A quantitative evaluation method for driver characteristics

Abstract

The present invention discloses a quantitative evaluation method for driver characteristics, including: collecting lane change trajectory data and steering wheel angle data during the driver's driving process; classifying the lane change trajectory data and steering wheel angle data according to working conditions according to the speed of the vehicle, the distance between the vehicle and the front vehicle, and the speed of the front vehicle; processing the lane change trajectory data and steering wheel angle data of the driver under fixed working conditions to obtain a lane change trajectory cluster and a steering wheel angle cluster under the working condition; processing the lane change trajectory cluster and the steering wheel angle cluster respectively to obtain a lane change trajectory characteristic curve and a steering wheel angle characteristic curve; and quantitatively evaluating the driver's characteristics according to the lane change trajectory characteristic curve and the steering wheel angle characteristic curve. The method of the present invention analyzes the driver's daily lane change data, extracts driving characteristics, considers the driver's lane change trajectory and steering wheel angle, and proposes a driver's lane change trajectory characteristic curve and a steering wheel angle characteristic curve to describe the driver's driving characteristics.

Description

Technical Field

[0001] The invention belongs to the field of automobile dynamics control, and in particular relates to a quantitative evaluation method for driver characteristics. Background Art

[0002] As cars become more and more intelligent, various intelligent driving assistance systems have emerged to further ensure the active safety of car driving. As the driver is the most important participant in vehicle driving, analyzing and studying the driver's driving characteristics is of great significance to improving the existing intelligent driving assistance systems.

[0003] The Chinese invention patent application number is CN201910453895.7, which is titled as a method for early warning of safe driving of automobiles based on driver characteristics. It discloses adjusting the vehicle speed and early warning according to the driver characteristics, thereby improving driving safety. The Chinese invention patent application number is CN201810188786.2, which is titled as a method for identifying driver characteristics. It discloses identifying the characteristics of various driving situations of the driver in combination with the actual situation, thereby making the model closer to reality and having strong universality and promotion. However, the above-mentioned technology does not provide a specific method for quantitative evaluation of driver characteristics, and has limitations in adapting to intelligent driver assistance systems.

[0004] The age, gender, physiological and psychological state of the driver all affect their driving performance during the driving process. Therefore, different types of drivers have different control capabilities for the vehicle. The driver's driving operation directly affects the driving state of the vehicle. Therefore, the intelligent driving assistance system needs to adapt to the driving characteristics of different drivers and meet the driving needs of different drivers. However, most of the existing intelligent driving assistance systems are of uniform design and cannot be personalized according to drivers with different driving characteristics. In addition, the driver's characteristics have extremely strong nonlinear characteristics. Therefore, how to accurately and reasonably evaluate the driver's driving characteristics is a problem that needs to be solved. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method for quantitatively evaluating driver characteristics, so as to accurately and reasonably evaluate the driver's driving characteristics, so that the intelligent driving assistance system can perform personalized control according to drivers with different driving characteristics.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A quantitative evaluation method of driver characteristics of the present invention comprises the following steps:

[0008] 1) Collect lane change trajectory data and steering wheel angle data of the driver during driving;

[0009] 2) Classify the lane change trajectory data and steering wheel angle data according to the working conditions based on the vehicle speed, the distance between the vehicle and the front vehicle, and the speed of the front vehicle;

[0010] 3) Processing the lane change trajectory data and steering wheel angle data of the driver under a fixed working condition to obtain a lane change trajectory cluster and a steering wheel angle cluster under the working condition;

[0011] 4) processing the lane change trajectory cluster and the steering wheel angle cluster respectively to obtain a lane change trajectory characteristic curve and a steering wheel angle characteristic curve;

[0012] 5) Quantitatively evaluate the driver's characteristics based on the lane change trajectory characteristic curve and the steering wheel angle characteristic curve.

[0013] Furthermore, the step 1) specifically includes: collecting lane change trajectory data and steering wheel angle data of the driver during driving through a visual sensor and a steering wheel angle sensor.

