A vehicle-road cooperation lane changing risk assessment method based on coordinate transformation

Abstract

The application discloses a kind of vehicle-road cooperation lane changing risk assessment methods based on coordinate transformation, including analyzing vehicle-road cooperation platform vehicle platoon information parameters, obtain the driving data of related vehicle;Determine the two-dimensional coordinate system with lane changing vehicle as center coordinate;Transform geographic coordinate system into space coordinate system, and map into two-dimensional coordinate system, complete the transformation from geographic coordinate system to real-time plane coordinate system;Establish risk assessment model;The driving data of related vehicle in the process of lane changing and expected value are input into risk assessment model, and operation analysis and simulation are carried out;Influence of vehicle driving data on lane changing risk is explored, and the greatest factor of risk influence on vehicle in the process of lane changing is obtained.The application calculates the collision risk brought by lane changing of vehicle in driving by means of coordinate transformation algorithm and lane changing risk assessment model, quantifies risk index, guarantees the safety and effectiveness of vehicle in the process of lane changing, and provides new ideas for lane changing logic judgment and lane changing path planning.

Description

Technical Field

[0001] This invention belongs to the field of risk assessment for vehicle-road cooperative systems, and specifically relates to a risk assessment method for vehicle-road cooperative lane changing based on coordinate transformation. Background Technology

[0002] With the development of vehicle-road cooperative technology and the breakthroughs and popularization of intelligent connected vehicle technology, the collision risk caused by lane-changing behavior during driving has gradually become a research trend. However, due to the relatively complex and diverse factors such as vehicle driving environment, road type, and road conditions, there is no unified method to assess lane-changing risks. In previous studies, although the assessment methods varied, they all used data that the lane-changing vehicle itself could collect as assessment parameters to build assessment models, which could not accurately identify the driving intentions of surrounding vehicles, and the conclusions were mostly qualitative. At the same time, there is the influence of human factors. The driver's subjective and random driving behavior can cause significant differences in some assessment parameters, leading to invalid parameters and ultimately causing a large deviation in the assessment results.

[0003] Existing research has not yet yielded results on using vehicle-road cooperative platforms to group vehicle information, employing unified methods to assess lane-changing risks, and analyzing the influencing factors of these risks. Therefore, a new technical solution is needed to achieve this. Summary of the Invention

[0004] Purpose of the invention: To overcome the shortcomings of existing vehicle lane-changing risk assessment models, a vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation is proposed. By transforming and mapping the vehicle's geographic coordinate system, a unified two-dimensional coordinate transformation is achieved, a risk assessment model is established, and factors affecting the risk of vehicles during lane changing are analyzed to ensure the safety and effectiveness of vehicles during lane changing. This provides a new approach for lane-changing logic judgment and lane-changing path planning.

[0005] Technical Solution: To achieve the above objectives, this invention provides a vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation, comprising the following steps:

[0006] S1: Lane-changing vehicles receive and parse vehicle-road cooperative vehicle grouping information to obtain related vehicle driving data;

[0007] S2: Determine a two-dimensional coordinate system centered on the lane-changing vehicle;

[0008] S3: Convert the geographic coordinates of the related vehicles obtained in step S1 into a spatial coordinate system and map them into the two-dimensional coordinate system in step S2 to complete the transformation from the geographic coordinate system of the related vehicles to the two-dimensional coordinate system.

[0009] S4: Based on the two-dimensional coordinate system established in step S3, construct a vehicle lane-changing risk assessment model;

[0010] S5: Input the related vehicle driving parameters obtained in step S1 into the risk assessment model established in step S4, and perform calculation and simulation.

[0011] S6: Change the driving data of any one vehicle while keeping other parameters unchanged, and perform further calculations and simulations to analyze the impact of a single parameter on lane-changing risk;

[0012] S7: By comparing the simulation results, we can identify the factors that have the greatest impact on lane change risk.

[0013] Furthermore, in step S1, the vehicle grouping information is a real-time driving dataset of a group of vehicles, denoted by D, and the vehicle information is denoted by C, then D = [C1, C2, C3, ..., C2]. n ].

