A kind of intelligent network connection environment intersection vehicle conflict early warning method, computer equipment, storage medium

Abstract

The application discloses a kind of intersection vehicle conflict early warning method under intelligent network connection environment, computer equipment, storage medium, specifically includes: obtaining intelligent network connection car and target intersection micro data;Crossing space is modeled by grid network, and grid space occupation matrix is established;According to vehicle driving data, vehicle trajectory is predicted and the transverse static buffer zone and longitudinal dynamic buffer zone of vehicle in each time step are calculated;The transverse static buffer zone and longitudinal dynamic buffer zone of trajectory are merged to obtain comprehensive safety buffer zone, the conflict relationship of comprehensive safety buffer zone and expected grid network occupation state is judged, so as to accurately identify and early warning potential trajectory conflict before vehicle enters intersection.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic control technology, and in particular to an intersection vehicle conflict warning method, computer equipment, and storage medium in an intelligent network environment. Background Art

[0002] With the development of intelligent connected vehicle technology, traffic flow management at intersections has become increasingly prominent. In high-density traffic and complex environments, effectively avoiding conflicts between vehicle trajectories is crucial for improving traffic safety and efficiency. Currently, many studies rely on communication between vehicles and between vehicles and infrastructure to predict and prevent potential intersection conflicts. However, these methods often fail to fully consider the dynamics of intersections, particularly in conflict detection and the provision of safety buffer zones.

[0003] On the one hand, existing intersection modeling techniques are often oversimplified and fail to fully utilize grid networks to finely segment the intersection's interior space, resulting in inaccurate vehicle trajectory prediction and conflict detection. Grid networks, as an effective spatial segmentation method, enable fine-grained modeling of the intersection interior, thereby improving the accuracy and real-time performance of trajectory conflict prediction.

[0004] On the other hand, existing technologies often fail to dynamically adapt the design and application of safety buffers to the real-time driving status and relative positions of vehicles within an intersection. Existing solutions often use static or uniform buffer sizes, ignoring the importance of adjusting the safety buffer based on factors such as vehicle speed, acceleration, and direction. This limits their effectiveness in complex traffic situations.

[0005] Therefore, there is an urgent need for a method that combines grid networks and dynamic safety buffer zones to achieve accurate monitoring of vehicle trajectories within intersections and conflict warnings, thereby significantly improving traffic safety and efficiency. Summary of the Invention

[0006] Purpose of the invention: In order to overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to propose a vehicle conflict warning method, computer equipment, and storage medium for intersections in an intelligent connected environment. The method aims to perform grid network modeling on the intersection, predict vehicle trajectories based on vehicle driving data, and calculate the vehicle's safety buffer zone in each time step, so as to accurately identify and warn of potential trajectory conflicts before the vehicle enters the intersection. The present invention focuses on realizing a reliable trajectory conflict warning mechanism to provide an auxiliary tool for vehicles at the intersection to reduce the risk of traffic accidents.

[0007] Technical solution: To solve the above technical problems, such as Figure 1As shown, the present application proposes a conflict warning method for intersection vehicles in an intelligent connected environment, which comprises the following steps:

[0008] Step 1: Obtain the micro data of all intelligent connected vehicles and the target intersection, the micro data of the intelligent connected vehicles including: vehicle speed, vehicle acceleration, vehicle deceleration, vehicle length, vehicle width, entrance lane of the vehicle entering the intersection and exit lane of the vehicle leaving the intersection; the micro data of the intersection including: the geometric shape of the intersection, the functional use of each entrance lane, and the internal guiding path of the intersection connecting the entrance lane and the exit lane, i.e. the specific route of the intersection for guiding the vehicle to travel safely, and the safety speed limit set for these guiding paths;

