WO2024124906A1 - Procédé et système de détermination d'état de risque d'opération de suivi de véhicule, et dispositif - Google Patents

Procédé et système de détermination d'état de risque d'opération de suivi de véhicule, et dispositif Download PDF

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WO2024124906A1
WO2024124906A1 PCT/CN2023/108863 CN2023108863W WO2024124906A1 WO 2024124906 A1 WO2024124906 A1 WO 2024124906A1 CN 2023108863 W CN2023108863 W CN 2023108863W WO 2024124906 A1 WO2024124906 A1 WO 2024124906A1
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following
following vehicle
vehicle
vehicle group
group
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PCT/CN2023/108863
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English (en)
Chinese (zh)
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李烨
伍丹
黄合来
李载宁
唐进君
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中南大学
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Publication of WO2024124906A1 publication Critical patent/WO2024124906A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the disclosed embodiments relate to the field of data processing technology, and more particularly to a method, system, and device for determining a risk state of a car following operation.
  • the embodiments of the present disclosure provide a method, system and device for determining the risk status of a vehicle following operation, which at least partially solve the problems of poor adaptability and accuracy in the prior art.
  • an embodiment of the present disclosure provides a method for determining a risk state of a car-following operation, comprising:
  • Step 1 obtaining trajectory data of multiple manually driven vehicles in actual road traffic, wherein the trajectory data includes position, speed and acceleration information of the corresponding vehicles at each time in a preset time period;
  • Step 2 All artificial vehicles are divided into K following vehicle groups, each following vehicle group includes a leading vehicle and a trailing vehicle, and based on the trajectory data of the trailing vehicle of each following vehicle group and the scene discrimination function, the traffic scene of the following vehicle group is judged to be normal following or abnormal following, and based on the trajectory data of the trailing vehicle of each normal following vehicle group and the style discrimination function, the driving style of the corresponding driver is judged to be aggressive non-sensitive, conservative non-sensitive, aggressive sensitive, or conservative sensitive;
  • Step 3 Determine the accident risk of each following car group based on the traffic scenario and the driving style of the driver of each following car group.
  • step 1 specifically includes:
  • trajectory data of multiple manually driven vehicles are extracted from the videos using video image processing technology, and data cleaning operations are performed on the extracted trajectory data to obtain traffic trajectory data, wherein the data cleaning operations include noise removal, interpolation, and rebalancing.
  • the expression of the scene discrimination function is:
  • v(t) represents the speed of the rear vehicle at time t
  • F 3 is the first judgment function value of the traffic scene
  • F' 3 is the second judgment function value of the traffic scene
  • F 3 ⁇ 1 or F' 3 ⁇ 2 it is determined as an abnormal following scene, otherwise, it is determined as a normal following scene, wherein ⁇ 1 and ⁇ 2 are preset traffic scene judgment coefficients.
  • the expression of the style discriminant function is:
  • PRT is the average perception reaction time of the following vehicle driver
  • Ti-1 (n) refers to the moment when the leading vehicle decelerates for the nth time
  • Ti (n) refers to the moment when the following vehicle decelerates for the nth time
  • N is the total number of decelerations of the following vehicle group
  • F4 is the value of the first judgment function of driving style
  • F'4 is the value of the second judgment function of driving style
  • F 4 > ⁇ 1 and F' 4 > ⁇ 2 it is judged as an aggressive non-sensitive type; if F 4 ⁇ 1 and F' 4 > ⁇ 2 , it is judged as a conservative non-sensitive type; if F 4 > ⁇ 1 and F' 4 ⁇ 2 , it is judged as an aggressive sensitive type; if F 4 ⁇ 1 and F' 4 ⁇ 2 , it is judged as a conservative sensitive type, wherein ⁇ 1 and ⁇ 2 are preset driving style judgment coefficients.
  • step 3 specifically includes:
  • Step A1 calculate the initial discriminant function value F 1 of the running state of the following vehicle group according to the following formula:
  • x i-1 (t) and x i (t) are the front center positions of the front and rear vehicles at time t
  • vi -1 (t) and vi (t) are the speeds of the front and rear vehicles at time t
  • Li-1 is the length of the front vehicle
  • Step A2 if the driving style of the driver of the following vehicle in the current following vehicle group is aggressive and non-sensitive, determine whether the initial discriminant function value F1 satisfies F1 ⁇ 1 ; if so, determine that the current following vehicle group is in a high-risk state; if not, directly determine that the current following vehicle group is in a non-high-risk state;
  • the driving style of the driver of the following vehicle in the current following vehicle group is conservative and non-sensitive, then determine whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 2 . If so, then determine that the current following vehicle group is in a high-risk state. If not, then directly determine that the current following vehicle group is in a non-high-risk state.
