CN118243132A - Dynamic path planning method based on Astar algorithm and non-zero and game - Google Patents

Abstract

The invention belongs to the technical field of path planning, and particularly relates to a dynamic path planning method based on an Astar algorithm and non-zero and game, which comprises the following steps: constructing a path planning framework comprising an unmanned plane, a vehicle, an intelligent road side terminal RSU and a core controller; the method comprises the steps that a core controller receives a path planning request from a vehicle; performing path planning by using an Astar algorithm based on the planning factor; the core controller transmits the roads with the occurrence condition to all vehicles, and after the vehicles receive the information, the vehicles detect own driving paths, and if the roads with the occurrence condition are included, a re-path planning request is returned to the core controller; forming a game model by all vehicles returning to the re-path planning request, and returning a new recommended driving path to the vehicles according to non-zero and games; and (5) finishing path planning. The invention can realize reasonable utilization of accident road sections, realize vehicle diversion, maximize vehicle group benefit, reasonably drive, ensure driving safety and reduce the congestion of travel vehicles.

Description

Dynamic path planning method based on Astar algorithm and non-zero-sum game

Technical Field

The invention belongs to the technical field of path planning, and in particular relates to a dynamic path planning method based on an Astar algorithm and a non-zero-sum game.

Background technique

In recent years, with the continuous increase in the number of vehicles on the road, traffic congestion often occurs. In order to cope with the worsening traffic congestion, many researchers have begun to study solutions. At present, some road planning algorithms are very good and can show good performance. For example, by obtaining the remaining time of traffic lights through networking and synchronizing road congestion conditions, a more reasonable driving route can be planned and the travel time can be estimated. However, the perception and avoidance of sudden road situations are still lacking.

Path planning algorithms are algorithms used to find the best path from a starting point to a destination, and are widely used in various fields such as transportation, logistics, robot navigation, etc. The optimal path is determined by considering the cost or weight of different paths in order to maximize efficiency or minimize cost under certain conditions.

In the application process of path planning, the intelligent roadside terminal RSU (Road Side Unit) is a device deployed on the edge of the road or on traffic facilities. It can collect and process traffic data, including road flow, congestion, traffic accidents, vehicle speed and other information, and conduct real-time analysis and processing to generate traffic reports and statistical analysis results to provide decision support for traffic management and planning. In the event of a traffic accident or emergency, the RSU can send emergency service information to the vehicle. UAV (Unmanned Aerial Vehicle) has a variety of functions and strong creativity. It can perform flight missions without human control and complete various tasks such as monitoring, surveying, and cargo transportation through pre-set routes, remote controls or autonomous flights. At this stage, it is also used in road safety, traffic monitoring, and highway infrastructure management.

The defects and shortcomings of existing path planning algorithms are mainly as follows: at present, path planning algorithms are mostly static, that is, the optimal driving path is only returned once by specifying the shortest time or shortest path; the road planning algorithm is based on the shortest time or shortest path, which may cause vehicles to concentrate on the optimal section, causing traffic congestion on the optimal section; there is a delay in the transmission of vehicle information during driving, which poses a safety hazard; in the face of downtown areas where the flow of vehicles and people is growing exponentially, if a traffic accident occurs, the path planning system cannot synchronize information in real time. This information delay will cause vehicles to drive to the accident section as originally planned, causing congestion on the accident section and nearby sections.

Summary of the invention

In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a dynamic path planning method based on the Astar algorithm and non-zero-sum game, which can achieve rational utilization of accident sections, realize vehicle diversion, maximize the benefits of vehicle groups, enable vehicles to travel reasonably, ensure driving safety, and reduce congestion of traveling vehicles.

To achieve the above objectives, the present invention provides a dynamic path planning method based on the Astar algorithm and non-zero-sum game, comprising the following steps:

S1. Build a path planning framework including drones, vehicles, intelligent roadside terminals RSU and core controllers (a well-known core controller can be used), where:

Drones are used to monitor road conditions. When a road condition is observed by a drone, the drone immediately returns the road information to the core controller.

