CN112277958B - Driver braking behavior analysis method - Google Patents
Driver braking behavior analysis method Download PDFInfo
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- CN112277958B CN112277958B CN202011160603.XA CN202011160603A CN112277958B CN 112277958 B CN112277958 B CN 112277958B CN 202011160603 A CN202011160603 A CN 202011160603A CN 112277958 B CN112277958 B CN 112277958B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0029—Mathematical model of the driver
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Abstract
The invention relates to a method and a system for analyzing the braking behavior of a driver, which respectively obtain the braking parameters of a self-vehicle, relative driving parameters between the self-vehicle and other traffic participants and environmental parameters; taking the condition that a brake pedal of the self-vehicle is changed from a non-braking state to a braking state as a reference, extracting scene key frames and information of the key frames in the first 3 seconds, and dividing the scene where the key frames are located into a front-vehicle jam adding scene, a front-vehicle deceleration scene and a front-vehicle static scene; classifying and storing the key frame information according to the scene division result, and calculating a head-off time distance value THW of the key frame as a longitudinal distance/vehicle speed; and performing regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by using a linear regression algorithm in all combined scenes so as to obtain a driver braking behavior model in each scene. The method divides scenes into three major categories, has mutually independent driver braking behavior models, and can provide more reliable theoretical support for the design of an automatic driving function algorithm.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a driver braking behavior analysis method.
Background
With the powerful development of intelligent networked automobiles, the automatic driving capability of the automobiles needs to be enhanced by combining the native natural driving habits, and the current automatic driving function parameter is formulated to lack the support of the conclusion of the Chinese natural driving habits.
The noun explains:
CIPV (Closest in path vehicle): the nearest vehicle on the route.
Disclosure of Invention
The invention provides a method for analyzing the braking behavior of a driver, aiming at the technical problems in the prior art.
The technical scheme for solving the technical problems is as follows:
in a first aspect, the present invention provides a driver braking behavior analysis method, comprising the steps of:
respectively obtaining a braking parameter of a self-vehicle, a relative driving parameter between the self-vehicle and other traffic participants and an environmental parameter; the self-vehicle braking parameters comprise self-vehicle speed and brake pedal speed;
taking the condition that a brake pedal of the self-vehicle is changed from a non-braking state to a braking state as a reference, extracting a scene key frame and information of the key frame in the first 3 seconds, and dividing the scene where the key frame is located into a front-vehicle jam scene, a front-vehicle deceleration scene and a front-vehicle static scene according to the key frame and the information of the key frame in the first 3 seconds;
classifying and storing the key frame information according to the scene division result, and calculating a head-off time distance value THW of the key frame as a longitudinal distance/vehicle speed;
and performing regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by using a linear regression algorithm in all combined scenes so as to obtain a driver braking behavior model in each scene.
Further, the relative driving parameters between the own vehicle and other traffic participants comprise: longitudinal distance, transverse distance, relative speed and transverse speed change rate of other traffic participants between the self vehicle and other traffic participants; the environmental parameters include CIPV, road type, weather, time.
Further, the dividing the scene where the key frame is located into a preceding vehicle jam scene, a preceding vehicle deceleration scene and a preceding vehicle static scene according to the key frame and the information of the key frame corresponding to the preceding 3 seconds includes:
searching the frame number of the current vehicle CIPV state value changing from 0 to 1 in three seconds from the key frames, and classifying the current vehicle CIPV state value into a congestion scene;
when the CIPV state value of the current vehicle is kept 1 within three seconds and the sum of the relative speed and the speed of the current vehicle is calculated to be reduced within 3 seconds, the current vehicle is classified into a deceleration scene of the previous vehicle;
the CIPV state value of the current vehicle is kept 1 within three seconds, the sum of the calculated relative speed and the speed of the current vehicle is less than or equal to 1m/s within 3 seconds, and the current vehicle is classified as a static scene of the previous vehicle.
Further, before performing the regression calculation, the method further comprises: and further subdividing the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene according to the road type, the time and the weather.
Further, the preceding vehicle blocking scene, the preceding vehicle deceleration scene and the preceding vehicle static scene are further subdivided according to the road type, the time and the weather, and specifically:
distinguishing the ordinary road from the expressway according to the road type under the scene of vehicle congestion and the scene of vehicle deceleration;
the method is characterized in that the method is distinguished from the method in the night and the day according to the time in the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene, and is distinguished from the method in the sunny day and the rainy day according to the weather.
