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

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

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Publication number
CN118243132A
CN118243132A CN202410666244.7A CN202410666244A CN118243132A CN 118243132 A CN118243132 A CN 118243132A CN 202410666244 A CN202410666244 A CN 202410666244A CN 118243132 A CN118243132 A CN 118243132A
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road
vehicle
construction
accident
vehicles
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CN118243132B (en
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崔鑫
赵庆慧
张艺炜
王靖雯
杜政良
于国龙
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Shandong University of Technology
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Shandong University of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

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 and game
Technical Field
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.
Background
In recent years, as road vehicles continue to increase, traffic congestion is frequently occurring, and many researchers have begun to study solutions to cope with the increased traffic congestion. The partial road planning algorithm at the present stage is quite excellent, can show better performance, for example, the method of acquiring the residual duration of the traffic light, synchronizing the road congestion situation and the like through networking is adopted, a more reasonable driving path is planned, the travel time is estimated, and the perception and avoidance of the road emergency are lacking.
The path planning algorithm is an algorithm for finding an optimal path from a start point to an end point, and is widely used in various fields such as transportation, logistics, robot navigation, and the like. The optimal path is determined by considering the cost or weight of the different paths in order to maximize efficiency or minimize cost under certain conditions.
In the application process of path planning, the intelligent road side terminal RSU (Road Side Unit) is a device deployed on the road edge or traffic facilities, and can collect and process traffic data, including information such as road flow, congestion, traffic accidents, vehicle speed, etc., and perform real-time analysis and processing to generate traffic reports and statistical analysis results, thereby providing decision support for traffic management and planning. In the event of a traffic accident or emergency, the RSU may send emergency service information to the vehicle. Unmanned UAVs (Unmanned AERIAL VEHICLE) are diverse in functions and high in creativity, can perform flight tasks without being controlled by people, and can complete various tasks such as monitoring, reconnaissance, cargo transportation and the like through preset airlines, remote controllers or autonomous flight. The present stage is also used in road safety, traffic monitoring and highway infrastructure management.
The defects and shortcomings of the existing path planning algorithm are mainly: the path planning algorithm at the present stage is mostly static, namely, the optimal running path is returned only once by designating the shortest time or the shortest path; the road planning algorithm is based on the shortest time or the shortest path, and can lead the vehicle to intensively drive to the optimal road section, so that the road of the optimal road section is jammed; the information transmission of the vehicle is delayed in the running process, so that potential safety hazards exist; in the downtown area where traffic flow and people flow are geometrically multiplied, if traffic accidents occur, the path planning system cannot synchronize information in real time, and the information delay can lead vehicles to drive to the accident road section according to the original plan, so that the accident road section and the nearby road sections are jammed.
Disclosure of Invention
According to the defects in the prior art, the invention aims to provide the dynamic path planning method based on the Astar algorithm and the non-zero and game, so that reasonable utilization of an accident road section can be realized, vehicle diversion is realized, the benefit of a vehicle group is maximized, vehicles can reasonably run, driving safety is ensured, and the congestion of the traveling vehicles is reduced.
In order to achieve the above object, the present invention provides a dynamic path planning method based on Astar algorithm and non-zero and game, comprising the following steps:
S1, constructing a path planning framework comprising an unmanned plane, a vehicle, an intelligent road side terminal (RSU) and a core controller (a well-known core controller is adopted), wherein:
the unmanned aerial vehicle is used for supervising road conditions, and when the unmanned aerial vehicle is used for observing the road conditions, the unmanned aerial vehicle immediately returns road information to the core controller;
When the road where the vehicle is located changes in the running process, the vehicle returns the information to the core controller;
The RSU is deployed on each road and used for calculating the planning factor of the road;
Dividing the received position information corresponding to each piece of information into each piece of road by the core controller, calculating and summarizing the accident scale, construction scale and vehicle area of each piece of road, issuing the accident scale, construction scale and vehicle area to the RSU of the corresponding road, and calculating the planning factor of each piece of road by the RSU;
establishing a Cartesian coordinate system for the region where all roads are located, wherein i represents a horizontal axis and j represents a vertical axis;
s2, the core controller receives a path planning request from a vehicle, wherein the request comprises a starting position and an ending position;
s3, using an Astar algorithm, planning a path by using the Astar algorithm based on a planning factor, and returning a recommended driving path to the vehicle; here, a well-known Astar algorithm (a commonly used path searching and graph traversing algorithm) is adopted for path planning;
S4, the core controller receives the road condition from the unmanned aerial vehicle in real time, the road with the condition is issued to all vehicles, after the vehicles receive the information, the vehicles detect own driving paths, and if the road with the condition is included, a re-path planning request including the current position and the end position is returned to the core controller; otherwise, continuing running according to the original recommended running path is ignored;
S5, after waiting for a unit time, forming a game model by all vehicles returning to the re-path planning request, wherein in the game model, game participants are n vehicles requesting re-path planning, a strategy set is a set of recommended driving paths returned to each participant by a core controller, and a new recommended driving path is returned to the vehicles according to non-zero and games;
s6, the vehicle reaches the end position, and the path planning is finished.