When the vehicle is driving and the road it is on changes, it returns its information to the core controller;

RSU is deployed on each road to calculate the planning factors of the road;

The core controller divides the location information corresponding to each piece of information received into each road, calculates and summarizes the accident scale, construction scale and vehicle area of each road, and sends it to the RSU of the corresponding road, which calculates the planning factor of each road;

A Cartesian coordinate system is established for the area where all roads are located, i represents the horizontal axis and j represents the vertical axis;

S2. The core controller receives a path planning request from the vehicle, which includes a starting position and an end position;

S3. Use the Astar algorithm to perform path planning based on the planning factors and return a recommended driving path to the vehicle. Here, the well-known Astar algorithm (a commonly used path finding and graph traversal algorithm) can be used for path planning.

S4. The core controller receives the road conditions from the drone in real time and sends the road conditions to all vehicles. After receiving the information, the vehicle detects its own driving path. If it includes the road conditions, it returns a request for re-route planning to the core controller, including the current position and the end position; otherwise, it ignores it and continues to drive along the original recommended driving path;

S5. After waiting for a unit time, all vehicles that return re-route planning requests are combined into a game model. In the game model, the game participants are n vehicles that request re-route planning, and the strategy set is the set of recommended driving routes returned by the core controller to each participant. Based on the non-zero-sum game, a new recommended driving route is returned to the vehicle.

S6: The vehicle arrives at the destination, and the path planning ends.

In S1, the road conditions include accidents and road repairs, where:

When the drone finds an accident, it returns the accident location to the core controller and the number of vehicles involved in the accident/> , and then pay attention to the location where the accident occurred. After the accident is handled, the information is returned to the core controller, and the core controller deletes the accident information;

When the drone finds road construction, it observes the construction fence, sends a construction signal to the core controller, and returns to the construction starting point. , Construction end/> And the width of the road occupied by the construction/> ,After receiving the construction information, the core controller makes a judgment on the ,impact of the construction.

When the drone detects an accident, it also reports the accident scale to the core controller. , which is defined as:

(1);

Where S is the projection area of the accident vehicle;

When the drone discovers road construction, it determines the impact of the construction in the following ways:

set up is the width of the road where the construction is taking place,/> For small vehicle width, /> For medium-sized vehicle width, /> is the width of a large vehicle, if/> , then mark the road as inaccessible to all types of vehicles; if , then mark the road as not passable for medium and large vehicles; if/> , then mark the road as not passable by large vehicles; if/> , then calculate the construction scale/> , Construction scale of the starting section of construction/> and the construction scale of the section where construction is completed/> , the calculation formulas are:

(2);

(3);

(4);

Where k is the number of inflection points within the construction range, is the coordinate of the first turning point within the construction range from the construction starting point,/> is the coordinate of the last turning point within the construction range from the construction starting point,/> is the coordinate of the uth turning point within the construction range from the construction starting point,/> It is the coordinate of the u+1th turning point within the construction range from the construction starting point.

In the S1, 、/> 、/> The values are 1.75, 1.9, and 2.5 respectively. For vehicles with a width greater than or equal to 1.6 meters and less than or equal to 1.75 meters, they are set as small vehicles; for vehicles with a width greater than or equal to 1.75 meters and less than 2.5 meters, they are set as medium-sized vehicles; for vehicles with a width greater than or equal to 2.5 meters and less than or equal to 3 meters, they are set as large vehicles. This section is based on the width range set in "GB1589-2016 External dimensions, axle loads and mass limits for automobiles, trailers and automobile trains", and is set to simplify the calculation./> 、/> 、/> The value of .

In S1, the vehicle's own information includes the vehicle's own position and its own projection area/> .

In S1, the accident scale of each road is set to The construction scale is/> , the projection area of the moving vehicle is/> ,Expressed as:

(5);

(6);

(7);

In the formula, is the center point of the road (here it represents the center point of a certain road), /> For/> The set of all points on the road with the center point as the center point, /> For/> The accident point on the road with the center point as the Represents the accident scale of each accident point on the road;/> For/> A road repair point on a road with a central point, /> Represents the construction scale of each road repair point on the road;/> For/> is the vehicle position on the road at the center point, /> represents the projection area of each vehicle on the road; n ₁ , n ₂ , n ₃ are respectively The number of accidents, road repairs, and vehicles on the road with the center point as the center point.

