CN112461256A

CN112461256A - Path planning method and device

Info

Publication number: CN112461256A
Application number: CN202110146371.0A
Authority: CN
Inventors: 马忠伟; 周玮玉; 王劲
Original assignee: Ciic Technology Co ltd
Current assignee: Ciic Technology Co ltd
Priority date: 2021-02-03
Filing date: 2021-02-03
Publication date: 2021-03-09
Anticipated expiration: 2041-02-03
Also published as: CN112461256B

Abstract

The method comprises the steps of firstly determining a target driving area comprising a plurality of sub-areas, determining candidate sub-areas according to predicted congestion values of the sub-areas at preset time points, then determining a target area navigation path according to a travel starting point, a travel destination and the candidate sub-areas of a vehicle to be planned, and when the vehicle to be planned enters the current sub-area along the target area navigation path, determining a target lane navigation path according to the target area navigation path, the current position of the vehicle to be planned and traffic flow loads of all road sections in the current sub-area. The method and the system have the advantages that the vehicle planning problem in a large range is divided into the vehicle planning problems in a plurality of small ranges, a large number of vehicle planning scenes can be met, traffic flow loads of all sub-areas and all road sections are integrated when the vehicles are planned, the effects of avoiding the sub-areas with congestion and realizing load balance of the whole traffic network can be achieved simultaneously, and therefore the applicability is high.

Description

Path planning method and device

Technical Field

The application relates to the technical field of automatic driving, in particular to a path planning method and device.

Background

When an autonomous vehicle travels, a travel path needs to be planned for the autonomous vehicle, wherein planning of autonomous vehicles in a certain area is involved. The existing planning method can only meet the requirements of small-range path planning and single-scene planning, is not suitable for large-range vehicle planning and cannot adapt to variable application scenes, and the planning of the shortest time path or the shortest distance path for the vehicle to be planned can only be realized during the path planning, so that the congestion area in the traffic network cannot be avoided from the perspective of the whole traffic network, and the load balance is realized.

Therefore, the existing path planning method has the technical problem of poor applicability, and needs to be improved.

Disclosure of Invention

The embodiment of the application provides a path planning method and a path planning device, which are used for relieving the technical problem of weak applicability in the existing path planning method.

In order to solve the above technical problem, an embodiment of the present application provides the following technical solutions:

the application provides a path planning method, which comprises the following steps:

determining a target driving area according to a travel starting point and a travel end point of a vehicle to be planned, wherein the target driving area comprises a plurality of sub-areas, and the sub-areas comprise a plurality of crossed lanes;

determining candidate sub-regions according to the predicted congestion values of all the sub-regions in the target driving region at a preset time point;

determining a target area navigation path according to the travel starting point, the travel end point and the candidate sub-area, so that the vehicle to be planned determines information of a driving area;

when the vehicle to be planned drives into the current sub-area, acquiring traffic flow loads of all road sections in the current sub-area;

and determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the area navigation path, the current position of the vehicle to be planned and the traffic load of each road section in the current sub-area, so that the vehicle to be planned determines the information of a driving lane.

An embodiment of the present application further provides a path planning apparatus, where the path planning apparatus includes:

the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining a target driving area according to a travel starting point and a travel ending point of a vehicle to be planned, the target driving area comprises a plurality of sub-areas, and the sub-areas comprise a plurality of crossed lanes;

the second determination module is used for determining candidate sub-regions according to the predicted congestion values of all the sub-regions in the target driving region at a preset time point;

the third determining module is used for determining a target area navigation path according to the travel starting point, the travel end point and the candidate sub-area, so that the vehicle to be planned determines the information of a driving area;

the obtaining module is used for obtaining traffic load of each road section in the current sub-area when the vehicle to be planned drives into the current sub-area;

and the fourth determining module is used for determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the area navigation path, the current position of the vehicle to be planned and the traffic flow load of each road section in the current sub-area, so that the vehicle to be planned determines the information of the driving lane.