In the step S1, the road condition includes accident and road repair, wherein:
When the unmanned aerial vehicle finds an accident, the unmanned aerial vehicle returns to the accident position to the core controller And accident-related vehicle quantity/>Then focusing on the position point of the accident, returning information to the core controller after the accident handling is finished, and deleting the accident information by the core controller;
When the unmanned aerial vehicle finds the road repair condition, the unmanned aerial vehicle returns to the core controller to construct signals and returns to the construction starting point by observing the construction fence Construction end point/>Road width occupied by construction/>And after receiving the construction information, the core controller judges the construction influence.
When the unmanned plane discovers the accident, the accident scale is returned to the core controllerIt is defined as:
（1）；
wherein S is the projection area of the accident vehicle;
when the unmanned aerial vehicle finds the road repair condition, the mode for judging the construction influence is as follows:
Setting up For the width of the road where the construction is located,/>For small vehicle width,/>For medium vehicle width,/>For large vehicle width, if/>Marking the road as non-walkable for the full-size vehicle; if it isMarking the road as non-walkable for medium and large vehicles; if/>Marking the road as non-walkable for large vehicles; if/>Then calculate construction scale/>Construction Scale of construction Start road section/>And construction scale of construction end road section/>The calculation formulas are respectively as follows:
（2）；
（3）；
（4）；
wherein k is the number of inflection points in the construction range, For 1 st inflection point coordinates from the construction start point in the construction range,/>For the last inflection point coordinate from the construction start point in the construction range,/>For the u-th inflection point coordinate from the construction start point in the construction range,/>The u+1st inflection point coordinates from the construction start point in the construction range.
In the step S1, the step C,、/>、/>The values are respectively 1.75, 1.9 and 2.5, wherein, for vehicles with the width of more than or equal to 1.6 meters and less than or equal to 1.75 meters, the vehicles are set as small-sized vehicles; for a vehicle having a width of more than 1.75 meters and less than 2.5 meters, it is set as a medium-sized vehicle; for vehicles having a width of 2.5 meters or more and 3 meters or less, a large vehicle is set. The section is a width range set based on GB1589-2016 automobile, trailer and train overall size, axle load and mass limit, and the/>, is set for simplifying calculation、/>、/>Is a value of (a).
In the step S1, the self information includes the self position of the vehicleSelf projected area/>。
In the step S1, the accident scale of each road is set asConstruction Scale is/>The projected area of the running vehicle is/>Expressed as:
（5）；
（6）；
（7）；
In the method, in the process of the invention, Is the road center point (here, represents the center point of a certain road),/>To/>Is a set of all points on the road of the central point,/>To/>Is an accident point on the road at the center point,Representing the accident scale of each accident point on the road; /(I)To/>Repair point on road as center point,/>Representing the construction scale of each road repairing point on the road; /(I)To/>Vehicle position on road as center point,/>Representing the projected area of each traveling vehicle on the road; n 1、n2、n3 are respectivelyThe number of accident points, the number of repair points and the number of running vehicles on the road which is the center point.
In the step S1, the calculation mode of the planning factor is as follows:
for the road where the accident occurs:
（8）；
For the road being constructed:
（9）；
for roads where accidents occur and are being constructed:
（10）；
In the method, in the process of the invention, To/>Planning factor of road as center point,/>To/>Total area of road as center point,/>Is a coefficient and the sum is 1.