In S1, the planning factor is calculated as follows:

For roads where accidents occurred:

(8);

For roads under construction:

(9);

For roads where accidents have occurred and where construction is ongoing:

(10);

In the formula, For/> is the planning factor of the road at the center point,/> For/> is the total area of the road at the center point,/> are coefficients and sum to 1.

In S1, for the entire construction section, there will also be accident points and moving vehicles (mainly construction vehicles, etc.), and the planning factors are expressed as:

(11);

in:

(12);

(13);

(14);

In the formula, For/> is the planning factor of the road at the center point,/> is the total area of the construction section;/> is the center point of the construction section,/> For/> The set of all points on the road with the center point as the center point; For/> The accident point on the construction section with the center point as the center point,/> Represents the accident scale of each accident point on the road;/> For/> The road repair point on the construction section with the center point as the center point,/> Represents the construction scale of each road repair point on the road;/> For/> is the vehicle position on the construction section at the center point, /> represents the projection area of each vehicle on the road; n ₄ , n ₅ , n ₆ are respectively / > The number of accidents, road repairs, and vehicles on the road with the center point as the center point.

In S5, the process of returning a new recommended driving route to the vehicle based on the non-zero-sum game is as follows:

S51. For the strategy set, the strategy set of the first vehicle is expressed as , the strategy set of the d-th vehicle is expressed as/> , the total strategy set is expressed as ;

S52. The overall profit function of the game model is expressed as:

(15);

In the formula, represents the travel time of each vehicle;

S53. The congestion model of the game model is expressed as:

(16);

In the formula, is the congestion index at time t, /> is the traffic speed at time t, /> is the traffic speed at time t-1, /> is the maximum permissible speed, /> is the traffic density at time t, /> is the traffic density at time t-1, /> is the maximum allowed density, /> is the accident situation at time t, /> Other factors affecting time t (such as weather conditions, morning and evening rush hours, traffic management conditions, etc.), /> is the weight coefficient of traffic speed at time t, /> is the weight coefficient of traffic speed at time t-1,/> is the weight coefficient of traffic density at time t,/> is the weight coefficient of traffic density at time t-1,/> is the weight coefficient of the influence function of traffic density at time t,/> is the weight coefficient of the influence function of traffic density at time t-1,/> is the weight coefficient of the accident situation at time t, /> is the weight coefficient of other influencing factors at time t;

In the game model, the decision of each vehicle can be expressed by minimizing its own driving time. Assume that the driving path selected by vehicle y is , and the set of driving paths selected by other vehicles is/> , then the optimal decision of vehicle y is expressed as:

(17);

In the formula, Indicates the given other vehicle's driving path selection/> In the case of , the travel time of vehicle y is calculated by the congestion model; argmin represents the time that makes/> Minimize the driving route selection.

The algorithm involved in the present invention can be executed by an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the algorithm is implemented by executing software through the processor.

The beneficial effects of the present invention are:

The present invention performs global planning and control through a core controller, transmits traffic accident information by deploying RSU, collects and transmits road information, and uses its computing power to share calculations and calculate planning factors. The design of planning factors can disperse vehicles on the road, realize reasonable vehicle diversion, and avoid concentrated selection of optimal road sections.

The present invention selects UAV to monitor and synchronize accident information and road conditions, uses UAV to monitor roads, shortens the time for synchronizing accident information, and further shortens traffic congestion caused by accident information delay.

The present invention designs a non-zero-sum game model when the vehicle group replans the route, so that the route combination selected by the vehicle is optimal and coordinated and stable, ensuring that each vehicle gets a reasonable route, reducing losses and improving efficiency. The game model is used to achieve reasonable utilization of the accident section, and vehicle diversion is achieved to maximize the benefits of the vehicle group; through overall cooperative operation, vehicles are driven reasonably, driving safety is guaranteed, and congestion of traveling vehicles is reduced.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a schematic diagram of a process of the present invention;

FIG. 2 is a schematic diagram of a path planning framework in an embodiment of the present invention.