[0009] Step 2: According to the micro data of the intelligent connected vehicles obtained in step 1, calculate the expected time of the vehicle entering the intersection and the speed at the time of entering; according to the information of the entrance lane of the vehicle entering the intersection and the exit lane of the vehicle leaving the intersection, determine the guiding path of the vehicle in the intersection; based on the above determined expected time of the vehicle entering the intersection, speed and guiding path in the intersection, calculate the specific position of each time step during the uniform speed travel of the vehicle along the guiding path in the intersection to determine the expected space-time trajectory of the vehicle in the intersection; calculate the trajectory transverse static buffer zone according to the expected space-time trajectory of the vehicle in the intersection; calculate the trajectory longitudinal dynamic buffer zone according to the space-time trajectory of the vehicle in the intersection and the travel speed of the vehicle in the intersection, and combine the transverse static buffer zone and the longitudinal dynamic buffer zone to form a comprehensive safety buffer zone;

[0010] Step 3: According to the geometric shape of the intersection obtained in step 1, divide the intersection into a grid network; establish a grid space occupation matrix based on the grid network; update the grid space occupation matrix according to the expected space-time trajectory of other vehicles; according to the expected space-time trajectory of the target vehicle in the intersection, at each discrete time point, use the method of step 2 to establish a comprehensive safety buffer zone, and mark the grid network cells occupied by the comprehensive safety buffer zone in a temporary grid space occupation matrix; compare the above temporary grid space occupation matrix with the grid space occupation matrix, if a conflict is found, the system will issue a warning signal to prompt the potential collision risk.

[0011] Further, the specific method of step 2 is as follows:

[0012] 2.1 The vehicle travels uniformly accelerated or uniformly decelerated from the initial position to the intersection: when v s ≤v e , the vehicle uniformly accelerates to the intersection, and the travel time is When v s >v e , the vehicle uniformly decelerates to the intersection, and the travel time is

[0013] Where, t a is the time it takes for the vehicle to travel from its initial position to the stop line at the intersection, in seconds; L a is the distance between the vehicle's initial position and the intersection, in meters; v s is the initial speed of the vehicle, in m / s; v e The speed of the vehicle when entering the intersection is equal to the safe speed limit of the vehicle along the guide path in the intersection, in m / s; accel and a decel Represents the acceleration and deceleration of the vehicle respectively. The acceleration value is positive and the deceleration value is negative. The unit is m / s 2 ;

[0014] The time when the vehicle is at the initial position is recorded as t0, and the time when the vehicle reaches the stop line of the intersection is the expected time when the vehicle enters the intersection. The expected time when the vehicle enters the intersection is t enter =t0+t a ;

[0015] 2.2 The guidance path within the intersection is given by the function r(s) = [x(s), y(s)], where s represents the distance traveled along the path from the starting point of the guidance path, and x(s) and y(s) are functions of the spatial coordinates of points on the path. The starting point of the guidance path connects to the entrance road of the intersection, and the end point connects to the exit road of the intersection.

[0016] 2.3 The movement of a vehicle in an intersection is described by the change in the cumulative distance s(t) along the path from the starting point of the path over time. The specific position of the vehicle at time t is represented as r(s(t)). Within any time step Δt, the vehicle moves from r(s(t)) to r(s(t+Δt)) to form a continuous driving trajectory. After entering the intersection, the vehicle will move at a speed v in the intersection. e Maintaining a constant speed, the distance a vehicle needs to travel along the guided path to pass through the intersection is L inter , the time t that a vehicle is expected to take to pass through the intersection expected =L inter / v e ; Vehicle from r(s(t=t enter ))Move to r(s(t=t enter +t expected )) Obtain the expected spatiotemporal trajectory of the vehicle in the intersection;

[0017] The lateral static buffer zone described in 2.4 is calculated by extending each point of the vehicle's trajectory on the guided path by a certain distance in the direction of the normal vector. Specifically, the position r(s(t)) on the trajectory is extracted and expanded outward along both sides of the path to obtain the extension point:

[0018]

[0019]

[0020] Among them, r ± (s(t)) represents the extension points on both sides of the trajectory, n(r(s(t))) is the unit normal vector of the path at position r(s(t)), d is half the width of the static buffer, representing the width of the extension from the trajectory to each side, and w vehicle Indicates the width of the vehicle, w static Indicates the set lateral static safety distance, with a value of 0.5m. Calculates the extension points of multiple points on the trajectory and connects all the extension points on the same side of the trajectory with straight lines to form two parallel boundaries of the trajectory. These two boundaries are finally connected at the start and end points of the trajectory to form a lateral static buffer zone around the trajectory.