In S1, for the whole construction section, accident points and traveling vehicles (mainly for construction vehicles and the like) are also involved, and the planning factor is expressed as follows:
（11）；
Wherein:
（12）；
（13）；
（14）；
In the method, in the process of the invention, To/>Planning factor of road as center point,/>Is the total area of the construction section; /(I)For the center point of the construction road section,/>To/>A set of all points on the road that is a center point; To/> Accident Point on construction section being center Point,/>Representing the accident scale of each accident point on the road; /(I)To/>Repair point on construction road section being center point,/>Representing the construction scale of each road repairing point on the road; /(I)To/>Vehicle position on construction section as center point,/>Representing the projected area of each traveling vehicle on the road; n 4、n5、n6 is respectively expressed as/>The number of accident points, the number of repair points and the number of running vehicles on the road which is the center point.
In the step S5, the process of returning the new recommended driving path to the vehicle according to the non-zero game is as follows:
S51, regarding the strategy set, the strategy set of the 1 st vehicle is expressed as The policy set for the d-th vehicle is denoted/>The total policy set is expressed as；
S52, the overall benefit function of the game model is expressed as:
（15）；
In the method, in the process of the invention, Representing the travel time of each vehicle;
s53, expressing a congestion model of the game model as:
（16）；
In the method, in the process of the invention, 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 allowable speed,/>Is the traffic density at time t,/>Is the traffic density at time t-1,/>Is the maximum allowable density,/>Is the accident occurrence at time t,/>Is other influencing factors (e.g. weather conditions, peaks in the morning and evening, traffic management conditions etc.) at time t,/>Is the weight coefficient of the traffic speed at the moment t,/>Is the weight coefficient of the traffic speed at the time t-1,/>Is the weight coefficient of traffic density at time t,/>Is the weight coefficient of traffic density at t-1 time,/>Weight coefficient of influence function of traffic density at t moment,/>Weight coefficient of influence function of traffic density at t-1 time,/>Is the weight coefficient of the accident situation at the moment t,/>The weight coefficient of other influencing factors at the moment t;
In the game model, the decision of each vehicle can be represented by minimizing the own travel time, assuming that the travel path selected by the vehicle y is While the travel path set selected by other vehicles is/>Then the optimal decision for vehicle y is expressed as:
（17）；
In the method, in the process of the invention, Representing given other vehicle travel path selection/>In the case of (2), the travel time of the vehicle y is calculated by the congestion model; argmin represents such/>Minimized travel path selection.
The algorithm according to the present invention may be executed by an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the algorithm being implemented by the processor executing software.
The invention has the beneficial effects that:
the invention carries out global planning control through the core controller, transmits traffic accident information through the deployment RSU, collects and transmits road information, and simultaneously utilizes the computing capacity to allocate and calculate to calculate planning factors; the design of the planning factors can enable vehicles to be scattered on the road, reasonable shunting of the vehicles is achieved, and centralized selection of the optimal road sections is avoided.
According to the invention, the UAVs are selected to monitor and synchronize accident information and road conditions, and the UAVs are used to monitor roads, so that the time for synchronizing the accident information is shortened, and the traffic jam caused by accident information delay is further shortened.
According to the invention, the non-zero and game models are designed when the vehicle group re-plans the paths, so that the path combination benefits of the vehicles are optimal, mutually coordinated and stable, each vehicle is ensured to obtain a reasonable path, the loss is reduced, and the efficiency is improved. Reasonable utilization of accident road sections is realized through a game model, vehicle diversion is realized, and vehicle group benefits are maximized; through integral cooperative operation, reasonable running of the vehicle is realized, running safety is guaranteed, and congestion of the traveling vehicle is reduced.