Detailed ways

The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

As shown in FIG1 , the dynamic path planning method based on the Astar algorithm and non-zero-sum game includes the following steps:

S1. Build a path planning framework including drones, vehicles, intelligent roadside terminals RSU and core controllers, where:

Drones are used to monitor road conditions. When a road condition is observed by a drone, the drone immediately returns the road information to the core controller.

When the vehicle is driving and the road it is on changes, it returns its information to the core controller;

RSU is deployed on each road to calculate the planning factors of the road;

The core controller divides the location information corresponding to each piece of information received into each road, calculates and summarizes the accident scale, construction scale and vehicle area of each road, and sends it to the RSU of the corresponding road, which calculates the planning factor of each road;

A Cartesian coordinate system is established for the area where all roads are located, i represents the horizontal axis and j represents the vertical axis;

S2. The core controller receives a path planning request from the vehicle, which includes a starting position and an end position;

S3, using the Astar algorithm to perform path planning based on the planning factors, and returning a recommended driving path to the vehicle;

S4. The core controller receives the road conditions from the drone in real time and sends the road conditions to all vehicles. After receiving the information, the vehicle detects its own driving path. If it includes the road conditions, it returns a request for re-route planning to the core controller, including the current position and the end position; otherwise, it ignores it and continues to drive along the original recommended driving path;

S5. After waiting for a unit time, all vehicles that return re-route planning requests are combined into a game model. In the game model, the game participants are n vehicles that request re-route planning, and the strategy set is the set of recommended driving routes returned by the core controller to each participant. Based on the non-zero-sum game, a new recommended driving route is returned to the vehicle.

S6: The vehicle arrives at the destination, and the path planning ends.

In this embodiment, the core controller is used as the processing center of the entire path planning framework to perform macro-control of the entire scene to ensure the normal driving of the vehicle and ensure travel safety.

Among them, the information transmission methods within the path planning framework are divided into:

Category I: Information transmission between RSU and core controller;

Category II: Information transmission between UAV (unmanned aerial vehicle) and RSU;

Category III: Information transmission between RSU and vehicles;

Category IV: Information transmission between UAV and vehicle;

Category V: Information transmission between vehicles.

The information from the UAV is transmitted to the core controller through the RSU. The information generated by the vehicle itself is preferentially transmitted to the RSU; when the distance between the vehicle and the RSU is too far and the transmission conditions are not met, the information is first transmitted to the UAV, which then transmits it to the RSU and then to the core controller; when the distance between the vehicle and both the RSU and the UAV is far, the information can be transmitted between the vehicle groups until the intermediate transmission vehicle meets the above two transmission methods, and then the information is transmitted to the core controller.

The core controller functions include: responsible for maintaining road topology information, including the locations of RSU, UAV and vehicles and the transmission relationship between them; receiving various types of information from RSU, UAV and vehicles, and processing accident information; responding to and processing path planning requests, including the initial path planning and the establishment of a game model during secondary path planning.

Figure 2 shows a simplified path planning framework, using different line types to indicate the information transmission method, and gives a simplified road model, which includes a UAV and an RSU. In addition to the main path planning function, the core controller can also maintain road topology information and ensure road safety.

In S1, road conditions include accidents and road repairs, among which:

When the drone finds an accident, it returns the accident location to the core controller and the number of vehicles involved in the accident/> , and then pay attention to the location where the accident occurred. After the accident is handled, the information is returned to the core controller, and the core controller deletes the accident information;

When the drone finds road construction, it observes the construction fence, sends a construction signal to the core controller, and returns to the construction starting point. , Construction end/> And the width of the road occupied by the construction/> ,After receiving the construction information, the core controller makes a judgment on the ,impact of the construction.