  • the driving style of the driver of the following vehicle in the current following vehicle group is the aggressive sensitive type, then determine whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 3 . If so, then determine that the current following vehicle group is in a high-risk state. If not, then directly determine that the current following vehicle group is in a non-high-risk state.
  • the driving style of the driver of the following vehicle in the current following vehicle group is conservative and sensitive, it is determined whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 4 . If so, it is determined that the current following vehicle group is in a high-risk state. If not, it is directly determined that the current following vehicle group is in a non-high-risk state.
  • ⁇ 1 , ⁇ 2 , ⁇ 3 and ⁇ 4 are the direct discriminant coefficients of aggressive non-sensitive, conservative non-sensitive, aggressive sensitive and conservative sensitive driving styles respectively;
  • Step A3 calculating the initial discriminant function value F 2 of the deceleration required for the rear vehicle of the following vehicle group to avoid collision with the corresponding front vehicle at time t:
  • vi-1 (t) and vi (t) are the speeds of the leading and trailing vehicles at time t
  • x -1 (t) and x (t) are the center positions of the leading and trailing vehicles at time t
  • Li -1 is the length of the leading vehicle.
  • Step A4 judging whether the initial discriminant function value F2 satisfies F2 ⁇ , if so, judging that the current following vehicle group is in a non-high-risk state, if not, judging that the current following vehicle group is in a high-risk state, wherein ⁇ refers to the maximum deceleration that the following vehicle can reach.
  • an embodiment of the present disclosure provides a vehicle following operation risk state determination system, comprising:
  • An acquisition module used to acquire trajectory data of multiple manually driven vehicles in actual road traffic, wherein the trajectory data includes position, speed and acceleration information of the corresponding vehicles at each moment in a preset time period;
  • a discrimination module is used to divide all artificial vehicles into K following vehicle groups, each following vehicle group includes a leading vehicle and a trailing vehicle, and to judge whether the traffic scene of the following vehicle group is normal following or abnormal following based on the trajectory data of the trailing vehicle of each following vehicle group and the scene discrimination function, and to judge whether the driving style of the corresponding driver belongs to the aggressive non-sensitive type, the conservative non-sensitive type, the aggressive sensitive type, or the conservative sensitive type based on the trajectory data of the trailing vehicle of each normal following vehicle group and the style discrimination function;
  • the determination module is used to determine the accident risk of the following vehicle group according to the traffic scene of each following vehicle group and the driving style of the driver.
  • an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:
  • the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle following operation risk state determination method in the aforementioned first aspect or any implementation of the first aspect.
  • the vehicle following operation risk state determination scheme in the disclosed embodiment includes: step 1, obtaining trajectory data of multiple manually driven vehicles in actual road traffic, wherein the trajectory data includes the position, speed and acceleration information of the corresponding vehicles at each moment in a preset time period; step 2, dividing all manual vehicles into K following vehicle groups, each following vehicle group includes a front vehicle and a rear vehicle, and judging whether the traffic scene of the following vehicle group is normal following or abnormal following based on the trajectory data of the rear vehicle of each following vehicle group and the scene discrimination function, and judging whether the driving style of the corresponding driver belongs to aggressive non-sensitive type, conservative non-sensitive type, aggressive sensitive type or conservative sensitive type based on the trajectory data of the rear vehicle of each normal following vehicle group and the style discrimination function; step 3, judging the accident risk of the following vehicle group according to the traffic scene of each following vehicle group and the driving style of the driver.