Drawings
FIG. 1 is a flow schematic of the present invention;
fig. 2 is a schematic diagram of a path planning framework according to an embodiment of the present invention.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
As shown in fig. 1, the dynamic path planning method based on the Astar algorithm and the non-zero and game comprises the following steps:
S1, constructing a path planning framework comprising an unmanned plane, a vehicle, an intelligent road side terminal RSU and a core controller, wherein:
the unmanned aerial vehicle is used for supervising road conditions, and when the unmanned aerial vehicle is used for observing the road conditions, the unmanned aerial vehicle immediately returns road information to the core controller;
When the road where the vehicle is located changes in the running process, the vehicle returns the information to the core controller;
The RSU is deployed on each road and used for calculating the planning factor of the road;
Dividing the received position information corresponding to each piece of information into each piece of road by the core controller, calculating and summarizing the accident scale, construction scale and vehicle area of each piece of road, issuing the accident scale, construction scale and vehicle area to the RSU of the corresponding road, and calculating the planning factor of each piece of road by the RSU;
establishing a Cartesian coordinate system for the region where all roads are located, wherein i represents a horizontal axis and j represents a vertical axis;
s2, the core controller receives a path planning request from a vehicle, wherein the request comprises a starting position and an ending position;
s3, using an Astar algorithm, planning a path by using the Astar algorithm based on a planning factor, and returning a recommended driving path to the vehicle;
S4, the core controller receives the road condition from the unmanned aerial vehicle in real time, the road with the condition is issued to all vehicles, after the vehicles receive the information, the vehicles detect own driving paths, and if the road with the condition is included, a re-path planning request including the current position and the end position is returned to the core controller; otherwise, continuing running according to the original recommended running path is ignored;
S5, after waiting for a unit time, forming a game model by all vehicles returning to the re-path planning request, wherein in the game model, game participants are n vehicles requesting re-path planning, a strategy set is a set of recommended driving paths returned to each participant by a core controller, and a new recommended driving path is returned to the vehicles according to non-zero and games;
s6, the vehicle reaches the end position, and the path planning is finished.
In the embodiment, the core controller is used as a processing center of the whole path planning framework to macroscopically regulate and control the whole scene, so that the normal running of the vehicle is ensured, and the trip safety is ensured.
The information transmission modes in the path planning framework are divided into:
Class I: information transfer between the RSU and the core controller;
Class II: information transfer between UAV (unmanned aerial vehicle) and RSU;
class III: information transfer between the RSU and the vehicle;
Class IV: information transfer between the UAV and the vehicle;
Class V: information transfer from vehicle to vehicle.
Information from the UAV is passed to the core controller through the RSU. The information generated by the vehicle is preferentially selected to be transmitted by the RSU; when the distance between the UAV and the RSU is far and does not meet the transmission condition, the information is transmitted to the UAV, and then transmitted to the RSU and then transmitted to the core controller; when the distance between the vehicle and the RSU and the UAV is far, the information can be transmitted among the vehicle groups until the intermediate transmission vehicle meets the two transmission modes, and the information is transmitted to the core controller.
The core controller functions include: is responsible for maintaining road topology information, including the positions of RSUs, UAVs and vehicles and the transfer relationship between them; receiving various information from the RSU, the UAV and the vehicle, and processing accident information; and responding and processing the path planning request, including initial path planning and building a game model during path planning again.
Fig. 2 shows a simplified path planning framework, with different linearities indicating the way of information delivery, and a simplified road model, comprising a UAV and an RSU, the core controller implementing, in addition to the main path planning functions, the functions of maintaining road topology information and ensuring road safety.
In S1, the road condition includes an accident and a road repair situation, where:
When the unmanned aerial vehicle finds an accident, the unmanned aerial vehicle returns to the accident position to the core controller And accident-related vehicle quantity/>Then focusing on the position point of the accident, returning information to the core controller after the accident handling is finished, and deleting the accident information by the core controller;
When the unmanned aerial vehicle finds the road repair condition, the unmanned aerial vehicle returns to the core controller to construct signals and returns to the construction starting point by observing the construction fence Construction end point/>Road width occupied by construction/>And after receiving the construction information, the core controller judges the construction influence.