When the drone detects an accident, it also reports the accident scale to the core controller. , which is defined as:

(1);

Where S is the projected area of the accident vehicle;

When the drone discovers road construction, it determines the impact of the construction in the following ways:

set up is the width of the road where the construction is taking place,/> For small vehicle width, /> For medium-sized vehicle width, /> is the width of a large vehicle, if/> , then mark the road as inaccessible to all types of vehicles; if , then mark the road as not passable for medium and large vehicles; if/> , then mark the road as not passable by large vehicles; if/> , then calculate the construction scale/> , Construction scale of the starting section of construction/> and the construction scale of the section where construction is completed/> , the calculation formulas are:

(2);

(3);

(4);

Where k is the number of inflection points within the construction range, is the coordinate of the first turning point within the construction range from the construction starting point,/> is the coordinate of the last turning point within the construction range from the construction starting point,/> is the coordinate of the uth turning point within the construction range from the construction starting point,/> It is the coordinate of the u+1th turning point within the construction range from the construction starting point.

In S1, 、/> 、/> The values are 1.75, 1.9, and 2.5, respectively. For vehicles with a width greater than or equal to 1.6 meters and less than or equal to 1.75 meters, they are set as small vehicles; for vehicles with a width greater than or equal to 1.75 meters and less than or equal to 2.5 meters, they are set as medium-sized vehicles; for vehicles with a width greater than or equal to 2.5 meters and less than or equal to 3 meters, they are set as large vehicles.

In S1, the vehicle’s own information includes the vehicle’s own position and its own projection area/> .

In S1, the accident scale of each road is set to The construction scale is/> , the projection area of the moving vehicle is ,Expressed as:

(5);

(6);

(7);

In the formula, is the center point of the road, /> For/> The set of all points on the road with the center point as the For/> The accident point on the road with the center point as the center point, /> Represents the accident scale of each accident point on the road;/> For/> A road repair point on a road with a central point, /> Represents the construction scale of each road repair point on the road;/> For/> is the vehicle position on the road at the center point, /> represents the projection area of each vehicle on the road; n ₁ , n ₂ , n ₃ are respectively / > The number of accidents, road repairs, and vehicles on the road with the center point as the center point.

In S1, the planning factor is calculated as:

For roads where accidents occurred:

(8);

For roads under construction:

(9);

For roads where accidents have occurred and where construction is ongoing:

(10);

In the formula, For/> is the planning factor of the road at the center point,/> For/> is the total area of the road at the center point,/> are coefficients and sum to 1.

In S1, for the entire construction section, there will also be accident points and moving vehicles involved, and its planning factor is expressed as:

(11);

in:

(12);

(13);

(14);

In the formula, For/> is the planning factor of the road at the center point,/> is the total area of the construction section;/> is the center point of the construction section,/> For/> The set of all points on the road with the center point as the center point; For/> The accident point on the construction section with the center point as the center point,/> Represents the accident scale of each accident point on the road;/> For/> The road repair point on the construction section with the center point as the center point,/> Represents the construction scale of each road repair point on the road;/> For/> is the vehicle position on the construction section at the center point, /> represents the projection area of each vehicle on the road; n ₄ , n ₅ , n ₆ are respectively / > The number of accidents, road repairs, and vehicles on the road with the center point as the center point.

In S5, based on the non-zero-sum game, the process of returning a new recommended driving path to the vehicle is:

S51. For the strategy set, the strategy set of the first vehicle is expressed as , the strategy set of the d-th vehicle is expressed as/> , the total strategy set is expressed as ;

S52. The overall profit function of the game model is expressed as:

(15);

In the formula, represents the travel time of each vehicle;

S53. The congestion model of the game model is expressed as:

(16);