[0014] Vehicle Information C i ={p i v i a i r i , Δp b θ i ...}, where p i =(L i B i H i ) represents the real-time geographic coordinates of vehicle i (L i For longitude, B i H represents latitude. i (for elevation), v i Let a be the speed of vehicle i. i Let r be the acceleration of vehicle i. i Let Δp be the lane where vehicle i is located. b =(ΔL) b ΔB b ΔH b ) represents the position correction information of the positioning reference station, θ i Let be the steering angle of vehicle i.

[0015] Furthermore, in step S2, a two-dimensional coordinate system is established with the lane-changing vehicle P0 as the center point, with due north as the positive Y-axis and due east as the positive X-axis, which is consistent with the direction of the Earth's spatial coordinate system, so as to facilitate subsequent translation and transformation of the geographic coordinate system.

[0016] Furthermore, in step S3, the vehicle geographic coordinate point information parsed in step S1 is used to reduce positioning errors using a positioning reference station, and then converted into a two-dimensional coordinate system, completing the mapping of the vehicle location to the two-dimensional coordinate system. The specific transformation process is as follows:

[0017] A1: Based on the standard latitude and longitude error of the positioning reference station, the vehicle's latitude and longitude are uniformly corrected to reduce error interference and obtain the actual coordinates p. ′ i(L′ i B′ i H′ i ).

[0018]

[0019] Because the relative distance between the lane-changing vehicles and the trains in the formation is relatively short at the same time, and the elevation information of their respective road segments does not change significantly, the impact of elevation information on lane-changing risk is ignored in the subsequent coordinate transformation process, and only L′ is used. i B′ i , and perform calculations.

[0020] A2: Based on the corrected vehicle positioning latitude and longitude information p′ i Calculate the distance l between each vehicle in the train group and the lane-changing vehicle p′0 (L′0, B′0, H′0). i0 .

[0021]

[0022] Among them, l i0 Let R be the distance between vehicle i and the vehicle changing lanes, R be the Earth's radius, and PI be pi.

[0023] A3: Translate the coordinates of the Earth's center point to the center of the lane-changing vehicle, keeping the X and Y axes unchanged and ignoring the Z axis. Project the coordinates of the related vehicles into a two-dimensional coordinate system to obtain the transformed coordinates A of the related vehicles. i (A ix A iy ).

[0024]

[0025] Furthermore, step S4 uses the two-dimensional coordinate system established in step S3 to construct a vehicle lane-changing risk assessment model RI, where RI = (R l R r ), R l R r R represents the risk indicators for changing lanes to the left and right, respectively. l R r =Max(RI) x RI y ), where RIx represents the lateral (vehicle spacing in the X-axis direction during lane changing) lane-changing risk, RI y This assesses lane-changing risk in the longitudinal direction (vehicle spacing along the Y-axis during lane changes). The specific model building steps are as follows:

[0026] B1: Calculate the coordinate position of the vehicle after ΔT time according to the instantaneous driving parameters of the vehicle. ΔT is the expected time for lane change. Since ΔT is relatively short, the uniform acceleration motion formula is used The calculated coordinate after movement is A' i (A' ix , A' iy ).

[0027]

[0028] B2: Calculate the lateral distance between vehicle i and the lane-changing vehicle respectively

[0029] B3: Establish RI x Lateral lane-changing risk model, the expression is:

[0030]

[0031] Among them, A' 0x is the X-axis coordinate obtained by the lane-changing vehicle after ΔT time, A' ix is the X-axis coordinate of the related vehicle i, |A' 0x -A' ix | is the lateral distance between the two vehicles, S x is the expected minimum lateral lane-changing distance.

[0032] B4: Similarly, establish RI y Lateral lane-changing risk model, the expression is:

[0033]

[0034] Among them, A' 0y is the Y-axis coordinate obtained by the lane-changing vehicle after ΔT time, A' iy is the Y-axis coordinate of the related vehicle i, |A' 0y -A' iy | is the longitudinal distance between the two vehicles, S y is the expected minimum longitudinal lane-changing distance.