  • the beneficial effects of the disclosed embodiments are as follows: (1) Based on the high-precision trajectory data of manually driven vehicles in actual road traffic, the traffic scene of the current following vehicle group is judged as normal following or abnormal following; for the normal following vehicle group, the driving style of the rear vehicle driver is judged to be aggressive non-sensitive, conservative non-sensitive, aggressive sensitive or conservative sensitive. Furthermore, the vehicle following operation status under different traffic scenes and driving style conditions is analyzed, laying the foundation for the classification and refined accident risk assessment of the following vehicle group;
  • the traffic scenes are distinguished and divided according to the distance between the front and rear vehicles and the speed of the rear vehicle during the vehicle following operation;
  • the driving style of the driver is distinguished and divided based on the sum of the absolute values of the rear vehicle speed and acceleration and the average perception reaction time of the rear vehicle driver; the method used is simple in design, easy to calculate, and intuitively reflects the differences between different types of traffic scenes and driving styles;
  • the present invention improves the accuracy of accident risk assessment for a following vehicle group and clarifies a method for determining a high-risk operating state of a following vehicle group. This is of great significance for promoting the further development of future traffic safety and has excellent application prospects.
  • FIG1 is a flow chart of a method for determining a risk state of a vehicle following operation provided by an embodiment of the present disclosure
  • FIG2 is a schematic diagram of a technical route of a method for determining a risk state of a car-following operation provided by an embodiment of the present disclosure
  • FIG3 is a schematic diagram of the structure of a vehicle following operation risk state determination system provided by an embodiment of the present disclosure
  • FIG. 4 is a schematic diagram of an electronic device provided in an embodiment of the present disclosure.
  • the disclosed embodiment provides a method for determining the risk status of a car following operation, which can be applied to the process of analyzing the operation status of a car in a road traffic safety scenario.
  • FIG1 a flow chart of a method for determining a vehicle's car-following operation risk state provided by an embodiment of the present disclosure is shown. As shown in FIG1 , the method mainly includes the following steps:
  • Step 1 obtaining trajectory data of multiple manually driven vehicles in actual road traffic, wherein the trajectory data includes position, speed and acceleration information of the corresponding vehicles at each time in a preset time period;
  • step 1 specifically includes:
  • trajectory data of multiple manually driven vehicles are extracted from the videos using video image processing technology, and data cleaning operations are performed on the extracted trajectory data to obtain traffic trajectory data, wherein the data cleaning operations include noise removal, interpolation, and rebalancing.
  • trajectory data of several manually driven vehicles in actual road traffic can be obtained; wherein the trajectory data includes the position, speed and acceleration information of the corresponding vehicle at each moment in a preset time period.
  • the method for acquiring trajectory data is as follows: collecting videos of actual road traffic through drone aerial photography, and using existing video image processing technology to extract trajectory data of several manually driven vehicles from the video; performing cleaning processes such as denoising, interpolation, and rebalancing on the extracted trajectory data to obtain high-precision microscopic traffic trajectory data, and numbering the trajectory data according to the manually driven vehicles.
  • Step 2 All artificial vehicles are divided into K following vehicle groups, each following vehicle group includes a leading vehicle and a trailing vehicle, and based on the trajectory data of the trailing vehicle of each following vehicle group and the scene discrimination function, the traffic scene of the following vehicle group is judged to be normal following or abnormal following, and based on the trajectory data of the trailing vehicle of each normal following vehicle group and the style discrimination function, the driving style of the corresponding driver is judged to be aggressive non-sensitive, conservative non-sensitive, aggressive sensitive, or conservative sensitive;
  • the expression of the scene discrimination function is:
  • v(t) represents the speed of the rear vehicle at time t
  • F 3 is the first judgment function value of the traffic scene
  • F' 3 is the second judgment function value of the traffic scene
  • F 3 ⁇ 1 or F' 3 ⁇ 2 it is determined as an abnormal following scene, otherwise, it is determined as a normal following scene, wherein ⁇ 1 and ⁇ 2 are preset traffic scene judgment coefficients.
  • PRT is the average perception reaction time of the following vehicle driver
  • Ti-1 (n) refers to the moment when the leading vehicle decelerates for the nth time
  • Ti (n) refers to the moment when the following vehicle decelerates for the nth time
  • N is the total number of decelerations of the following vehicle group
  • F4 is the value of the first judgment function of driving style
  • F'4 is the value of the second judgment function of driving style
  • F 4 > ⁇ 1 and F' 4 > ⁇ 2 it is judged as an aggressive non-sensitive type; if F 4 ⁇ 1 and F' 4 > ⁇ 2 , it is judged as a conservative non-sensitive type; if F 4 > ⁇ 1 and F' 4 ⁇ 2 , it is judged as an aggressive sensitive type; if F 4 ⁇ 1 and F' 4 ⁇ 2 , it is judged as a conservative sensitive type, wherein ⁇ 1 and ⁇ 2 are preset driving style judgment coefficients.