When the unmanned plane discovers the accident, the accident scale is returned to the core controllerIt is defined as:
（1）；
wherein S is the projection area of the accident vehicle;
when the unmanned aerial vehicle finds the road repair condition, the mode for judging the construction influence is as follows:
Setting up For the width of the road where the construction is located,/>For small vehicle width,/>For medium vehicle width,/>For large vehicle width, if/>Marking the road as non-walkable for the full-size vehicle; if it isMarking the road as non-walkable for medium and large vehicles; if/>Marking the road as non-walkable for large vehicles; if/>Then calculate construction scale/>Construction Scale of construction Start road section/>And construction scale of construction end road section/>The calculation formulas are respectively as follows:
（2）；
（3）；
（4）；
wherein k is the number of inflection points in the construction range, For 1 st inflection point coordinates from the construction start point in the construction range,/>For the last inflection point coordinate from the construction start point in the construction range,/>For the u-th inflection point coordinate from the construction start point in the construction range,/>The u+1st inflection point coordinates from the construction start point in the construction range.
In the step S1, the step of,、/>、/>The values are respectively 1.75, 1.9 and 2.5, wherein, for vehicles with the width of more than or equal to 1.6 meters and less than or equal to 1.75 meters, the vehicles are set as small-sized vehicles; for a vehicle having a width of more than 1.75 meters and less than 2.5 meters, it is set as a medium-sized vehicle; for vehicles having a width of 2.5 meters or more and 3 meters or less, a large vehicle is set.
In S1, the self information includes the self position of the vehicleSelf projected area/>。
S1, setting the accident scale of each road asConstruction Scale is/>The projection area of the running vehicle isExpressed as:
（5）；
（6）；
（7）；
In the method, in the process of the invention, Is the center point of the road,/>To/>A set of all points on the road that is the center point,To/>Accident Point on road being the center Point,/>Representing the accident scale of each accident point on the road; /(I)To/>Repair point on road as center point,/>Representing the construction scale of each road repairing point on the road; /(I)To/>Vehicle position on road as center point,/>Representing the projected area of each traveling vehicle on the road; n 1、n2、n3 is respectively expressed as/>The number of accident points, the number of repair points and the number of running vehicles on the road which is the center point.
In S1, the calculation mode of the planning factor is as follows:
for the road where the accident occurs:
（8）；
For the road being constructed:
（9）；
for roads where accidents occur and are being constructed:
（10）；
In the method, in the process of the invention, To/>Planning factor of road as center point,/>To/>Total area of road as center point,/>Is a coefficient and the sum is 1.
In S1, for the whole construction section, accident points and running vehicles are also involved, and the planning factor is expressed as follows:
（11）；
Wherein:
（12）；
（13）；
（14）；
In the method, in the process of the invention, To/>Planning factor of road as center point,/>Is the total area of the construction section; /(I)For the center point of the construction road section,/>To/>A set of all points on the road that is a center point; To/> Accident Point on construction section being center Point,/>Representing the accident scale of each accident point on the road; /(I)To/>Repair point on construction road section being center point,/>Representing the construction scale of each road repairing point on the road; /(I)To/>Vehicle position on construction section as center point,/>Representing the projected area of each traveling vehicle on the road; n 4、n5、n6 is respectively expressed as/>The number of accident points, the number of repair points and the number of running vehicles on the road which is the center point.
In S5, according to the non-zero game and the game, the process of returning a new recommended driving path to the vehicle is as follows:
S51, regarding the strategy set, the strategy set of the 1 st vehicle is expressed as The policy set for the d-th vehicle is denoted/>The total policy set is expressed as；
S52, the overall benefit function of the game model is expressed as:
（15）；
In the method, in the process of the invention, Representing the travel time of each vehicle;
s53, expressing a congestion model of the game model as:
（16）；
In the method, in the process of the invention, 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 allowable speed,/>Is the traffic density at time t,/>Is the traffic density at time t-1,/>Is the maximum allowable density,/>Is the accident occurrence at time t,/>Is the other influencing factor at time t,/>Is the weight coefficient of the traffic speed at the moment t,/>Is the weight coefficient of the traffic speed at the time t-1,/>Is the weight coefficient of traffic density at time t,/>Is the weight coefficient of traffic density at t-1 time,/>Weight coefficient of influence function of traffic density at t moment,/>Weight coefficient of influence function of traffic density at t-1 time,/>Is the weight coefficient of the accident situation at the moment t,/>The weight coefficient of other influencing factors at the moment t;
In the game model, the decision of each vehicle can be represented by minimizing the own travel time, assuming that the travel path selected by the vehicle y is While the travel path set selected by other vehicles is/>Then the optimal decision for vehicle y is expressed as:
（17）；
In the method, in the process of the invention, Representing given other vehicle travel path selection/>In the case of (2), the travel time of the vehicle y is calculated by the congestion model; argmin represents such/>Minimized travel path selection.