[0014] Furthermore, the classification method in step 2) specifically includes:

[0015] 21) When the speed of the vehicle and the vehicle ahead are within the range of 0-40 km / h, it is a low-speed condition; when the speed of the vehicle and the vehicle ahead are within the range of 40-80 km / h, it is a medium-speed condition; when the speed of the vehicle and the vehicle ahead are within the range of 80-120 km / h, it is a high-speed condition; when the vehicle changes lanes, the distance between the vehicle and the vehicle ahead is within the range of 0-50m, it is an emergency lane change condition; when the vehicle changes lanes, the distance between the vehicle and the vehicle ahead is greater than 50m, it is a normal lane change condition;

[0016] There are several working conditions as follows:

[0017] Condition 1: The vehicle is at a low speed, the vehicle ahead is at a low speed, and the vehicle makes an emergency lane change;

[0018] Condition 2: The vehicle is at a medium speed, the vehicle ahead is at a medium speed, and the vehicle makes an emergency lane change;

[0019] Condition 3: The vehicle is in high-speed condition, the vehicle ahead is in high-speed condition, and the vehicle makes an emergency lane change;

[0020] Condition 4: The vehicle is at a low speed, the vehicle ahead is at a low speed, and the vehicle is changing lanes normally;

[0021] Condition 5: The vehicle is at a medium speed, the vehicle ahead is at a medium speed, and the vehicle is changing lanes normally;

[0022] Condition 6: The vehicle is in high-speed condition, the preceding vehicle is in high-speed condition, and the vehicle is changing lanes normally;

[0023] Condition 7: The vehicle is at a medium speed, the vehicle ahead is at a low speed, and the vehicle makes an emergency lane change;

[0024] Condition 8: The vehicle is at a medium speed, the vehicle ahead is at a low speed, and the vehicle is changing lanes normally;

[0025] Condition 9: The vehicle is at high speed, the vehicle ahead is at low speed, and the vehicle changes lanes urgently;

[0026] Condition 10: The vehicle is at high speed, the vehicle ahead is at low speed, and the vehicle is changing lanes normally;

[0027] Condition 11: The vehicle is in high-speed condition, the vehicle ahead is in medium-speed condition, and the vehicle changes lanes urgently;

[0028] Condition 12: The vehicle is operating at high speed, the vehicle ahead is operating at medium speed, and the vehicle is changing lanes normally.

[0029] Furthermore, in the step 3), the lane change trajectory data and the steering wheel angle data are filtered to filter out external noise interference and remove the data group obtained by the driver's operating error.

[0030] Furthermore, the method for obtaining the lane change trajectory characteristic curve and the steering wheel angle characteristic curve in step 4) specifically includes:

[0031] Based on the obtained lane change trajectory cluster and steering wheel angle cluster, the driver's lane change trajectory characteristic curve and steering wheel angle characteristic curve are expressed as:

[0032]

[0033] Where m represents the number of sampling points; n represents the number of lane-changing trajectories in the lane-changing trajectory cluster; y op Indicates the lateral position of the vehicle corresponding to the lane change trajectory characteristic curve; y i Indicates the lateral position of the vehicle corresponding to the lane change trajectory; x op Indicates the longitudinal position of the vehicle corresponding to the lane change trajectory characteristic curve; x i represents the longitudinal position of the vehicle corresponding to the lane change trajectory; θ op Indicates the steering wheel angle corresponding to the steering wheel angle characteristic curve; θ i It indicates the steering wheel angle corresponding to the lane changing trajectory; t indicates the lane changing time.

[0034] Furthermore, the driver characteristics evaluation method in step 5) specifically includes:

[0035] The driver characteristic evaluation index is expressed as:

[0036]

[0037] Where E represents the driver's lane-changing characteristics; k1, k2, k3, k4 represent weight coefficients.

[0038] Beneficial effects of the present invention:

[0039] The method of the present invention analyzes the driver's daily lane changing data, extracts driving characteristics, considers the driver's lane changing trajectory and steering wheel angle, and proposes a driver's lane changing trajectory characteristic curve and a steering wheel angle characteristic curve to describe the driver's driving characteristics.

[0040] The present invention designs a driver characteristic quantitative evaluation index based on the driver's lane change trajectory characteristic curve and the steering wheel angle characteristic curve, and performs quantitative evaluation on the driving characteristics of different types of drivers, which is helpful for the personalized control design of the intelligent driving assistance system and further improves the active safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure is a flow chart of the evaluation method of the present invention. DETAILED DESCRIPTION

[0042] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0043] Reference Figure 1 As shown, a quantitative evaluation method of driver characteristics of the present invention comprises the following steps:

[0044] 1) Collect lane change trajectory data and steering wheel angle data of the driver during driving;

[0045] Specifically, the lane change trajectory data and steering wheel angle data of the driver during driving are collected through visual sensors and steering wheel angle sensors.