  • all manual vehicles can be divided into K following car groups, each following car group includes a front car and a rear car; based on the trajectory data of the rear car of each following car group, the traffic scene of the following car group is judged as normal following or abnormal following; based on the trajectory data of the rear car of each normal following car group, the driving style of the corresponding driver is judged as aggressive non-sensitive, conservative non-sensitive, aggressive sensitive or conservative sensitive.
  • the specific discriminant function is:
  • v(t) represents the speed of the rear vehicle at time t
  • T is the recording duration of the trajectory data
  • F 3 is the first judgment function value of the traffic scene
  • F' 3 is the second judgment function value of the traffic scene
  • F 3 ⁇ 1 or F' 3 ⁇ 2 it is determined as an abnormal following scene; otherwise, it is determined as a normal following scene; wherein ⁇ 1 and ⁇ 2 are preset traffic scene judgment coefficients.
  • the most intuitive feature of whether it is abnormal following is the speed of the rear vehicle or the small distance between the front and rear vehicles in the following vehicle group. Therefore, the judgment conditions are mainly set to include two situations: (1) F 3 ⁇ 1 is satisfied, but F' 3 ⁇ 2 is not satisfied, which corresponds to abnormal following when the distance between the front and rear vehicles in the following vehicle group is too small; (2) F' 3 ⁇ 2 is satisfied, but F 3 ⁇ 1 is not satisfied, which corresponds to abnormal following when the speed of the rear vehicle in the following vehicle group is too small.
  • the judgment conditions set for the normal following scenario are F 3 > ⁇ 1 and F' 3 > ⁇ 2. Therefore, according to the speed of the rear vehicle and the distance between the front and rear vehicles in the following vehicle group, the present invention can intuitively and accurately simulate and Determine whether the traffic scenario of the following vehicle group is normal following or abnormal following.
  • ⁇ 1 and ⁇ 2 are preset traffic scene judgment coefficients, which can be understood as the corresponding safe vehicle distance and vehicle speed when driving at low speed.
  • the driving style of the corresponding driver belongs to the aggressive non-sensitive type, the conservative non-sensitive type, the aggressive sensitive type or the conservative sensitive type.
  • the specific discriminant function is:
  • PRT is the perception reaction time of the following vehicle driver
  • Ti-1 (n) refers to the moment when the leading vehicle decelerates for the nth time
  • Ti (n) refers to the moment when the following vehicle decelerates for the nth time
  • N is the total number of decelerations of the following vehicle group
  • F3 is the value of the first driving style judgment function
  • F'3 is the value of the second driving style judgment function
  • the most intuitive feature of whether it belongs to the aggressive driving style is that the speed and its fluctuation (acceleration) are large; the most intuitive feature of whether it belongs to the sensitive driving style is that the average perception reaction time of the driver is short, that is, the time difference between the moment when the front vehicle decelerates and the moment when the rear vehicle makes the corresponding deceleration operation is small. Therefore, the intuitive manifestation of the aggressive non-sensitive driving style is fast driving, large speed fluctuations at adjacent moments (that is, large absolute acceleration value) and long driver perception reaction time, so the discrimination condition is set to F 4 > ⁇ 1 and F' 4 > ⁇ 2.
  • the intuitive manifestation of the conservative non-sensitive driving style is non-fast driving, small absolute acceleration value, and long driver perception reaction time, so the discrimination condition is set to F 4 ⁇ 1 and F' 4 > ⁇ 2.
  • the intuitive manifestation of the aggressive sensitive driving style is fast driving, large absolute acceleration value, and short driver perception reaction time, so the discrimination condition is set to F 4 > ⁇ 1 and F' 4 ⁇ 2 .
  • the intuitive manifestation of the conservative sensitive driving style is non-fast driving, small absolute value of acceleration, and short driver perception reaction time, so the judgment condition is set to F 4 ⁇ 1 and F' 4 ⁇ 2. Therefore, the present invention can more intuitively and accurately judge whether the driving style belongs to the aggressive non-sensitive type, conservative non-sensitive type, aggressive sensitive type or conservative sensitive type according to the sum of the absolute values of speed and acceleration and the average perception reaction time of the driver.
  • ⁇ 1 and ⁇ 2 are preset driving style judgment coefficients.