[0035] When the lane-changing vehicle changes lanes to the left, R l = Max(RI x , RI y ), when R l ≥ RI0, RI0 is the critical risk value, and the collision during lane change will occur 100%. When R l < RI0, lane change can be made, and the collision probability caused by lane change is relatively low.

[0036] S xand S y These are all preset values, i.e., safe lane-changing distances.

[0037] B5: Using the vehicle grouping data parsed in step S1, group related vehicles into lanes and calculate the risk index for changing lanes to the left and right according to the established risk analysis model.

[0038] Furthermore, in step S5, the resolved driving parameters of the related vehicles are input into the risk assessment model established in step S4, and calculations and simulations are performed to determine the risks brought about by vehicles changing lanes under various complex conditions, and further determine the most critical influencing factors.

[0039] To quantify the risk level of vehicles during lane changes, this invention introduces a lane-changing risk index (RI) and constructs a vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation. This method, in conjunction with a vehicle-road cooperative platform, acquires and analyzes vehicle grouping information from the platform, corrects and calculates the vehicle's geographic coordinates, and maps these coordinates to a two-dimensional coordinate system, completing the coordinate system transformation. Simultaneously, a risk assessment model is established, using grouped vehicle information as input parameters, and simulation experiments are conducted to identify factors that significantly impact lane-changing risk, thereby ensuring vehicle safety during operation.

[0040] Beneficial effects: Compared with existing technologies, this invention combines precise data from the vehicle-road cooperative platform with coordinate transformation to establish a unified two-dimensional coordinate system and construct a risk assessment model for simulation. The simulation object is not only the lane-changing vehicles and the vehicles in front and behind, but also the vehicles in the entire train. The results are more accurate and reliable, and it can judge the lane-changing risks in the left and right lanes, as well as in the longitudinal and lateral directions. It is applicable to various road environments, ensures the safety of lane changing, and improves lane-changing efficiency. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention;

[0042] Figure 2 This is a diagram illustrating the vehicle grouping data receiving process of the vehicle-road cooperative platform of the present invention.

[0043] Figure 3 This is a schematic diagram of vehicle formation information for the present invention;

[0044] Figure 4 This is a schematic diagram of the vehicle lane-changing process according to the present invention;

[0045] Figure 5 This is a schematic diagram illustrating the end of a lane change for the vehicle according to the present invention.

[0046] Figure 6 This is a diagram showing the vehicle coordinate mapping relationship of the present invention. Detailed Implementation

[0047] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0048] This invention provides a vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation, such as... Figure 1 As shown, it includes the following steps:

[0049] S1: Lane-changing vehicles receive and parse vehicle-road cooperative vehicle grouping information to obtain related vehicle driving data;

[0050] S2: Determine a two-dimensional coordinate system centered on the lane-changing vehicle;

[0051] S3: Convert the geographic coordinates of the related vehicles obtained in step S1 into a spatial coordinate system and map them into the two-dimensional coordinate system in step S2 to complete the transformation from the geographic coordinate system of the related vehicles to the two-dimensional coordinate system.

[0052] S4: Based on the two-dimensional coordinate system established in step S3, construct a vehicle lane-changing risk assessment model;

[0053] S5: Input the related vehicle driving parameters obtained in step S1 into the risk assessment model established in step S4, and perform calculation and simulation.

[0054] S6: Change the driving data of any one vehicle while keeping other parameters unchanged, and perform further calculations and simulations to analyze the impact of a single parameter on lane-changing risk;

[0055] S7: By comparing the simulation results, we can identify the factors that have the greatest impact on lane change risk.

[0056] like Figure 2 As shown, in step S1, the vehicle grouping information is a real-time driving dataset of a group of vehicles, denoted by D, and the vehicle information is denoted by C. Then D = [C1, C2, C3, ..., C2]. n ].