[0046] 2) Classify the lane change trajectory data and steering wheel angle data according to the working conditions based on the vehicle speed, the distance between the vehicle and the front vehicle, and the speed of the front vehicle;

[0047] The classification method in step 2) specifically includes:

[0048] 21) When the speed of the vehicle and the vehicle ahead are within the range of 0-40 km / h, it is a low-speed condition; when the speed of the vehicle and the vehicle ahead are within the range of 40-80 km / h, it is a medium-speed condition; when the speed of the vehicle and the vehicle ahead are within the range of 80-120 km / h, it is a high-speed condition; when the vehicle changes lanes, the distance between the vehicle and the vehicle ahead is within the range of 0-50m, it is an emergency lane change condition; when the vehicle changes lanes, the distance between the vehicle and the vehicle ahead is greater than 50m, it is a normal lane change condition;

[0049] There are several working conditions as follows:

[0050] Condition 1: The vehicle is at a low speed, the vehicle ahead is at a low speed, and the vehicle makes an emergency lane change;

[0051] Condition 2: The vehicle is at a medium speed, the vehicle ahead is at a medium speed, and the vehicle makes an emergency lane change;

[0052] Condition 3: The vehicle is in high-speed condition, the vehicle ahead is in high-speed condition, and the vehicle makes an emergency lane change;

[0053] Condition 4: The vehicle is at a low speed, the vehicle ahead is at a low speed, and the vehicle is changing lanes normally;

[0054] Condition 5: The vehicle is at a medium speed, the vehicle ahead is at a medium speed, and the vehicle is changing lanes normally;

[0055] Condition 6: The vehicle is in high-speed condition, the preceding vehicle is in high-speed condition, and the vehicle is changing lanes normally;

[0056] Condition 7: The vehicle is at a medium speed, the vehicle ahead is at a low speed, and the vehicle makes an emergency lane change;

[0057] Condition 8: The vehicle is at a medium speed, the vehicle ahead is at a low speed, and the vehicle is changing lanes normally;

[0058] Condition 9: The vehicle is at high speed, the vehicle ahead is at low speed, and the vehicle changes lanes urgently;

[0059] Condition 10: The vehicle is at high speed, the vehicle ahead is at low speed, and the vehicle is changing lanes normally;

[0060] Condition 11: The vehicle is in high-speed condition, the vehicle ahead is in medium-speed condition, and the vehicle changes lanes urgently;

[0061] Condition 12: The vehicle is operating at high speed, the vehicle ahead is operating at medium speed, and the vehicle is changing lanes normally.

[0062] 3) Processing the lane change trajectory data and steering wheel angle data of the driver under fixed working conditions, filtering the lane change trajectory data and steering wheel angle data, filtering out external noise interference, and removing the data group obtained by the driver's operating errors, to obtain the lane change trajectory cluster and steering wheel angle cluster under the working condition;

[0063] 4) processing the lane change trajectory cluster and the steering wheel angle cluster respectively to obtain a lane change trajectory characteristic curve and a steering wheel angle characteristic curve;

[0064] The method for obtaining the lane change trajectory characteristic curve and the steering wheel angle characteristic curve in step 4) specifically includes:

[0065] Based on the obtained lane change trajectory cluster and steering wheel angle cluster, the driver's lane change trajectory characteristic curve and steering wheel angle characteristic curve are expressed as:

[0066]

[0067] Where m represents the number of sampling points; n represents the number of lane-changing trajectories in the lane-changing trajectory cluster; y op Indicates the lateral position of the vehicle corresponding to the lane change trajectory characteristic curve; yi Indicates the lateral position of the vehicle corresponding to the lane change trajectory; x op Indicates the longitudinal position of the vehicle corresponding to the lane change trajectory characteristic curve; x i represents the longitudinal position of the vehicle corresponding to the lane change trajectory; θ op Indicates the steering wheel angle corresponding to the steering wheel angle characteristic curve; θ i It indicates the steering wheel angle corresponding to the lane changing trajectory; t indicates the lane changing time.

[0068] 5) quantitatively evaluating the driver's characteristics according to the lane change trajectory characteristic curve and the steering wheel angle characteristic curve;

[0069] The driver characteristics assessment method specifically includes:

[0070] The driver characteristic evaluation index is expressed as:

[0071]

[0072] Where E represents the driver's lane-changing characteristics; k1, k2, k3, k4 represent weight coefficients.

[0073] The present invention has many specific application paths. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principle of the present invention. These improvements should also be regarded as the protection scope of the present invention.