  • ⁇ 1 can be understood as fast driving The sum of the critical value of driving speed and the absolute value of the critical acceleration and deceleration corresponding to the comfortable driving feeling.
  • v 16.68 means that it is in a fast driving state; and
  • 3 is the critical value of acceleration for comfortable driving feeling. The smaller
  • is, the more comfortable the driving feeling is. Therefore, in this embodiment, ⁇ 1 19.68 is taken.
  • ⁇ 2 it can be understood as the average time difference between the moments when the following vehicle makes multiple deceleration operations corresponding to the leading vehicle in the following vehicle group during the observation period.
  • Step 3 Determine the accident risk of each following vehicle group based on the traffic scenario and the driving style of the driver of each following vehicle group.
  • step 3 specifically includes:
  • Step A1 calculate the initial discriminant function value F 1 of the running state of the following vehicle group according to the following formula:
  • x i-1 (t) and x i (t) are the front center positions of the front and rear vehicles at time t
  • vi -1 (t) and vi (t) are the speeds of the front and rear vehicles at time t
  • Li-1 is the length of the front vehicle
  • Step A2 if the driving style of the driver of the following vehicle in the current following vehicle group is aggressive and non-sensitive, determine whether the initial discriminant function value F1 satisfies F1 ⁇ 1 ; if so, determine that the current following vehicle group is in a high-risk state; if not, directly determine that the current following vehicle group is in a non-high-risk state;
  • the driving style of the driver of the following vehicle in the current following vehicle group is conservative and non-sensitive, then determine whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 2 . If so, then determine that the current following vehicle group is in a high-risk state. If not, then directly determine that the current following vehicle group is in a non-high-risk state.
  • the driving style of the driver of the following vehicle in the current following vehicle group is the aggressive sensitive type, then determine whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 3 . If so, then determine that the current following vehicle group is in a high-risk state. If not, then directly determine that the current following vehicle group is in a non-high-risk state.
  • the driving style of the driver of the following vehicle in the current following vehicle group is conservative and sensitive, it is determined whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 4 . If so, it is determined that the current following vehicle group is in a high-risk state. If not, it is directly determined that the current following vehicle group is in a non-high-risk state.
  • ⁇ 1 , ⁇ 2 , ⁇ 3 and ⁇ 4 are radical non-sensitive, conservative non-sensitive, radical sensitive, Direct discrimination coefficients of sensitive and conservative-sensitive driving styles;
  • Step A3 calculating the initial discriminant function value F 2 of the deceleration required for the rear vehicle of the following vehicle group to avoid collision with the corresponding front vehicle at time t:
  • vi-1 (t) and vi (t) are the speeds of the leading and trailing vehicles at time t
  • x -1 (t) and x (t) are the center positions of the leading and trailing vehicles at time t
  • Li -1 is the length of the leading vehicle.
  • Step A4 judging whether the initial discriminant function value F2 satisfies F2 ⁇ , if so, judging that the current following vehicle group is in a non-high-risk state, if not, directly judging that the current following vehicle group is in a high-risk state, wherein ⁇ refers to the maximum deceleration that the following vehicle can reach.
  • the steps for determining the accident risk of the following vehicle group under different traffic scenarios and driving styles can be as follows:
  • Step A1 for the following vehicle group in the abnormal following traffic scenario determined in step 2, calculate the initial discriminant function value F 2 of the deceleration required for the following vehicle to avoid collision with the leading vehicle of the corresponding following vehicle group at time t:
  • vi-1 (t) and vi (t) are the speeds of the leading and trailing vehicles at time t
  • x -1 (t) and x (t) are the center positions of the leading and trailing vehicles at time t
  • Li -1 is the length of the leading vehicle.
  • Step A2 for the following vehicle group in the normal following traffic scenario determined in step 2, the initial discriminant function value F 1 of the running state of the following vehicle group is calculated according to the following formula:
  • x i-1 (t) and x i (t) are the front center positions of the front and rear vehicles at time t
  • vi -1 (t) and vi (t) are the speeds of the front and rear vehicles at time t
  • Li-1 is the length of the front vehicle
  • step 2 it is determined whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 1 . If so, it is determined that the current following vehicle group is in a high-risk state. If not, it is directly determined that the current following vehicle group is in a non-high-risk state.
  • step 2 determines whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 2 . If so, then determine that the current following vehicle group is in a high-risk state. If not, then directly determine that the current following vehicle group is in a non-high-risk state.