In a second aspect, the present invention provides a driver braking behavior analysis system comprising:
the data acquisition module is used for respectively acquiring the braking parameters of the self-vehicle, the relative driving parameters between the self-vehicle and other traffic participants and the environmental parameters; the self-vehicle braking parameters comprise self-vehicle speed and brake pedal speed;
the scene dividing module is used for extracting a scene key frame and information of the key frame in the first 3 seconds by taking the condition that a brake pedal of the vehicle is changed from a non-braking state to a braking state as a reference, and dividing the scene where the key frame is located into a vehicle ahead jam adding scene, a vehicle ahead deceleration scene and a vehicle ahead static scene according to the key frame and the information of the key frame in the first 3 seconds;
the calculation module is used for classifying and storing the key frame information according to the scene division result and calculating the head-off time distance value THW of the key frame as the longitudinal distance/the vehicle speed;
and the model construction module is used for carrying out regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by utilizing a linear regression algorithm under all combined scenes so as to obtain a driver braking behavior model under each scene.
Further, the relative driving parameters between the own vehicle and other traffic participants comprise: longitudinal distance, transverse distance, relative speed and transverse speed change rate of other traffic participants between the self vehicle and other traffic participants; the environmental parameters include CIPV, road type, weather, time.
Further, before performing the regression calculation, the method further comprises: and further subdividing the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene according to the road type, the time and the weather.
In a third aspect, the present invention provides an electronic device comprising:
the system comprises a plurality of sensors, a monitoring system and a control system, wherein the sensors are used for acquiring braking parameters of a vehicle, relative driving parameters between the vehicle and other traffic participants and environmental parameters;
a memory for storing parameters acquired by the sensor and for storing a computer software program;
and the processor is used for reading and executing the computer software program stored in the memory, and processing the parameters acquired by the sensor, so as to realize the driver braking behavior analysis method of the first aspect of the invention.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium having stored therein a computer software program for implementing a driver braking behavior analysis method according to the first aspect of the present invention.
The invention has the beneficial effects that: the traditional analysis of the braking behavior of the driver does not specifically set a model according to scenes, the scene coverage is low, the indexing is single, the reliability is insufficient, the method divides the scenes into three categories, and the independent braking behavior models of the driver are provided, so that more reliable theoretical support can be provided for the design of an automatic driving function algorithm.
Drawings
Fig. 1 is a flowchart of a method for analyzing braking behavior of a driver according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a driver braking behavior analysis method, including the following steps:
1. during collection, the speed of the vehicle, the state of a brake pedal, the longitudinal distance, the transverse distance and the relative speed between the vehicle and other traffic participants, the transverse speed change rate of the other traffic participants, CIPV, road type, weather and time are recorded at a fixed frame rate.
2. And the scene key frame is searched by changing the brake pedal of the self-vehicle from a non-braking state to a braking state, and the current frame acquisition information is extracted.
3. The scenes are classified into three categories, namely front vehicle deceleration, front vehicle static and front vehicle jamming by utilizing the lateral distance, the speed of the vehicle and the relative speed for calculation and classification.
4. And (4) performing model analysis on each scene through a regression algorithm, and summarizing the brake model of the driver suitable for the scene.
Specifically, the speed of the vehicle and the speed of the brake pedal are analyzed and recorded from the CAN channel during collection, and the longitudinal distance, the transverse distance, the relative speed, the transverse speed change rate of other traffic participants, the CIPV, the road type, the weather and the time are analyzed and recorded from the sensor channel.
And then fusing two kinds of acquired data at a fixed frame rate, and extracting the scene key frame number and the information within 3 seconds before the frame number corresponds to the scene key frame number by taking the condition that the brake pedal of the self-vehicle is changed from a non-braking state to a braking state as a reference.
Searching the frame number of the current vehicle CIPV state value changing from 0 to 1 in three seconds from the key frames, and classifying the current vehicle CIPV state value into a congestion scene; when the CIPV state value of the current vehicle is kept 1 within three seconds and the sum of the relative speed and the speed of the current vehicle is calculated to be reduced within 3 seconds, the current vehicle is classified into a deceleration scene of the previous vehicle; keeping the CIPV state value of the current vehicle unchanged for 1 in three seconds, calculating the sum of the relative speed and the speed of the current vehicle to be less than or equal to 1m/s in 3 seconds, and classifying the current vehicle into a static scene of the previous vehicle;
respectively storing the key frame information according to classification, and calculating a key frame lower head time distance value THW which is the longitudinal distance/the vehicle speed;
distinguishing ordinary roads and expressway roads according to road types in a preceding vehicle jam scene and a preceding vehicle deceleration scene, distinguishing night and day in three scenes according to time, and distinguishing sunny days and rainy days according to weather;
performing regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by using a linear regression algorithm in all combined scenes;
finally obtaining the vehicle ahead traffic jam of the common road in the daytime of sunny days, the vehicle ahead deceleration of the common road in the daytime of sunny days, the vehicle ahead standstill of the common road in the daytime of sunny days, the vehicle ahead congestion of the expressway in the daytime of sunny days, the vehicle deceleration of the expressway in the daytime of sunny days, the vehicle ahead congestion of the common road in the nighttime of sunny days, the vehicle deceleration of the common road in the nighttime of sunny days, the vehicle ahead standstill of the vehicle ahead of the expressway in the nighttime of sunny days, the vehicle congestion of the expressway in the nighttime of sunny days and the vehicle deceleration of the expressway in the nighttime of sunny days, the driver braking behavior model is characterized in that the driver braking behavior model is calculated under 20 small scenes in total by adding a plug to a front vehicle of an ordinary road in the rainy day, decelerating the front vehicle of the ordinary road in the rainy day, keeping the front vehicle stationary in the rainy day, decelerating the front vehicle of an expressway in the rainy day, adding a plug to the front vehicle of the expressway in the rainy day, decelerating the front vehicle of the expressway in the rainy day, adding a plug to the front vehicle of the ordinary road in the rainy night, decelerating the front vehicle of the expressway in the rainy day and the nightnight.