[0057] Vehicle Information C i ={p i v i a i r i , Δp b θ i ...}, where p i =(L i B i H i ) represents the real-time geographic coordinates of vehicle i (Li For longitude, B i H represents latitude. i (for elevation), v i Let a be the speed of vehicle i. i Let r be the acceleration of vehicle i. i Let Δp be the lane where vehicle i is located. b =(ΔL) b ΔB b ΔH b ) represents the position correction information of the positioning reference station, θ i Let be the steering angle of vehicle i.

[0058] Step S2 establishes a two-dimensional coordinate system with lane-changing vehicle P0 as the center point, with due north as the positive Y-axis and due east as the positive X-axis, consistent with the direction of the Earth's spatial coordinate system, which facilitates subsequent translation and transformation of the geographic coordinate system.

[0059] In step S3, the vehicle geographic coordinate point information parsed in step S1 is used to reduce positioning errors using a positioning reference station, and then converted into a two-dimensional coordinate system, completing the mapping of the vehicle location to the two-dimensional coordinate system. The specific transformation process is as follows:

[0060] A1: Based on the standard latitude and longitude error of the positioning reference station, the vehicle's latitude and longitude are uniformly corrected to reduce error interference and obtain the actual coordinates p′. i (L′ i B′ i H′ i ).

[0061]

[0062] Because the relative distance between the lane-changing vehicles and the trains in the formation is relatively short at the same time, and the elevation information of their respective road segments does not change significantly, the impact of elevation information on lane-changing risk is ignored in the subsequent coordinate transformation process, and only L′ is used. i B′ i , and perform calculations.

[0063] A2: Based on the corrected vehicle positioning latitude and longitude information p′ i Calculate the distance l between each vehicle in the train group and the lane-changing vehicle p′0 (L′0, B′0, H′0). i0 .

[0064]

[0065] Among them, l i0 Let R be the distance between vehicle i and the vehicle changing lanes, R be the Earth's radius, and PI be pi.

[0066] A3: Translate the coordinates of the Earth's center point to the center of the lane-changing vehicle, keeping the X and Y axes unchanged and ignoring the Z axis. Project the coordinates of the related vehicles into a two-dimensional coordinate system to obtain the transformed coordinates A of the related vehicles. i (A ix A iy ).

[0067]

[0068] Step S4 uses the two-dimensional coordinate system established in step S3 to construct a vehicle lane-changing risk assessment model RI, where RI = (R l R r ), R l R r R represents the risk indicators for changing lanes to the left and right, respectively. l R r =Max(RI) x RI y ), where RI x To mitigate the risk of lateral lane changing (vehicle spacing in the X-axis direction during lane changes), RI y This assesses lane-changing risk in the longitudinal direction (vehicle spacing along the Y-axis during lane changes). The specific model building steps are as follows:

[0069] B1: Calculate the vehicle's coordinates after a time interval ΔT based on the vehicle's instantaneous driving parameters. ΔT represents the desired lane-changing time. Since ΔT is relatively short, the uniform acceleration motion formula is used. The calculated coordinates after the motion are A′ i (A′ ix A′ iy ).

[0070]

[0071] B2: Calculate the lateral distance between vehicle i and the lane-changing vehicle respectively.

[0072] B3: Establish RI x The lateral lane-changing risk model is expressed as follows:

[0073]

[0074] Among them, A′ 0x Let A′ be the X-axis coordinate of the lane-changing vehicle after a time interval ΔT. ix Let |A′| be the X-axis coordinate of the related vehicle i. 0x -A′ ix | represents the lateral distance between the two vehicles, S x This represents the desired minimum lateral lane change distance.

[0075] B4: Similarly, establish RI y Lane-changing risk model in the lateral direction, the expression is:

[0076]

[0077] Where, A' 0y is the Y-axis coordinate obtained by the lane-changing vehicle after ΔT time, A' iy is the Y-axis coordinate of the relevant vehicle i, |A' 0y - A' iy | is the longitudinal distance between the two vehicles, and S y is the expected minimum longitudinal lane-changing distance.

