JP-7857421-B2 - Driving skills evaluation method, driving skills evaluation system, and recording medium

Inventors

• 鳥居 武史
• 佐藤 能英瑠

Assignees

• 株式会社SUBARU

Dates

Publication Date

20260512

Application Date

20221031

Claims (7)

1. A kernel density estimation image is generated based on time-series data of a first parameter corresponding to the change in the direction of travel of the vehicle, and time-series data of a second parameter representing the square of the jerk in the direction of travel of the vehicle. This includes calculating image similarity by comparing the kernel density estimation image with a reference image obtained based on driving data of a skilled driver , and evaluating whether the driving skills of the vehicle's driver are similar to those of the skilled driver based on the image similarity, thereby evaluating the driving skills of the vehicle's driver. The first parameter is one of the following: a parameter relating to yaw angular velocity, a parameter relating to yaw angular acceleration, a parameter relating to the square of yaw angular acceleration, a parameter relating to lateral acceleration, or a parameter relating to the square of lateral acceleration. Driving skill evaluation method.
2. The driving skill evaluation method according to claim 1, wherein the time-series data of the first parameter and the time-series data of the second parameter are data obtained when the vehicle is traveling on a curve.
3. In the kernel density estimation image, the first image direction indicates time. The second image direction in the kernel density estimation image indicates the first parameter, and the pixel values in the kernel density estimation image are values corresponding to the data of the second parameter. The driving skill evaluation method according to claim 1.
4. In the kernel density estimation image, the first image direction indicates time. The second image orientation in the kernel density estimation image indicates the second parameter, and the pixel values in the kernel density estimation image are values corresponding to the data of the first parameter. The driving skill evaluation method according to claim 1.
5. The driving skills evaluation method according to claim 1, further comprising presenting the driver with the results of the evaluation of the driver's driving skills.
6. An image generation circuit generates a kernel density estimation image based on time-series data of a first parameter corresponding to the change in the direction of travel of the vehicle, and time-series data of a second parameter indicating the square of the jerk in the direction of travel of the vehicle. The system includes an evaluation circuit that calculates image similarity by comparing the kernel density estimation image with a reference image obtained based on driving data of a skilled driver , and evaluates whether the driving skills of the vehicle's driver are similar to those of the skilled driver based on the image similarity, thereby evaluating the driving skills of the vehicle's driver. The first parameter is one of the following: a parameter relating to yaw angular velocity, a parameter relating to yaw angular acceleration, a parameter relating to the square of yaw angular acceleration, a parameter relating to lateral acceleration, or a parameter relating to the square of lateral acceleration. Driving skill evaluation system.
7. A kernel density estimation image is generated based on time-series data of a first parameter corresponding to the change in the direction of travel of the vehicle, and time-series data of a second parameter representing the square of the jerk in the direction of travel of the vehicle. Software is recorded that causes the processor to perform the following: calculate image similarity by comparing the kernel density estimation image with a reference image obtained based on the driving data of a skilled driver , and evaluate whether the driving skills of the vehicle's driver are similar to those of the skilled driver based on the image similarity, thereby evaluating the driving skills of the vehicle's driver. The first parameter is one of the following: a parameter relating to yaw angular velocity, a parameter relating to yaw angular acceleration, a parameter relating to the square of yaw angular acceleration, a parameter relating to lateral acceleration, or a parameter relating to the square of lateral acceleration.

Description

This disclosure relates to a driving skills evaluation method and a driving skills evaluation system for evaluating a driver's driving skills, as well as a recording medium on which software for evaluating a driver's driving skills is recorded. In recent years, technologies for evaluating the driving skills of drivers have been developed for vehicles such as automobiles. For example, Patent Document 1 discloses a technology for evaluating a driver's driving skills based on longitudinal and lateral acceleration when a vehicle is turning. Japanese Patent Publication No. 2020-019289 A driving skill evaluation method according to one embodiment of the present disclosure includes: generating a kernel density estimation image based on time-series data of a first parameter corresponding to a change in the direction of travel of the vehicle, and time-series data of a second parameter indicating the square of the jerk in the direction of travel of the vehicle; calculating image similarity by comparing the kernel density estimation image with a reference image obtained based on the driving data of a skilled driver ; and evaluating whether the driving skills of the vehicle driver are similar to those of a skilled driver based on the image similarity, thereby evaluating the driving skills of the vehicle driver. The first parameter is one of the following: a parameter relating to yaw angular velocity, a parameter relating to yaw angular acceleration, a parameter relating to the square of yaw angular acceleration, a parameter relating to lateral acceleration, or a parameter relating to the square of lateral acceleration. A driving skill evaluation system according to one embodiment of the present disclosure comprises an image generation circuit and an evaluation circuit. The image generation circuit generates a kernel density estimation image based on time-series data of a first parameter corresponding to the change in the direction of travel of the vehicle, and time-series data of a second parameter indicating the square of the jerk in the direction of travel of the vehicle. The evaluation circuit calculates image similarity by comparing the kernel density estimation image with a reference image obtained based on the driving data of a skilled driver , and evaluates whether the driving skills of the vehicle driver are similar to those of a skilled driver based on the image similarity, thereby evaluating the driving skills of the vehicle driver. The first parameter is one of the following: a parameter relating to yaw angular velocity, a parameter relating to yaw angular acceleration, a parameter relating to the square of yaw angular acceleration, a parameter relating to lateral acceleration, or a parameter relating to the square of lateral acceleration. A recording medium according to one embodiment of the present disclosure contains software that causes a processor to generate a kernel density estimation image based on time-series data of a first parameter corresponding to the change in the direction of travel of the vehicle, and time-series data of a second parameter indicating the square of the jerk in the direction of travel of the vehicle, and to calculate image similarity by comparing the kernel density estimation image with a reference image obtained based on the driving data of a skilled driver, and to evaluate the driving skills of the vehicle driver by evaluating whether the driving skills of the vehicle driver are similar to those of a skilled driver based on the image similarity. The first parameter is one of the following: a parameter relating to yaw angular velocity, a parameter relating to yaw angular acceleration, a parameter relating to the square of yaw angular acceleration, a parameter relating to lateral acceleration, or a parameter relating to the square of lateral acceleration. The accompanying drawings are provided for further understanding of this disclosure and are incorporated herein and constitute part of this specification. The drawings illustrate one embodiment and, together with the specification, serve to illustrate the principles of this disclosure. This is an explanatory diagram showing an example configuration of a driving skills evaluation system in which the driving skills evaluation method according to one embodiment of this disclosure is used.Figure 1 is a block diagram showing one example of a smartphone configuration.Figure 1 is a block diagram showing one example configuration of the server device 30.Figure 3 is an explanatory diagram showing the data stored in the memory unit 32.Figure 4 is an explanatory diagram showing an example of yaw angular velocity data and curve data.Figure 4 shows an example of a kernel density estimation image, which is represented by the image data shown.Figure 6 is an explanatory diagram illustrating an example of parameters in the kernel density estimation image.Figure 1 is an explanatory diagram illustrating one example configuration of the data proc