The traditional analysis of the braking behavior of the driver does not specifically set a model according to scenes, the scene coverage is low, the indexing is single, the reliability is insufficient, the method divides the scenes into three categories, and the independent braking behavior models of the driver are provided, so that more reliable theoretical support can be provided for the design of an automatic driving function algorithm.
Example 2
An embodiment of the present invention provides an electronic device, including:
the system comprises a plurality of sensors, a monitoring system and a control system, wherein the sensors are used for acquiring braking parameters of a vehicle, relative driving parameters between the vehicle and other traffic participants and environmental parameters;
a memory for storing parameters acquired by the sensor and for storing a computer software program;
and the processor is used for reading and executing the computer software program stored in the memory, processing the parameters acquired by the sensor, further realizing a driver braking behavior analysis system, and completing the driver braking behavior analysis method disclosed in the embodiment 1 through the system. The system comprises: the data acquisition module is used for respectively acquiring the braking parameters of the self-vehicle, the relative driving parameters between the self-vehicle and other traffic participants and the environmental parameters; the self-vehicle braking parameters comprise self-vehicle speed and brake pedal speed;
the scene dividing module is used for extracting a scene key frame and information of the key frame in the first 3 seconds by taking the condition that a brake pedal of the vehicle is changed from a non-braking state to a braking state as a reference, and dividing the scene where the key frame is located into a vehicle ahead jam adding scene, a vehicle ahead deceleration scene and a vehicle ahead static scene according to the key frame and the information of the key frame in the first 3 seconds;
the calculation module is used for classifying and storing the key frame information according to the scene division result and calculating the head-off time distance value THW of the key frame as the longitudinal distance/the vehicle speed;
and the model construction module is used for carrying out regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by utilizing a linear regression algorithm under all combined scenes so as to obtain a driver braking behavior model under each scene.
Further, the relative driving parameters between the own vehicle and other traffic participants comprise: longitudinal distance, transverse distance, relative speed and transverse speed change rate of other traffic participants between the self vehicle and other traffic participants; the environmental parameters include CIPV, road type, weather, time.
Further, before performing the regression calculation, the method further comprises: and further subdividing the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene according to the road type, the time and the weather.
It should also be noted that the logic instructions in the computer software program can be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A driver braking behavior analysis method, characterized by comprising the steps of:
respectively obtaining a braking parameter of a self-vehicle, a relative driving parameter between the self-vehicle and other traffic participants and an environmental parameter; the self-vehicle braking parameters comprise self-vehicle speed and brake pedal speed;
taking the condition that a brake pedal of the self-vehicle is changed from a non-braking state to a braking state as a reference, extracting a scene key frame and information of the key frame in the first 3 seconds, and dividing the scene where the key frame is located into a front-vehicle jam scene, a front-vehicle deceleration scene and a front-vehicle static scene according to the key frame and the information of the key frame in the first 3 seconds;
classifying and storing the key frame information according to the scene division result, and calculating a head-off time distance value THW of the key frame as a longitudinal distance/vehicle speed;
and performing regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by using a linear regression algorithm in all combined scenes so as to obtain a driver braking behavior model in each scene.
2. The method of claim 1, wherein the relative driving parameters between the host vehicle and the other transportation participants comprise: longitudinal distance, transverse distance, relative speed and transverse speed change rate of other traffic participants between the self vehicle and other traffic participants; the environmental parameters include CIPV, road type, weather, time.
