the vehicle driving evaluation apparatus according to claim 4,
The risk evaluation formula is set based on a relationship of risk occurrence frequency with respect to driving behavior indexes of a large number of drivers. The vehicle driving evaluation apparatus according to any one of claims 1 to 7,
The driving behavior detecting means detects a plurality of driving behavior indices,
The risk calculating means calculates a risk index based on the plurality of driving behavior indexes,
The overall risk index calculating means calculates a total risk index for a specific risk form using a plurality of risk indexes based on the plurality of driving action indexes. The vehicle driving evaluation apparatus according to any one of claims 1 to 7,
The risk calculating means calculates a plurality of risk indexes corresponding to a plurality of risk forms based on the driving behavior index,
The overall risk index calculating means calculates a total risk index for a plurality of risk forms using the plurality of risk indices based on a single driving action index. The vehicle driving evaluation apparatus according to any one of claims 1 to 7,
The driving behavior detecting means detects a plurality of driving behavior indices,
The risk calculating means calculates a plurality of risk indicators corresponding to a plurality of risk forms respectively represented by the plurality of driving behavior indicators,
The overall risk index calculating means calculates an overall risk index for the plurality of risk forms by using the plurality of risk indices. The vehicle driving evaluation apparatus according to any one of claims 1 to 7,
The driving behavior detecting means detects a plurality of driving behavior indices,
The risk calculating means calculates a risk index based on the plurality of driving behavior indexes,
The overall risk index calculation means calculates a total risk index for a plurality of risk forms using a plurality of risk indexes based on the plurality of driving action indexes. The vehicle operation evaluation apparatus according to any one of claims 1 to 11,
Further comprising a risk notification instruction means for instructing whether to notify the risk state by the risk notification means;
The vehicle driving evaluation apparatus, wherein the risk notification means notifies the state of the risk according to a signal from the risk notification instruction means. The vehicle driving evaluation apparatus according to any one of claims 1 to 12,
The vehicle driving evaluation device, wherein the risk notification means automatically notifies the risk state when the total risk index exceeds a predetermined value. The vehicle driving evaluation apparatus according to any one of claims 1 to 13,
The vehicle risk evaluation unit displays the hierarchy from the comprehensive risk index to an individual risk index. The vehicle driving evaluation apparatus according to any one of claims 1 to 14,
An advice presentation instruction means for instructing whether to present the advice by the advice presentation means;
The vehicle driving evaluation device, wherein the advice presenting means presents the advice in accordance with a signal from the advice presenting instruction means. Detect driving behavior indicators that represent the driving behavior of the driver,
Based on the detected driving behavior index, a risk index representing the magnitude of the risk is calculated,
Calculating an overall risk index based on the calculated risk index;
Based on the calculated overall risk index, inform the driver of the risk status,
A vehicle driving evaluation method characterized by presenting the driver with advice for reducing the risk based on the calculated overall risk index. Driving behavior detecting means for detecting a driving behavior index representing the driving behavior of the driver;
Risk calculating means for calculating a risk index representing the magnitude of the risk based on the driving action index detected by the driving action detecting means;
Total risk index calculation means for calculating a total risk index based on the risk index calculated by the risk calculation means;
Risk notification means for notifying the driver of the state of the risk based on the total risk index calculated by the total risk index calculation means;
A vehicle driving evaluation device comprising: an advice presenting means for presenting an advice for reducing the risk to the driver based on the comprehensive risk index calculated by the comprehensive risk index calculating means. vehicle.

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