Claims (9)

1. The dynamic path planning method based on the Astar algorithm and the non-zero and game is characterized by comprising the following steps:
S1, constructing a path planning framework comprising an unmanned plane, a vehicle, an intelligent road side terminal RSU and a core controller, wherein:
the unmanned aerial vehicle is used for supervising road conditions, and when the unmanned aerial vehicle is used for observing the road conditions, the unmanned aerial vehicle immediately returns road information to the core controller;
When the road where the vehicle is located changes in the running process, the vehicle returns the information to the core controller;
The RSU is deployed on each road and used for calculating the planning factor of the road;
Dividing the received position information corresponding to each piece of information into each piece of road by the core controller, calculating and summarizing the accident scale, construction scale and vehicle area of each piece of road, issuing the accident scale, construction scale and vehicle area to the RSU of the corresponding road, and calculating the planning factor of each piece of road by the RSU;
establishing a Cartesian coordinate system for the region where all roads are located, wherein i represents a horizontal axis and j represents a vertical axis;
s2, the core controller receives a path planning request from a vehicle, wherein the request comprises a starting position and an ending position;
s3, using an Astar algorithm, planning a path by using the Astar algorithm based on a planning factor, and returning a recommended driving path to the vehicle;
S4, the core controller receives the road condition from the unmanned aerial vehicle in real time, the road with the condition is issued to all vehicles, after the vehicles receive the information, the vehicles detect own driving paths, and if the road with the condition is included, a re-path planning request including the current position and the end position is returned to the core controller; otherwise, continuing running according to the original recommended running path is ignored;
S5, after waiting for a unit time, forming a game model by all vehicles returning to the re-path planning request, wherein in the game model, game participants are n vehicles requesting re-path planning, a strategy set is a set of recommended driving paths returned to each participant by a core controller, and a new recommended driving path is returned to the vehicles according to non-zero and games;
s6, the vehicle reaches the end position, and the path planning is finished.
2. The Astar algorithm and non-zero and game based dynamic path planning method according to claim 1, characterized in that: in the step S1, the road condition includes accident and road repair, wherein:
When the unmanned aerial vehicle finds an accident, the unmanned aerial vehicle returns to the accident position to the core controller And accident-related vehicle quantityThen focusing on the position point of the accident, returning information to the core controller after the accident handling is finished, and deleting the accident information by the core controller;
When the unmanned aerial vehicle finds the road repair condition, the unmanned aerial vehicle returns to the core controller to construct signals and returns to the construction starting point by observing the construction fence Construction end point/>Road width occupied by construction/>And after receiving the construction information, the core controller judges the construction influence.
3. The Astar algorithm and non-zero and game based dynamic path planning method according to claim 2, characterized in that: when the unmanned plane discovers the accident, the accident scale is returned to the core controllerIt is defined as:
（1）；
wherein S is the projection area of the accident vehicle;
when the unmanned aerial vehicle finds the road repair condition, the mode for judging the construction influence is as follows:
Setting up For the width of the road where the construction is located,/>For small vehicle width,/>For medium vehicle width,/>For large vehicle width, if/>Marking the road as non-walkable for the full-size vehicle; if it isMarking the road as non-walkable for medium and large vehicles; if/>Marking the road as non-walkable for large vehicles; if/>Then calculate construction scale/>Construction Scale of construction Start road section/>And construction scale of construction end road section/>The calculation formulas are respectively as follows:
（2）；
（3）；
（4）；
wherein k is the number of inflection points in the construction range, To construct the 1 st inflection point coordinates from the start point of construction in the range,For the last inflection point coordinate from the construction start point in the construction range,/>For the u-th inflection point coordinate from the construction start point in the construction range,/>The u+1st inflection point coordinates from the construction start point in the construction range.