In the formula, is the congestion index at time t, /> is the traffic speed at time t, /> is the traffic speed at time t-1, /> is the maximum permissible speed, /> is the traffic density at time t, /> is the traffic density at time t-1, /> is the maximum allowed density, /> is the accident situation at time t, /> are other influencing factors at time t, /> is the weight coefficient of traffic speed at time t, /> is the weight coefficient of traffic speed at time t-1,/> is the weight coefficient of traffic density at time t,/> is the weight coefficient of traffic density at time t-1,/> is the weight coefficient of the influence function of traffic density at time t,/> is the weight coefficient of the influence function of traffic density at time t-1,/> is the weight coefficient of the accident situation at time t, /> is the weight coefficient of other influencing factors at time t;

In the game model, the decision of each vehicle can be expressed by minimizing its own driving time. Assume that the driving path selected by vehicle y is , and the set of driving paths selected by other vehicles is/> , then the optimal decision of vehicle y is expressed as:

(17);

In the formula, Indicates the given other vehicle's driving path selection/> In the case of , the travel time of vehicle y is calculated by the congestion model; argmin represents the time that makes/> Minimize the driving route selection.

Claims (9)

1. A dynamic path planning method based on Astar algorithm and non-zero-sum game, characterized by comprising the following steps: S1. Build a path planning framework including drones, vehicles, intelligent roadside terminals RSU and core controllers, where: Drones are used to monitor road conditions. When a road condition is observed by a drone, the drone immediately returns the road information to the core controller. When the vehicle is driving and the road it is on changes, it returns its information to the core controller; RSU is deployed on each road to calculate the planning factors of the road; The core controller divides the location information corresponding to each piece of information received into each road, calculates and summarizes the accident scale, construction scale and vehicle area of each road, and sends it to the RSU of the corresponding road, which calculates the planning factor of each road; A Cartesian coordinate system is established for the area where all roads are located, i represents the horizontal axis and j represents the vertical axis; S2. The core controller receives a path planning request from the vehicle, which includes a starting position and an end position; S3, using the Astar algorithm to perform path planning based on the planning factors, and returning a recommended driving path to the vehicle; S4. The core controller receives the road conditions from the drone in real time and sends the road conditions to all vehicles. After receiving the information, the vehicle detects its own driving path. If it includes the road conditions, it returns a request for re-route planning to the core controller, including the current position and the end position; otherwise, it ignores it and continues to drive along the original recommended driving path; S5. After waiting for a unit time, all vehicles that return re-route planning requests are combined into a game model. In the game model, the game participants are n vehicles that request re-route planning, and the strategy set is the set of recommended driving routes returned by the core controller to each participant. Based on the non-zero-sum game, a new recommended driving route is returned to the vehicle. S6: The vehicle arrives at the destination, and the path planning ends. 2. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 1 is characterized in that: in said S1, the road conditions include accidents and road repairs, wherein: When the drone finds an accident, it returns the accident location to the core controller and the number of vehicles involved in the accident , and then pay attention to the location where the accident occurred. After the accident is handled, the information is returned to the core controller, and the core controller deletes the accident information; When the drone finds road construction, it observes the construction fence, sends a construction signal to the core controller, and returns to the construction starting point. , Construction end/> And the width of the road occupied by the construction/> ,After receiving the construction information, the core controller makes a judgment on the ,impact of the construction. 3. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 2 is characterized in that: when the UAV finds an accident, it also returns the accident scale to the core controller , which is defined as: (1); Where S is the projection area of the accident vehicle; When the drone discovers road construction, it determines the impact of the construction in the following ways: set up is the width of the road where the construction is taking place,/> For small vehicle width, /> For medium-sized vehicle width, /> is the width of a large vehicle, if/> , then mark the road as inaccessible to all types of vehicles; if , then mark the road as not passable for medium and large vehicles; if/> , then mark the road as not passable by large vehicles; if/> , then calculate the construction scale/> , Construction scale of the starting section of construction/> and the construction scale of the section where construction is completed/> , the calculation formulas are: (2); (3); (4); Where k is the number of inflection points within the construction range, is the coordinate of the first turning point from the construction starting point within the construction range, is the coordinate of the last turning point within the construction range from the construction starting point,/> is the coordinate of the uth turning point within the construction range from the construction starting point,/> It is the coordinate of the u+1th turning point within the construction range from the construction starting point. 4. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 3 is characterized in that: in said S1, 、/> 、/> The values are 1.75, 1.9, and 2.5, respectively. For vehicles with a width greater than or equal to 1.6 meters and less than or equal to 1.75 meters, they are set as small vehicles; for vehicles with a width greater than or equal to 1.75 meters and less than or equal to 2.5 meters, they are set as medium-sized vehicles; for vehicles with a width greater than or equal to 2.5 meters and less than or equal to 3 meters, they are set as large vehicles. 5. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 3 is characterized in that: in said S1, the self information includes the vehicle's own position and its own projection area/> . 6. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 5 is characterized in that: in said S1, the accident scale of each road is set to The construction scale is/> , the projection area of the moving vehicle is ,Expressed as: (5); (6); (7); In the formula, is the center point of the road, /> For/> The set of all points on the road with the center point as the For/> The accident point on the road with the center point as the center point, /> Represents the accident scale of each accident point on the road;/> For/> A road repair point on a road with a central point, /> Represents the construction scale of each road repair point on the road;/> For/> is the vehicle position on the road at the center point, /> represents the projection area of each vehicle on the road; n ₁ , n ₂ , n ₃ are respectively / > The number of accidents, road repairs, and vehicles on the road with the center point as the center point. 7. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 6 is characterized in that: in said S1, the calculation method of the planning factor is: For roads where accidents occurred: (8); For roads under construction: (9); For roads where accidents have occurred and where construction is ongoing: (10); In the formula, For/> is the planning factor of the road at the center point,/> For/> is the total area of the road at the center point,/> are coefficients and sum to 1. 8. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 7 is characterized in that: in the above S1, for the overall construction section, accident points and running vehicles are also involved, and its planning factor is expressed as: (11); in: (12); (13); (14); In the formula, For/> is the planning factor of the road at the center point,/> is the total area of the construction section; is the center point of the construction section,/> For/> The set of all points on the road with the center point as the center point; For/> The accident point on the construction section with the center point as the center point,/> Represents the accident scale of each accident point on the road;/> For/> The road repair point on the construction section with the center point as the center point,/> Represents the construction scale of each road repair point on the road;/> For/> is the vehicle position on the construction section at the center point, /> represents the projection area of each vehicle on the road; n ₄ , n ₅ , n ₆ are respectively / > The number of accidents, road repairs, and vehicles on the road with the center point as the center point. 9. The dynamic path planning method based on Astar algorithm and non-zero-sum game according to claim 1 is characterized in that: in said S5, the process of returning a new recommended driving path to the vehicle according to the non-zero-sum game is: S51. For the strategy set, the strategy set of the first vehicle is expressed as , the strategy set of the d-th vehicle is expressed as/> , the total strategy set is expressed as ; S52. The overall profit function of the game model is expressed as: (15); In the formula, represents the travel time of each vehicle; S53. The congestion model of the game model is expressed as: (16); In the formula, is the congestion index at time t, /> is the traffic speed at time t, /> is the traffic speed at time t-1, /> is the maximum permissible speed, /> is the traffic density at time t, /> is the traffic density at time t-1, /> is the maximum allowed density, /> is the accident situation at time t, /> are other influencing factors at time t, /> is the weight coefficient of traffic speed at time t, /> is the weight coefficient of traffic speed at time t-1,/> is the weight coefficient of traffic density at time t,/> is the weight coefficient of traffic density at time t-1,/> is the weight coefficient of the influence function of traffic density at time t,/> is the weight coefficient of the influence function of traffic density at time t-1,/> is the weight coefficient of the accident situation at time t, /> is the weight coefficient of other influencing factors at time t; In the game model, the decision of each vehicle can be expressed by minimizing its own driving time. Assume that the driving path selected by vehicle y is , and the set of driving paths selected by other vehicles is/> , then the optimal decision of vehicle y is expressed as: (17); In the formula, Indicates the given other vehicle's driving path selection/> In the case of , the travel time of vehicle y is calculated by the congestion model; argmin represents the time that makes/> Minimize the driving route selection.