The application also provides an electronic device comprising a memory and a processor; the memory stores an application program, and the processor is configured to run the application program in the memory to perform any one of the operations of the path planning method.

The present application also provides a computer-readable storage medium having stored thereon a computer program for execution by a processor to implement the path planning method of any of the above.

Has the advantages that: the embodiment of the application provides a path planning method and a device, when a vehicle is planned for a journey, a target driving area is divided into sub-areas and predicted congestion values of the sub-areas are calculated respectively, the vehicle planning problem in a large area can be divided into vehicle planning problems in a plurality of small areas, therefore, a large number of vehicle planning scenes can be met, according to the predicted congestion values, the whole congestion sub-areas can be avoided when determining the area navigation paths of the vehicle among the sub-areas, the congestion risk of the vehicle is reduced, subsequently, in each sub-area where the target area navigation paths pass, the vehicle load conditions of all road sections in the sub-area are integrated, the target lane navigation path of a lane in each sub-area is obtained, and the vehicle can obtain a better lane navigation path in each sub-area, namely, the traffic flow loads of the sub-areas and the road sections are integrated when the vehicle is planned, the method can simultaneously achieve the effects of avoiding the sub-areas of congestion and realizing load balance of the whole traffic network, and is also suitable for large-scale vehicle planning scenes, so that the method is high in applicability.

Drawings

The technical solution and other advantages of the present application will become apparent from the detailed description of the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a scene schematic diagram applicable to the path planning method provided in the embodiment of the present application.

Fig. 2 is a first flowchart of a path planning method according to an embodiment of the present application.

Fig. 3 is a schematic diagram illustrating map sub-area division in the path planning method according to the embodiment of the present application.

Fig. 4 is a schematic diagram of sub-area division of map congestion and non-congestion in the path planning method provided in the embodiment of the present application.

Fig. 5 is a first schematic diagram of generation of a target area navigation path in the path planning method according to the embodiment of the present application.

Fig. 6 is a second schematic diagram of generation of a target area navigation path in the path planning method according to the embodiment of the present application.

Fig. 7 is a schematic diagram illustrating generation of a target lane navigation path in the path planning method according to the embodiment of the present application.

Fig. 8 is a second flowchart of the path planning method according to the embodiment of the present application.

Fig. 9 is a schematic structural diagram of a path planning apparatus according to an embodiment of the present application.

Fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 1, fig. 1 is a schematic view of a scenario in which the path planning method provided in the embodiment of the present application is applied, where the scenario may include terminals and servers, and the terminals, the servers, and the terminals and the servers are connected and communicated through the internet formed by various gateways, and the like, where the application scenario includes a data acquisition device 11, a server 12, and a vehicle 13; wherein:

the data acquisition device 11 may be a roadside sensor arranged on both sides of a lane in vehicle-road cooperation, and includes a camera, a laser, a GPS antenna, a radar, etc., which can realize accurate acquisition of various lanes and vehicle-related data on the road, and calculation of the acquired data, etc., thereby acquiring information such as traffic flow of each lane;

the server 12 comprises a local server and/or a remote server and the like;

the vehicle 13 may be an autonomous vehicle or a manned vehicle, and the vehicle 13 is configured to travel according to a planned travel path.

The data acquisition device 11, the server 12 and the vehicle 13 are located in a wireless network or a wired network to realize data interaction among the three, wherein:

the server 12 determines a target driving area according to a travel starting point and a travel ending point of a vehicle 13 to be planned, the target driving area comprises a plurality of sub-areas, each sub-area comprises a plurality of crossed lanes, the data acquisition device 11 acquires the current traffic flow of each sub-area, and completes real-time evaluation or prediction of the traffic network state through an intelligent algorithm, so as to obtain the predicted traffic flow of each sub-area at a preset time point and send the predicted traffic flow to the server 12, the server 12 determines the sub-area with the predicted congestion value not greater than the preset value as a candidate sub-area according to the acquired predicted congestion value of each sub-area at the preset time point, then acquires the travel starting point, the travel ending point and the candidate sub-area of the target driving area in the current planning period of the vehicle 13 to be planned, and determines a navigation path of the target area, when the vehicle 13 to be planned enters the current sub-area, the data acquisition device 11 acquires traffic loads of road sections in the current sub-area and sends the traffic loads to the server 12, and the server 12 determines and generates a target lane navigation path of the vehicle to be planned in the current sub-area according to the target area navigation path, the current position of the vehicle 13 to be planned and the traffic loads of the road sections in the current sub-area, so that the vehicle 13 to be planned determines driving lane information. Finally, the server 12 sends the target lane navigation path to the vehicle 13 to be planned, so that the vehicle 13 to be planned runs in the current sub-area along the target lane navigation path, and in each current sub-area, the above operations are performed until the vehicle 13 to be planned runs to the travel end.

It should be noted that the system scenario diagram shown in fig. 1 is only an example, and the server and the scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not form a limitation on the technical solution provided in the embodiment of the present application, and as a person having ordinary skill in the art knows, with the evolution of the system and the occurrence of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems. The following are detailed below. It should be noted that the following description of the embodiments is not intended to limit the preferred order of the embodiments.

Referring to fig. 2, fig. 2 is a first schematic flow chart of a path planning method according to an embodiment of the present application, where the method includes:

s201: and determining a target driving area according to the travel starting point and the travel end point of the vehicle to be planned, wherein the target driving area comprises a plurality of sub-areas, and the sub-areas comprise a plurality of crossed lanes.

The vehicles to be planned can be automatic driving vehicles or unmanned vehicles, and the number of the vehicles to be planned is multiple. And determining a target driving area on the map according to the travel starting points and the travel end points of the vehicles to be planned, so that the travel starting points and the travel end points of all the vehicles to be planned fall in the target driving area. Each vehicle to be planned in the target driving area needs to be planned with a corresponding driving path.

The shape of the target travel area may be a regular rectangle, a circle, or the like, or may be other shapes. In one embodiment, the target driving area may be a city, a county, a district, etc., or any other defined area, as long as all vehicles to be planned are within the area. The target driving area comprises a plurality of intersecting lanes, each lane intersection point forms an intersection, the part of each lane between adjacent intersections forms a road section, when one lane is intersected with the plurality of lanes, the lane can form a plurality of road sections and a plurality of intersections, and at each intersection, vehicles can choose to continue driving on the current lane or drive into another lane.

The target driving area is divided into a plurality of sub-areas, and various modes can be adopted during the division. In one embodiment, a plurality of main lanes extending in a first direction and a second direction intersecting with each other may be determined, where the first direction and the second direction form an angle therebetween, for example, the first direction and the second direction may be a transverse direction and a longitudinal direction, and since the actual lanes cannot be guaranteed to be straight lines, most of the actual lanes are straight lines, curves or broken line combinations, and the like, the main lanes determined in this step may only have a direction substantially the same as the first direction or the second direction. In addition, the main lane is generally a long and wide lane in the target driving area, and when there are some special situations, such as a starting point or an ending point of a certain longitudinal lane falling in the target driving area, the lane, a second lane extending in the longitudinal direction nearest to the starting point or the ending point of the certain longitudinal lane, and a part of the transverse lane between the two lanes can be considered as a main lane together. The number of selectable main lanes may also be different according to the size of the target driving area, for example, m transverse main lanes and n longitudinal main lanes are determined in the target driving area, and a plurality of dividing lines arranged along the first direction and a plurality of dividing lines arranged along the second direction are generated on one side of each main lane according to a principle that the main lanes are consistent with the extending direction of each main lane, so that the target driving area can be divided into (m + 1) (n + 1) sub-areas by the intersected dividing lines at most. The selection of m and n can follow an equal division principle, namely the quantity of intersections in each sub-area after division is approximately equal, and the subsequent calculation amount of the traffic flow data of each sub-area is relatively even. Of course, the number of intersections in each divided sub-area may also be unequal, and the numerical value of the intersection is determined according to the actual scene.