4. A dynamic path planning method based on the Astar algorithm and the non-zero and game according to claim 3, characterized in that: in the step S1, the step C,、/>、/>The values are respectively 1.75, 1.9 and 2.5, wherein, for vehicles with the width of more than or equal to 1.6 meters and less than or equal to 1.75 meters, the vehicles are set as small-sized vehicles; for a vehicle having a width of more than 1.75 meters and less than 2.5 meters, it is set as a medium-sized vehicle; for vehicles having a width of 2.5 meters or more and 3 meters or less, a large vehicle is set.
5. A dynamic path planning method based on the Astar algorithm and the non-zero and game according to claim 3, characterized in that: in the step S1, the self information includes the self position of the vehicleSelf projected area/>。
6. The Astar algorithm and non-zero and game based dynamic path planning method according to claim 5, characterized in that: in the step S1, the accident scale of each road is set asConstruction Scale is/>The projection area of the running vehicle isExpressed as:
（5）；
（6）；
（7）；
In the method, in the process of the invention, Is the center point of the road,/>To/>A set of all points on the road that is the center point,To/>Accident Point on road being the center Point,/>Representing the accident scale of each accident point on the road; /(I)To/>Repair point on road as center point,/>Representing the construction scale of each road repairing point on the road; /(I)To/>Vehicle position on road as center point,/>Representing the projected area of each traveling vehicle on the road; n 1、n2、n3 is respectively expressed as/>The number of accident points, the number of repair points and the number of running vehicles on the road which is the center point.
7. The Astar algorithm and non-zero and game based dynamic path planning method according to claim 6, characterized in that: in the step S1, the calculation mode of the planning factor is as follows:
for the road where the accident occurs:
（8）；
For the road being constructed:
（9）；
for roads where accidents occur and are being constructed:
（10）；
In the method, in the process of the invention, To/>Planning factor of road as center point,/>To/>Total area of road as center point,/>Is a coefficient and the sum is 1.
8. The Astar algorithm and non-zero and game based dynamic path planning method of claim 7, wherein: in the step S1, for the whole construction section, accident points and running vehicles are also involved, and the planning factors are expressed as follows:
（11）；
Wherein:
（12）；
（13）；
（14）；
In the method, in the process of the invention, To/>Planning factor of road as center point,/>Is the total area of the construction section; for the center point of the construction road section,/> To/>A set of all points on the road that is a center point; To/> Accident Point on construction section being center Point,/>Representing the accident scale of each accident point on the road; /(I)To/>Repair point on construction road section being center point,/>Representing the construction scale of each road repairing point on the road; /(I)To/>Vehicle position on construction section as center point,/>Representing the projected area of each traveling vehicle on the road; n 4、n5、n6 is respectively expressed as/>The number of accident points, the number of repair points and the number of running vehicles on the road which is the center point.
9. The Astar algorithm and non-zero and game based dynamic path planning method according to claim 1, characterized in that: in the step S5, the process of returning the new recommended driving path to the vehicle according to the non-zero game is as follows:
S51, regarding the strategy set, the strategy set of the 1 st vehicle is expressed as The policy set for the d-th vehicle is denoted/>The total policy set is expressed as；
S52, the overall benefit function of the game model is expressed as:
（15）；
In the method, in the process of the invention, Representing the travel time of each vehicle;
s53, expressing a congestion model of the game model as:
（16）；
In the method, in the process of the invention, 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 allowable speed,/>Is the traffic density at time t,/>Is the traffic density at time t-1,/>Is the maximum allowable density,/>Is the accident occurrence at time t,/>Is the other influencing factor at time t,/>Is the weight coefficient of the traffic speed at the moment t,/>Is the weight coefficient of the traffic speed at the time t-1,/>Is the weight coefficient of traffic density at time t,/>Is the weight coefficient of traffic density at t-1 time,/>Weight coefficient of influence function of traffic density at t moment,/>Weight coefficient of influence function of traffic density at t-1 time,/>Is the weight coefficient of the accident situation at the moment t,/>The weight coefficient of other influencing factors at the moment t;
In the game model, the decision of each vehicle can be represented by minimizing the own travel time, assuming that the travel path selected by the vehicle y is While the travel path set selected by other vehicles is/>Then the optimal decision for vehicle y is expressed as:
（17）；
In the method, in the process of the invention, Representing given other vehicle travel path selection/>In the case of (2), the travel time of the vehicle y is calculated by the congestion model; argmin represents such/>Minimized travel path selection.
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