[0021] The longitudinal dynamic buffer zone described in 2.5 is calculated by extending the vehicle's trajectory on the guided path in both directions at the starting point and the end point, and extending the trajectory in the opposite direction at the starting point to obtain the rear extension point of the trajectory:

[0022]

[0023] Among them, r rear (s(t)) represents the rear extension point of the trajectory, r(s(t-Δt)) represents the starting point of the trajectory in this time step, and also represents the end point of the trajectory in the previous time step; ξ represents the predetermined safety time interval, which is 0.5s; c is a constant, which is 1m; τ(r(s(t-Δt))) represents the unit tangent vector of the guide path at position r(s(t-Δt));

[0024] Extend the track along the end point to get the front extension point of the track:

[0025]

[0026] Among them, r front (s(t)) represents the front extension point of the trajectory; r(s(t)) is the end point of the trajectory at this time step; τ(r(s(t))) represents the unit tangent vector of the path at position r(s(t));

[0027] Front and rear extension tracks extend the vehicle vertically by half the width on either side A rectangular area with the extended trajectory as the symmetry axis is obtained, forming the longitudinal dynamic buffer zone of the vehicle;

[0028] 2.6 Spatially merge the lateral static buffer zone calculated in step 2.4 and the longitudinal dynamic buffer zone calculated in step 2.5 to construct a complete integrated safety buffer zone for the vehicle trajectory:

[0029] G=G1∪G2

[0030] Among them, G represents the comprehensive safety buffer zone, G1 represents the horizontal static buffer zone, and G2 represents the longitudinal dynamic buffer zone.

[0031] Furthermore, the method of step 3 is as follows:

[0032] The grid network described in 3.1 refers to dividing the internal area of ​​the intersection into regular, continuous cells, each of which is a rectangle, and each grid network cell represents an area of ​​the internal area of ​​the intersection; in the grid network, the length of the grid network cell is m and the width is n; within a circular range with the center of the intersection as the center and a radius of half the width of the intersection, the grid network cells are further subdivided, and the length and width of the subdivided grid network cells are half the length and width of the grid network cells, that is, and Among them, m,n values ​​are between 0.5-5m;

[0033] 3.2 At a discrete time point, the occupancy status of each cell in the grid network is stored in the corresponding position of the matrix in descending order and from left to right. "Occupancy" refers to the partial spatial overlap between the grid network cell and the vehicle space area or the vehicle integrated safety buffer zone. In the grid space occupancy matrix, occupied grid cells are represented by "1" and unoccupied grid network cells are represented by "0". A grid space occupancy matrix is ​​established at each discrete time point to capture and record the occupancy status of the grid network cells at each discrete time point.

[0034] 3.3 Use the method in step 2 to calculate the expected spatiotemporal trajectories of other vehicles in the intersection. At each discrete time point, update the grid space occupancy matrix based on the occupancy of grid network cells by other vehicles.

[0035] 3.4 Use the method in step 2 to calculate the expected spatiotemporal trajectory of the target vehicle in the intersection, and establish a comprehensive safety buffer zone at each discrete time point. Identify the grid network cells occupied by the comprehensive safety buffer zone in a temporary grid space occupancy matrix.

[0036] 3.5 At each discrete time point, the elements of the temporary grid space occupancy matrix in 3.4 are compared one by one with the elements of the corresponding positions in the grid space occupancy matrix of the intersection. If at least one group of elements in the corresponding positions are all "1", it is determined that there is an occupancy conflict. When any occupancy conflict is detected, the system will issue a warning signal to warn participating vehicles of the potential collision risk and remind them to change their current speed.