[0078] When the lane-changing vehicle changes lanes to the left, R l = Max(RI x , RI y ), when R l ≥ RI0, RI0 is the critical risk value, and the collision during lane-changing will occur 100%. When R l < RI0, lane-changing can be performed, and the collision probability caused by lane-changing is relatively low.

[0079] B5: Through the vehicle formation data parsed in step S1, group the relevant vehicles by lanes, and calculate the risk indices for lane-changing to the left and to the right respectively according to the established risk analysis model.

[0080] In step S5, input the driving parameters of the parsed relevant vehicles into the risk assessment model established in step S4, and perform calculation and simulation to obtain the risks brought by lane-changing of the vehicle under various complex conditions, and further obtain the most critical influencing factors.

[0081] This embodiment also provides a vehicle-road collaborative lane-changing risk assessment system based on coordinate transformation. The system includes a network interface, a memory, and a processor; wherein, the network interface is used to receive and send signals during the process of receiving and sending information with other external network elements; the memory is used to store computer program instructions that can run on the processor; the processor is used to execute the steps of the above consensus method when running the computer program instructions.

[0082] This embodiment also provides a computer storage medium storing a computer program that, when executed by a processor, can implement the methods described above. The computer-readable medium can be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media include non-volatile memory circuitry (e.g., flash memory circuitry, erasable programmable read-only memory circuitry, or masked read-only memory circuitry), volatile memory circuitry (e.g., static random access memory circuitry or dynamic random access memory circuitry), magnetic storage media (e.g., analog or digital magnetic tape or hard disk drive), and optical storage media (e.g., CD, DVD, or Blu-ray disc). The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also include or depend on stored data. The computer program may include a basic input / output system (BIOS) for interacting with the hardware of a dedicated computer, device drivers for interacting with specific devices of the dedicated computer, one or more operating systems, user applications, background services, background applications, etc.

[0083] Based on the above, in order to verify the effectiveness of the method of the present invention, this embodiment applies the method of the present invention in an example, as follows:

[0084] like Figure 3 As shown in the figure, the vehicle grouping information in this embodiment includes vehicles C0 to C3, and their specific locations are shown in the figure; the specific implementation process is as follows:

[0085] 1) Lane-changing vehicle C0 receives and parses vehicle grouping information for vehicle-road cooperative systems, and obtains driving data of related vehicles (C1 to C3);

[0086] 2) Determine a two-dimensional coordinate system centered on the lane-changing vehicle C0;

[0087] 3)Reference Figure 6 The geographic coordinates of the related vehicles obtained in step 1 are converted into spatial coordinates and mapped to the two-dimensional coordinate system in step 2, thus completing the transformation from the geographic coordinate system of the related vehicles to the two-dimensional coordinate system.

[0088] 4) Based on the two-dimensional coordinate system established in step 3, construct a vehicle lane-changing risk assessment model;

[0089] 5) Input the related vehicle driving parameters obtained in step 1 into the risk assessment model established in step 4, and perform calculation and simulation.

[0090] 6) Change the driving data of any one vehicle while keeping other parameters constant, and then perform further calculations and simulations to analyze the impact of a single parameter on lane-changing risk;

[0091] 7) By comparing the simulation results, the factors that have the greatest impact on lane change risk are identified.

[0092] Vehicle C0, which is changing lanes, executes the lane change based on the acquired lane change risk factors, such as... Figure 4 and Figure 5 As shown.

[0093] Factors affecting lane-changing risk include the speed, acceleration, and steering angle of vehicles within the formation, as well as the relative distance between vehicles. Assuming other vehicles remain in constant motion, the faster the speed, the smaller the steering angle, and the greater the acceleration of the vehicle changing lanes, the greater the risk.