  • step 2 determines whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 3 . If so, then determine that the current following vehicle group is in a high-risk state. If not, then directly determine that the current following vehicle group is in a non-high-risk state.
  • step 2 it is determined whether the initial discriminant function value F 1 satisfies F 1 ⁇ ⁇ 4 . If so, it is determined that the current following vehicle group is in a high-risk state. If not, it is directly determined that the current following vehicle group is in a non-high-risk state.
  • the vehicle following operation risk state determination method determines whether the traffic scene of the current following vehicle group is normal following or abnormal following based on the high-precision trajectory data of manually driven vehicles in actual road traffic; for the normal following vehicle group, it determines whether the driving style of the rear vehicle driver belongs to the aggressive non-sensitive type, conservative non-sensitive type, aggressive sensitive type or conservative sensitive type. Furthermore, the vehicle following operation state under different traffic scenes and driving style conditions is analyzed to lay the foundation for the classification and refined accident risk assessment of the following vehicle group;
  • a following car group accident risk assessment model is constructed under corresponding traffic scenarios and driving style conditions. It can accurately calculate the accident risk results under each time and space state, thereby mining the high-risk operation state of the following car group, which helps to solve the limitations of directly using alternative safety assessment indicators in the safety assessment of the following car group operation.
  • the traffic scenes are distinguished and divided based on the distance between the front and rear vehicles and the speed of the rear vehicle during the vehicle following operation; the driving style of the driver is distinguished and divided based on the sum of the absolute values of the rear vehicle speed and acceleration and the average perceived reaction time of the rear vehicle driver; the adopted method is simple in design, easy to calculate, and intuitively reflects the differences in different types of traffic scenes and driving styles, improves the accuracy of accident risk assessment of the following vehicle group, and clarifies the method for determining the high-risk state of the following vehicle group, which is of great significance to promoting the further development of future traffic safety and has excellent application prospects.
  • S1 Use drone aerial photography to collect road traffic videos, use video image processing and data cleaning technology to process vehicle trajectory data, and obtain high-precision manually driven vehicle trajectory data.
  • the trajectory data contains the vehicle position, speed, and acceleration information every second, and the vehicle is labeled (i.e., vehicle ID).
  • step S2 The large amount of high-precision trajectory data of manually driven vehicles obtained in step S1 is used to distinguish and classify two typical traffic scenarios: normal following and abnormal following.
  • the discriminant function F3 is used to calculate the average distance between the leading and trailing vehicles in the first following vehicle group within 1 to 5 seconds:
  • the discriminant function F'3 is used to calculate the average speed of the following vehicle in the first following vehicle group within 1 to 5 seconds:
  • the discriminant function F3 is used to calculate the average distance between the leading and trailing vehicles in the second following vehicle group within 1 to 5 seconds:
  • the discriminant function F'4 is used to calculate the average speed of the second following vehicle group within 1 to 5 seconds:
  • step S3 The manually driven vehicle trajectory data in the normal following scenario obtained in step S2 is used to distinguish and classify the following vehicle driver's driving styles into four categories: aggressive non-sensitive type, conservative non-sensitive type, aggressive sensitive type, and conservative sensitive type.
  • the discriminant function F4 is used to calculate the mean of the sum of the absolute values of the speed and acceleration of the following vehicle in the second following vehicle group within 1 to 5 seconds:
  • the discriminant function F'4 is used to calculate the average perception reaction time of the drivers of the second following vehicle group within 1 to 5 seconds:
  • step S4 Based on the following vehicle group determined and divided in step S2, an accident risk determination is performed.
  • the specific steps are as follows:
  • the embodiment of the present disclosure further provides a vehicle following operation risk state determination system 30, comprising:
  • An acquisition module 301 is used to acquire trajectory data of multiple manually driven vehicles in actual road traffic, wherein the trajectory data includes position, speed and acceleration information of the corresponding vehicle at each time in a preset time period;
  • the discrimination module 302 is used to divide all artificial vehicles into K following vehicle groups, each following vehicle group includes a leading vehicle and a trailing vehicle, and to judge whether the traffic scene of the following vehicle group is normal following or abnormal following based on the trajectory data of the trailing vehicle of each following vehicle group and the scene discrimination function, and to judge whether the driving style of the corresponding driver belongs to the aggressive non-sensitive type, the conservative non-sensitive type, the aggressive sensitive type or the conservative sensitive type based on the trajectory data of the trailing vehicle of each normal following vehicle group and the style discrimination function;
  • the determination module 303 is used to determine the accident risk of each following vehicle group according to the traffic scene of each following vehicle group and the driving style of the driver.