[0037] In addition, the present invention proposes a computer device for warning of intersection vehicle conflicts in an intelligent connected environment, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the methods when executing the computer program.

[0038] Furthermore, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0039] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0040] The present invention discloses a method, system, computer, and storage medium for warning of vehicle conflicts at intersections in an intelligent network environment. Based on the microscopic data of intelligent networked vehicles and intersections, the intersection is rasterized and modeled, and the space occupancy of each grid in the intersection is described in detail using a grid space occupancy matrix. The future trajectory of the vehicle is predicted by analyzing the vehicle's driving data, and on this basis, a comprehensive safety buffer zone is calculated for each vehicle within each time step. The combination of this safety buffer zone and the grid space occupancy matrix allows the system to accurately identify and warn of potential trajectory conflicts based on the grid occupancy status before the vehicle enters the intersection, thereby taking measures in advance to avoid possible traffic accidents and significantly improving the safety and traffic efficiency of the intersection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the method of the present invention;

[0042] Figure 2 It is a schematic diagram of the intersection scene and the guidance path;

[0043] Figure 3 It is a schematic diagram of the intersection grid network division;

[0044] Figure 4 It is a schematic diagram of the lateral static buffer zone and longitudinal dynamic buffer zone of the vehicle trajectory;

[0045] Figure 5 It is a schematic diagram of the comprehensive safety buffer zone of vehicle trajectories and its grid occupancy. DETAILED DESCRIPTION

[0046] In order to make the content of the present application more easily be clearly understood, the present application is further described in detail below in combination with the drawings and specific embodiments.

[0047] Preferred traffic embodiment: a certain intersection, as shown in Figure 2 , according to intelligent connected vehicle micro data, the vehicle drives from the west to the intersection, the initial speed of the vehicle is 20 m / s, the acceleration is 5 m / s 2 , the deceleration is -5 m / s 2 , the vehicle width is 2.5 m, and the distance from the intersection is 30 m. The vehicle driving direction is from west to east, and after entering the intersection, it straightly passes through the intersection. The guide path of the vehicle in the intersection is shown in the figure, the guide path speed limit is 10 m / s, and the time step length is 0.5 s.

[0048] Below, a vehicle conflict warning method, system, computer, and storage medium under an intelligent connected environment proposed by the present application will be adopted:

[0049] (1) Based on intelligent connected vehicle micro data and intersection micro data, the initial speed of the vehicle v s = 20 m / s, the acceleration a accel = 5 m / s 2 , the deceleration a decel = -5 m / s 2 , the vehicle width w vehicle = 2.5 m, the initial position of the vehicle from the intersection L a = 30 m; the vehicle straightly passes through the intersection, so the guide path of the vehicle in the intersection is a straight line segment from the west side of the intersection to the east side of the exit lane, the intersection internal guide path speed limit v e = 10 m / s, and the intersection width is the sum of six lane widths, i.e. 6 x 3.5 = 21 m;

[0050] (2) The intersection is divided into grids as shown in Figure 3 , the grid network cell is rectangular, and the length and width are both 3.5 m; in the circular area with the intersection center as the center and the intersection width half as the radius, the grid network cell length and width are halved, i.e.

[0051] (3) Establish the grid space occupation matrix:

[0052]

[0053] Among them, the matrix element with value x does not map the actual grid network cell, but is a supplement to make the number of elements in each row and column equal when establishing the matrix, which does not affect the correspondence between the actual matrix element position relationship and the grid network cell topological relationship.​

[0054] (4) Update the grid space occupancy matrix based on the expected intersection occupancy of other vehicles. Here is the grid space occupancy matrix within [2,2.5]:

[0055]

[0056] (5) Calculate the time t when the target vehicle reaches the stop line of the intersection a , due to v s =20m / s>v e =10m / s, the vehicle decelerates uniformly to the intersection, and the travel time is t a for:

[0057]