Claims

1. A method for quantitatively evaluating driver characteristics, characterized in that: Here are the steps: 1) Collect lane change trajectory data and steering wheel angle data of the driver during driving; 2) Classify the lane change trajectory data and steering wheel angle data according to the working conditions based on the vehicle speed, the distance between the vehicle and the front vehicle, and the speed of the front vehicle; 3) Processing the lane change trajectory data and steering wheel angle data of the driver under a fixed working condition to obtain a lane change trajectory cluster and a steering wheel angle cluster under the working condition; 4) processing the lane change trajectory cluster and the steering wheel angle cluster respectively to obtain a lane change trajectory characteristic curve and a steering wheel angle characteristic curve; 5) quantitatively evaluating the driver's characteristics according to the lane change trajectory characteristic curve and the steering wheel angle characteristic curve; The method for obtaining the lane change trajectory characteristic curve and the steering wheel angle characteristic curve in step 4) specifically includes: Based on the obtained lane change trajectory cluster and steering wheel angle cluster, the driver's lane change trajectory characteristic curve and steering wheel angle characteristic curve are expressed as: Where m represents the number of sampling points; n represents the number of lane-changing trajectories in the lane-changing trajectory cluster; y op Indicates the lateral position of the vehicle corresponding to the lane change trajectory characteristic curve; y i Indicates the lateral position of the vehicle corresponding to the lane change trajectory; x op Indicates the longitudinal position of the vehicle corresponding to the lane change trajectory characteristic curve; x i Indicates the longitudinal position of the vehicle corresponding to the lane change trajectory; θ op Indicates the steering wheel angle corresponding to the steering wheel angle characteristic curve; θ i It indicates the steering wheel angle corresponding to the lane changing trajectory; t indicates the lane changing time.

2. The method for quantitatively evaluating driver characteristics according to claim 1, characterized in that: The step 1) specifically includes: collecting lane change trajectory data and steering wheel angle data of the driver during driving through a visual sensor and a steering wheel angle sensor.

3. The method for quantitatively evaluating driver characteristics according to claim 1, characterized in that: The classification method in step 2) specifically includes: When the speed of the vehicle and the vehicle ahead are within the range of 0-40km / h, it is a low-speed condition; when the speed of the vehicle and the vehicle ahead are within the range of 40-80km / h, it is a medium-speed condition; when the speed of the vehicle and the vehicle ahead are within the range of 80-120km / h, it is a high-speed condition; when the vehicle changes lanes, the distance between the vehicle and the vehicle ahead is within the range of 0-50m, it is an emergency lane change condition; when the vehicle changes lanes, the distance between the vehicle and the vehicle ahead is greater than 50m, it is a normal lane change condition; There are several working conditions as follows: Condition 1: The vehicle is at a low speed, the vehicle ahead is at a low speed, and the vehicle makes an emergency lane change; Condition 2: The vehicle is at a medium speed, the vehicle ahead is at a medium speed, and the vehicle makes an emergency lane change; Condition 3: The vehicle is in high-speed condition, the vehicle ahead is in high-speed condition, and the vehicle makes an emergency lane change; Condition 4: The vehicle is at a low speed, the vehicle ahead is at a low speed, and the vehicle is changing lanes normally; Condition 5: The vehicle is at a medium speed, the vehicle ahead is at a medium speed, and the vehicle is changing lanes normally; Condition 6: The vehicle is in high-speed condition, the preceding vehicle is in high-speed condition, and the vehicle is changing lanes normally; Condition 7: The vehicle is at a medium speed, the vehicle ahead is at a low speed, and the vehicle makes an emergency lane change; Condition 8: The vehicle is at a medium speed, the vehicle ahead is at a low speed, and the vehicle is changing lanes normally; Condition 9: The vehicle is at high speed, the vehicle ahead is at low speed, and the vehicle changes lanes urgently; Condition 10: The vehicle is at high speed, the vehicle ahead is at low speed, and the vehicle is changing lanes normally; Condition 11: The vehicle is in high-speed condition, the vehicle ahead is in medium-speed condition, and the vehicle changes lanes urgently; Condition 12: The vehicle is operating at high speed, the vehicle ahead is operating at medium speed, and the vehicle is changing lanes normally.

4. The method for quantitatively evaluating driver characteristics according to claim 1, characterized in that: In the step 3), the lane change trajectory data and the steering wheel angle data are filtered to filter out external noise interference and remove the data group obtained by the driver's operating error.

5. The method for quantitatively evaluating driver characteristics according to claim 1, characterized in that: The method for evaluating the driver's characteristics in step 5) specifically includes: The driver characteristic evaluation index is expressed as: Where E represents the driver's lane-changing characteristics; k1, k2, k3, k4 represent weight coefficients.

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