Claims

1. A vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation, characterized in that, Includes the following steps: S1: Lane-changing vehicles receive and parse vehicle-road cooperative vehicle grouping information to obtain related vehicle driving data; S2: Determine a two-dimensional coordinate system centered on the lane-changing vehicle; S3: Convert the geographic coordinates of the related vehicles obtained in step S1 into a spatial coordinate system and map them into the two-dimensional coordinate system in step S2 to complete the transformation from the geographic coordinate system of the related vehicles to the two-dimensional coordinate system. S4: Based on the two-dimensional coordinate system established in step S3, construct a vehicle lane-changing risk assessment model; S5: Input the related vehicle driving parameters obtained in step S1 into the risk assessment model established in step S4, and perform calculation and simulation. S6: Change the driving data of any one vehicle while keeping other parameters unchanged, and perform further calculations and simulations to analyze the impact of a single parameter on lane-changing risk; S7: By comparing the simulation results, we can identify the factors that have the greatest impact on lane change risk. The vehicle grouping information in step S1 is a real-time driving dataset of a group of vehicles, using... This indicates that vehicle information is used. Indicate, then ; Vehicle Information ,in, For vehicles Real-time geographic coordinate information, among which, Longitude Latitude For elevation, For vehicles driving speed, For vehicles The acceleration of the vehicle. For vehicles The lane where it is located To provide position correction information for the positioning reference station. For vehicles The steering angle; Step S2 establishes lane-changing vehicles A two-dimensional coordinate system centered at the Earth's spatial coordinate system, with due north as the positive Y-axis and due east as the positive X-axis, is consistent with the orientation of the Earth's spatial coordinate system. In step S3, the vehicle geographic coordinate point information parsed in step S1 is used to reduce positioning errors using a positioning reference station, and then converted into a two-dimensional coordinate system to complete the mapping of vehicle location to the two-dimensional coordinate system. The specific transformation process is as follows: A1: Based on the standard latitude and longitude error of the positioning reference station, the vehicle's latitude and longitude are uniformly corrected to obtain the actual coordinates. ; ； In subsequent coordinate transformations, the impact of elevation information on track-changing risks is ignored; only the following is used. , Perform calculations; A2: Based on the corrected vehicle positioning latitude and longitude information Calculate the distance between the vehicles in the train group and the vehicles changing lanes. Distance between ; ； in, For vehicles Distance from vehicles changing lanes For the Earth's radius, Pi; A3: Translate the Earth's center point coordinates to the center of the lane-changing vehicle, keeping the X and Y axes unchanged and ignoring the Z axis. Project the coordinates of the related vehicles into a two-dimensional coordinate system to obtain the transformed coordinates of the related vehicles. ; ； Step S4 uses the two-dimensional coordinate system established in step S3 to construct a vehicle lane-changing risk assessment model. , , , These represent the risk indicators for changing lanes to the left and right, respectively. ,in, To mitigate the risk of lateral lane changes, For the risk of longitudinal lane changes; Step S4 calculates based on the vehicle's instantaneous driving parameters. The coordinates of the vehicle after the specified time. The expected time for lane changing, due to The time is short, so the formula for uniformly accelerated motion is used. The calculated coordinates after the motion are as follows: ; ； Step S4 Establish The lateral lane-changing risk model is expressed as follows: ； in, For vehicles changing lanes The X-axis coordinates obtained after time. For related vehicles X-axis coordinates The lateral distance between the two vehicles. The desired minimum lateral lane change distance; Step S4 uses the vehicle grouping data parsed in Step S1 to group related vehicles into lanes, and calculates the risk index for changing lanes to the left and right according to the established risk analysis model. When a vehicle changes lanes to the left, ,when hour, At the critical risk value, a collision occurring during a lane change will be 100% guaranteed. When the probability of a collision due to lane changing is low, a lane change is performed.

2. The vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation according to claim 1, characterized in that, Step S4 settles the accounts for the vehicles respectively. Lateral distance between vehicles changing lanes = .

3. The vehicle-road cooperative lane-changing risk assessment method based on coordinate transformation according to claim 2, characterized in that, The step S4 establishes The longitudinal lane change risk model is expressed as follows: ； in, For vehicles changing lanes The Y-axis coordinate obtained after time, For related vehicles Y-axis coordinate, The longitudinal distance between the two vehicles. This represents the desired minimum longitudinal lane change distance.

Images