[0058] The time when the vehicle is in its initial state is taken as time zero, i.e., t0=0. Therefore, the time when the vehicle enters the intersection is t0+t a =t a ;

[0059] The time step T = 0.5, so the first time step when the vehicle enters the intersection is [t a ,t a +T] = [2, 2.5]s, calculate the distance s(t a +T)-s(t a ):

[0060] s(t a +T)-s(t a )=T·v e =0.5×10=5m

[0061] Establish as Figure 3 The coordinate axes shown in the figure show that the vehicle's trajectory is a line segment because it travels straight through the intersection. The trajectory starts at the middle of the stop line on the west side of the intersection, with coordinates (0, 5.25). The trajectory ends 5 meters later, traveling from west to east, at coordinates (5, 5.25). Therefore, the vehicle's trajectory during this time step is a line segment from (0, 5.25) to (5, 5.25).

[0062] (6) Calculate the lateral static buffer zone and longitudinal dynamic buffer zone of the vehicle trajectory and combine them to form a comprehensive safety buffer zone. Calculate the lateral static buffer zone of the vehicle trajectory:

[0063] Extract the starting point of the trajectory and expand outward along both sides of the path to obtain the extension point r ± (s(t)), where the lateral static safety distance w static =0.25m:

[0064]

[0065]

[0066] The coordinates of the extension points on both sides are (0,6.75) and (0,3.75) respectively;

[0067] Similarly, select other points on the trajectory and calculate the extension points. Connect all the extension points on the same side of the trajectory through straight line segments to form two parallel boundaries of the trajectory. These two boundary lines are finally connected at the starting point and the end point of the trajectory to form a horizontal static buffer around the trajectory. Figure 4 shown.

[0068] Calculate the longitudinal dynamic buffer of the vehicle trajectory:

[0069] Extend the trajectory in the opposite direction from the starting point to obtain the rear extension point of the trajectory:

[0070]

[0071] Since the starting point of the trajectory is located on the boundary of the intersection and the rear part of the trajectory extends beyond the intersection, the dynamic buffer generated by the rear extension of the trajectory does not occupy the internal grid space of the intersection and can be ignored in this example.

[0072] Extend the trajectory in the opposite direction at the end point of the trajectory to obtain the front extension point of the trajectory:

[0073]

[0074] The extended track at the front of the track extends half the vehicle width to both sides in the vertical direction of the extended track Forming the longitudinal dynamic buffer zone of the vehicle, such as Figure 4 shown.

[0075] The horizontal static buffer and the vertical dynamic buffer are combined to obtain the comprehensive buffer, such as Figure 5 The occupancy of the grid cells in the grid network by the integrated buffer is marked in the figure, and its occupancy status is mapped to the temporary grid space occupancy matrix grid temporary middle:

[0076]

[0077] (7) Comparison grid temporary and grid [2,2.5] , we can find at least the element A in the 8th row and 2nd column of the two matrices (9,2)At the same time, it is 1, indicating that there is a potential occupancy conflict between the vehicle's trajectory and the expected space occupancy of the intersection. The system will issue an early warning signal to warn vehicles participating in the traffic of the potential collision risk and remind the vehicle to change its current speed.