  • the system shown in FIG3 can correspondingly execute the contents in the above method embodiment.
  • the parts not described in detail in this embodiment refer to the contents recorded in the above method embodiment and will not be described again here.
  • the embodiment of the present disclosure further provides an electronic device 40, which includes: at least one processor and a memory in communication with the at least one processor.
  • the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the vehicle following operation risk state judgment in the aforementioned method embodiment. Determine the method.
  • the embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle following operation risk state determination method in the aforementioned method embodiment.
  • the embodiments of the present disclosure also provide a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium.
  • the computer program includes program instructions.
  • the program instructions When the program instructions are executed by a computer, the computer executes the vehicle following operation risk status determination method in the aforementioned method embodiment.
  • the electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.
  • mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc.
  • fixed terminals such as digital TVs, desktop computers, etc.
  • the electronic device shown in FIG4 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present disclosure.
  • the electronic device 40 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 to a random access memory (RAM) 403.
  • ROM read-only memory
  • RAM random access memory
  • various programs and data required for the operation of the electronic device 40 are also stored.
  • the processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404.
  • An input/output (I/O) interface 405 is also connected to the bus 404.
  • the following devices may be connected to the I/O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409.
  • the communication device 409 may allow the electronic device to 40 communicates with other devices wirelessly or by wire to exchange data.
  • the figure shows an electronic device 40 with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices can be implemented or have alternatively.
  • an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart.
  • the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402.
  • the processing device 401 When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.
  • the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above.
  • Computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.
  • a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried.
  • This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above.
  • the computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device.
  • the program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
  • the computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.
  • the computer-readable medium carries one or more programs.
  • the electronic device can execute the relevant steps of the method embodiment.
  • the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can perform the relevant steps of the method embodiment.
  • Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages.
  • the program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).
  • LAN local area network
  • WAN wide area network
  • Internet service provider e.g., AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
  • each box in the flowchart or block diagram may represent a module, a program segment, or a portion of a code, which contains one or more executable instructions for implementing the specified logical functions.
  • the functions marked in the boxes may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession may actually be executed substantially in parallel, and they may sometimes be executed in the opposite order, depending on the functions involved.
  • each box in the block diagram and/or flowchart, and the combination of boxes in the block diagram and/or flowchart may be implemented with a dedicated hardware-based system that performs the specified functions or operations, or may be implemented with a dedicated Implemented using a combination of hardware and computer instructions.
  • the units involved in the embodiments described in the present disclosure may be implemented by software or by hardware.

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Abstract

Procédé et système de détermination d'état de risque d'opération de suivi de véhicule et dispositif, se rapportant au domaine technique du traitement de données. Le procédé consiste plus particulièrement : étape 1, à acquérir des données de trajectoire d'une pluralité de véhicules conduits par un être humain dans un trafic routier réel ; étape 2, à diviser tous les véhicules conduits par un être humain en K groupes de véhicules se suivant, chaque groupe de véhicules se suivant comprenant un véhicule avant et un véhicule arrière ; en fonction des données de trajectoire et d'une fonction de discrimination de scène du véhicule arrière dans chaque groupe de véhicules se suivant, à déterminer que la scène de trafic du groupe de véhicules se suivant est un suivi normal ou un suivi anormal ; et en fonction des données de trajectoire et d'une fonction de discrimination de style du véhicule arrière dans chaque groupe de véhicules de suivi normal, à déterminer que le style de conduite d'un conducteur correspondant est un type non dangereux agressif, un type non dangereux conservateur, un type dangereux agressif ou un type dangereux conservateur ; et étape 3, à effectuer une détermination de risque d'accident sur chaque groupe de véhicules se suivant en fonction de la scène de trafic du groupe de véhicules se suivant et du style de conduite du conducteur. Le procédé de détermination améliore la précision et l'adaptabilité de détermination.
PCT/CN2023/108863 2022-12-13 2023-07-24 Procédé et système de détermination d'état de risque d'opération de suivi de véhicule, et dispositif WO2024124906A1 (fr)

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