3. The method according to claim 2, wherein the dividing the scene where the key frame is located into a preceding vehicle jam scene, a preceding vehicle deceleration scene, and a preceding vehicle static scene according to the key frame and the information of the key frame corresponding to the preceding 3 seconds includes:
searching the frame number of the current vehicle CIPV state value changing from 0 to 1 in three seconds from the key frames, and classifying the current vehicle CIPV state value into a congestion scene;
when the CIPV state value of the current vehicle is kept 1 within three seconds and the sum of the relative speed and the speed of the current vehicle is calculated to be reduced within 3 seconds, the current vehicle is classified into a deceleration scene of the previous vehicle;
the CIPV state value of the current vehicle is kept 1 within three seconds, the sum of the calculated relative speed and the speed of the current vehicle is less than or equal to 1m/s within 3 seconds, and the current vehicle is classified as a static scene of the previous vehicle.
4. The method of claim 2, further comprising, prior to performing the regression calculation: and further subdividing the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene according to the road type, the time and the weather.
5. The method according to claim 4, wherein the preceding vehicle congestion scene, the preceding vehicle deceleration scene and the preceding vehicle static scene are further subdivided according to road type, time and weather, and specifically:
distinguishing the ordinary road from the expressway according to the road type under the scene of vehicle congestion and the scene of vehicle deceleration;
the method is characterized in that the method is distinguished from the method in the night and the day according to the time in the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene, and is distinguished from the method in the sunny day and the rainy day according to the weather.
6. A driver braking behavior analysis system, comprising:
the data acquisition module is used for respectively acquiring the braking parameters of the self-vehicle, the relative driving parameters between the self-vehicle and other traffic participants and the environmental parameters; the self-vehicle braking parameters comprise self-vehicle speed and brake pedal speed;
the scene dividing module is used for extracting a scene key frame and information of the key frame in the first 3 seconds by taking the condition that a brake pedal of the vehicle is changed from a non-braking state to a braking state as a reference, and dividing the scene where the key frame is located into a vehicle ahead jam adding scene, a vehicle ahead deceleration scene and a vehicle ahead static scene according to the key frame and the information of the key frame in the first 3 seconds;
the calculation module is used for classifying and storing the key frame information according to the scene division result and calculating the head-off time distance value THW of the key frame as the longitudinal distance/the vehicle speed;
and the model construction module is used for carrying out regression calculation on the upper boundary of the data distribution point with THW as a vertical axis and the speed of the vehicle as a horizontal axis by utilizing a linear regression algorithm under all combined scenes so as to obtain a driver braking behavior model under each scene.
7. The system of claim 6, wherein the relative driving parameters between the host vehicle and the other traffic participants comprise: longitudinal distance, transverse distance, relative speed and transverse speed change rate of other traffic participants between the self vehicle and other traffic participants; the environmental parameters include CIPV, road type, weather, time.
8. The system of claim 6, further comprising, prior to performing the regression calculation: and further subdividing the preceding vehicle jam scene, the preceding vehicle deceleration scene and the preceding vehicle static scene according to the road type, the time and the weather.
9. An electronic device, comprising:
the system comprises a plurality of sensors, a monitoring system and a control system, wherein the sensors are used for acquiring braking parameters of a vehicle, relative driving parameters between the vehicle and other traffic participants and environmental parameters;
a memory for storing parameters acquired by the sensor and for storing a computer software program;
a processor for reading and executing the computer software program stored in the memory, and processing the parameters collected by the sensor, thereby implementing a driver braking behavior analysis method as claimed in any one of claims 1 to 5.
10. A non-transitory computer-readable storage medium, characterized in that the storage medium has stored therein a computer software program for implementing a driver braking behavior analysis method according to any one of claims 1 to 5.
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| WO2009013815A1 (en) * | 2007-07-24 | 2009-01-29 | Nissan Motor Co., Ltd. | Drive assistance apparatus for vehicle and vehicle equipped with the apparatus |
| CN106953856B (en) * | 2017-03-16 | 2020-02-21 | 大连理工大学 | Method for analyzing data validity in CAN bus protocol of vehicle-mounted network by combining vehicle driving state |
| US10279793B2 (en) * | 2017-05-11 | 2019-05-07 | Honda Motor Co., Ltd. | Understanding driver awareness through brake behavior analysis |
| JP6638701B2 (en) * | 2017-06-08 | 2020-01-29 | トヨタ自動車株式会社 | Driving awareness estimation device |
| CN107680204B (en) * | 2017-10-09 | 2020-07-03 | 航天科技控股集团股份有限公司 | Vehicle data processing system for analyzing driving behaviors |
| US10252729B1 (en) * | 2017-12-11 | 2019-04-09 | GM Global Technology Operations LLC | Driver alert systems and methods |
| CN109987085B (en) * | 2019-04-15 | 2021-06-11 | 昆山宝创新能源科技有限公司 | Vehicle and control method and device thereof |
| CN111599181B (en) * | 2020-07-22 | 2020-10-27 | 中汽院汽车技术有限公司 | Typical natural driving scene recognition and extraction method for intelligent driving system test |
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