[0078] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for warning vehicle conflicts at intersections in an intelligent connected environment, characterized in that: The method comprises the following steps: Step 1: Obtain micro-data for all intelligent connected vehicles and the target intersection. The micro-data for intelligent connected vehicles includes vehicle speed, acceleration, deceleration, length, width, the entrance lanes used by the vehicle to enter the intersection, and the exit lanes planned to exit the intersection. The micro-data for the intersection includes the intersection geometry, the functional purpose of each entrance lane, and the internal guidance paths connecting the entrance and exit lanes, i.e., the specific routes for safe vehicle travel within the intersection, as well as the safety speed limits set for these guidance paths. Step 2: Based on the microscopic data of the intelligent connected vehicle obtained in Step 1, calculate the expected time for the vehicle to enter the intersection and its speed at the time of entry; determine the vehicle's guidance path within the intersection based on the vehicle's entrance lane and the planned exit lane information; based on the above-determined expected time and speed of the vehicle entering the intersection and the guidance path within the intersection, calculate the specific position of the vehicle at each time step during the process of uniformly traveling along the guidance path within the intersection to determine the vehicle's expected spatiotemporal trajectory within the intersection; calculate the trajectory's lateral static buffer based on the vehicle's expected spatiotemporal trajectory within the intersection; calculate the trajectory's longitudinal dynamic buffer based on the vehicle's spatiotemporal trajectory within the intersection and the vehicle's driving speed within the intersection, and combine the lateral static buffer and the longitudinal dynamic buffer to form a comprehensive safety buffer; Step 3: Based on the intersection geometry obtained in step 1, divide the intersection into a grid network; establish a grid space occupancy matrix based on the grid network; update the grid space occupancy matrix based on the expected spatiotemporal trajectories of other vehicles; based on the expected spatiotemporal trajectory of the target vehicle in the intersection, at each discrete time point, use the method of step 2 to establish a comprehensive safety buffer zone, and identify the grid network cells occupied by the comprehensive safety buffer zone in a temporary grid space occupancy matrix; compare the above temporary grid space occupancy matrix with the grid space occupancy matrix. If an occupancy conflict is found, the system will issue a warning signal to indicate the potential collision risk.

2. The method for warning vehicle conflicts at intersections in an intelligent connected environment according to claim 1, characterized in that: The specific method of step 2 is as follows: 2.1 The vehicle moves from its initial position to the intersection with uniform acceleration or deceleration: When v s ≤v e When the vehicle accelerates uniformly to the intersection, the travel time is When v s >v e When the vehicle decelerates uniformly to the intersection, the travel time is Where, t a is the time it takes for the vehicle to travel from its initial position to the stop line at the intersection, in seconds; L a is the distance between the vehicle's initial position and the intersection, in meters; v s is the initial speed of the vehicle, in m / s; v e The speed of the vehicle when entering the intersection is equal to the safe speed limit of the vehicle along the guide path in the intersection, in m / s; accel and a decel Represents the acceleration and deceleration of the vehicle respectively. The acceleration value is positive and the deceleration value is negative. The unit is m / s 2 ; The time when the vehicle is at the initial position is recorded as t0, and the time when the vehicle reaches the stop line of the intersection is the expected time when the vehicle enters the intersection. The expected time when the vehicle enters the intersection is t enter =t0+t a ; 2.2 The guidance path within the intersection is given by the function r(s) = [x(s), y(s)], where s represents the distance traveled along the path from the starting point of the guidance path, and x(s) and y(s) are functions of the spatial coordinates of points on the path. The starting point of the guidance path connects to the entrance road of the intersection, and the end point connects to the exit road of the intersection. 2.3 The movement of a vehicle in an intersection is described by the change in the cumulative distance s(t) along the path from the starting point of the path over time. The specific position of the vehicle at time t is represented as r(s(t)). Within any time step Δt, the vehicle moves from r(s(t)) to r(s(t+Δt)) to form a continuous driving trajectory. After entering the intersection, the vehicle will move at a speed v in the intersection. e Maintaining a constant speed, the distance a vehicle needs to travel along the guided path to pass through the intersection is L inter , the time t that a vehicle is expected to take to pass through the intersection expected =L inter / v e ; Vehicle from r(s(t=t enter ))Move to r(s(t=t enter +t expected )) Obtain the expected spatiotemporal trajectory of the vehicle in the intersection; The lateral static buffer zone described in 2.4 is calculated by extending each point of the vehicle's trajectory on the guided path by a certain distance in the direction of the normal vector. Specifically, the position r(s(t)) on the trajectory is extracted and expanded outward along both sides of the path to obtain the extension point: Among them, r ± (s(t)) represents the extension points on both sides of the trajectory, n(r(s(t))) is the unit normal vector of the path at position r(s(t)), d is half the width of the static buffer, representing the width of the extension from the trajectory to each side, and w vehicle Indicates the width of the vehicle, w static Indicates the set lateral static safety distance, with a value of 0.5m. Calculates the extension points of multiple points on the trajectory and connects all the extension points on the same side of the trajectory with straight lines to form two parallel boundaries of the trajectory. These two boundaries are finally connected at the start and end points of the trajectory to form a lateral static buffer zone around the trajectory. The longitudinal dynamic buffer zone described in 2.5 is calculated by extending the vehicle's trajectory on the guided path in both directions at the starting point and the end point, and extending the trajectory in the opposite direction at the starting point to obtain the rear extension point of the trajectory: Among them, r rear (s(t)) represents the rear extension point of the trajectory, r(s(t-Δt)) represents the starting point of the trajectory in this time step, and also represents the end point of the trajectory in the previous time step; ξ represents the predetermined safety time interval, which is 0.5s; c is a constant, which is 1m; τ(r(s(t-Δt))) represents the unit tangent vector of the guide path at position r(s(t-Δt)); Extend the track along the end point to get the front extension point of the track: Among them, r front (s(t)) represents the front extension point of the trajectory; r(s(t)) is the end point of the trajectory at this time step; τ(r(s(t))) represents the unit tangent vector of the path at position r(s(t)); Front and rear extension tracks extend the vehicle vertically by half the width on either side A rectangular area with the extended trajectory as the symmetry axis is obtained, forming the longitudinal dynamic buffer zone of the vehicle; 2.6 Spatially merge the lateral static buffer zone calculated in step 2.4 and the longitudinal dynamic buffer zone calculated in step 2.5 to construct a complete integrated safety buffer zone for the vehicle trajectory: G=G1∪G2 Among them, G represents the comprehensive safety buffer zone, G1 represents the horizontal static buffer zone, and G2 represents the longitudinal dynamic buffer zone.

3. The method for warning vehicle conflicts at intersections in an intelligent connected environment according to claim 1, characterized in that: The method for step 3 is as follows: The grid network described in 3.1 refers to dividing the internal area of ​​the intersection into regular, continuous cells, each of which is a rectangle, and each grid network cell represents an area of ​​the internal area of ​​the intersection; in the grid network, the length of the grid network cell is m and the width is n; within a circular range with the center of the intersection as the center and a radius of half the width of the intersection, the grid network cells are further subdivided, and the length and width of the subdivided grid network cells are half the length and width of the grid network cells, that is, and Among them, m,n values ​​are between 0.5-5m; 3.2 At a discrete time point, the occupancy status of each cell in the grid network is stored in the corresponding position of the matrix in descending order and from left to right. "Occupancy" refers to the partial spatial overlap of a grid network cell with the vehicle space area or the vehicle integrated safety buffer zone. In the grid space occupancy matrix, occupied grid cells are represented by "1" and unoccupied grid network cells are represented by "0". A grid space occupancy matrix is ​​established at each discrete time point to capture and record the occupancy status of the grid network cells at each discrete time point. 3.3 Use the method in step 2 to calculate the expected spatiotemporal trajectories of other vehicles in the intersection. At each discrete time point, update the grid space occupancy matrix based on the occupancy of grid network cells by other vehicles. 3.4 Use the method in step 2 to calculate the expected spatiotemporal trajectory of the target vehicle in the intersection, and establish a comprehensive safety buffer zone at each discrete time point. Identify the grid network cells occupied by the comprehensive safety buffer zone in a temporary grid space occupancy matrix. 3.5 At each discrete time point, the elements of the temporary grid space occupancy matrix in 3.4 are compared one by one with the elements of the corresponding positions in the grid space occupancy matrix of the intersection. If at least one group of elements in the corresponding positions are all "1", it is determined that an occupancy conflict exists. When any occupancy conflict is detected, the system will issue a warning signal to warn participating vehicles of the potential collision risk and prompt them to change their current speed.

4. A computer device for warning vehicle conflicts at intersections in an intelligent network environment, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

5. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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