The above description only shows one mode of dividing the target driving area, but the present application is not limited thereto, and a person skilled in the art may select the dividing mode of the target driving area according to needs, for example, the target driving area may be divided into a plurality of sub areas with regular shapes, or into a plurality of sub areas with equal areas, or the sub areas may be divided according to the actual administrative area of the road surface. In order to avoid unclear division range of the sub-regions, all the division lines do not directly pass through all the intersections and the lanes, but intersect all the road sections.

Taking fig. 3 as an example, a peripheral limiting line 101 limits a target driving area, a dividing line 102 is formed in the limiting line 101, specifically, 2 transverse dividing lines and 2 longitudinal dividing lines, and the dividing line 102 divides the target driving area into 8 sub-areas, namely, an area a, an area B, an area C, an area D, an area E, an area F, an area G and an area H. Within each sub-region, a plurality of intersecting lanes are included, each lane intersection forming a plurality of intersections 111 and road segments 112 between adjacent intersections 111.

In the embodiment of the application, the target driving area is divided in the map, the map is used for reflecting the actual road conditions on the ground and providing driving directions for the driving of the vehicle, the map comprises high-precision coordinates and accurate road shapes, the lane data of each lane of the road surface comprises lane sidelines, lane central lines, lane gradient, curvature, course, elevation, roll and the like which can be embodied in the map, and in addition, data such as overhead objects, guard rails, trees, road edge types, road edge landmarks and the like are also included. The target travel area in the map corresponds to the target travel area on the actual ground, and the target travel area is divided in the map, so that the adjacency relation between the sub-areas can be obtained. It should be noted that the limiting line 101 and the dividing line 102 shown in fig. 3 are used for assisting the area division, and in practical applications, may be real lines that can be displayed on a map, or may be virtual lines that are only used for background capture and recognition, that is, the limiting line 101 and the dividing line 102 displayed on the map in fig. 3 are only one example of the area division, and do not constitute a limitation on the actual area division, and any other means that can realize the area division falls within the protection scope of the present application.

The vehicle planning problem belongs to a combinatorial optimization problem, and is generally an NP-Hard (NP-Hard) problem, namely a nondeterministic problem of polynomial complexity. Thus, as the number of vehicles to be planned increases, the algorithm often faces dimensional disasters and fails to provide an effective solution for a given planning cycle. In the method, the target driving area is divided into the sub-areas, the planning problem of the large area originally is divided into the planning problems in a plurality of sub-areas and between the sub-areas, and the calculation amount related to each sub-area is greatly reduced, so that the problem solving difficulty is reduced, and a large number of vehicle planning scenes can be met.

S202: and determining a candidate sub-region according to the predicted congestion value of the sub-region in the target driving region at a preset time point.

After the sub-areas are divided, the congestion conditions of the sub-areas are evaluated respectively, then the congested sub-areas and the non-congested sub-areas are determined, when a vehicle to be planned is planned, the vehicle is controlled to run only in the non-congested sub-areas, the whole congested sub-areas can be avoided, and the congestion risk of the vehicle is reduced. When a vehicle to be planned is planned in a target driving area, a certain time is often needed to complete a journey, the state of a traffic network is changed in real time, and it is unreasonable to generate a navigation path by only taking the traffic flow state at the current moment as a reference. Therefore, the predicted congestion value tau of each sub-region at the preset time point T is obtained firstly, the predicted congestion value reflects the overall congestion value degree of the sub-region at the preset time point T, and if the predicted congestion value tau is not larger than the preset value T1, the sub-regions are indicated to be not congested at the preset time point T, so that the sub-regions can be determined as candidate sub-regions, and vehicles to be planned are controlled to run in the candidate sub-regions subsequently. As shown in fig. 4, after the predicted congestion value τ of each sub-region is obtained, it is determined that the D-region and the E-region are congestion sub-regions, and the other sub-regions are candidate sub-regions.

In one embodiment, the steps specifically include: acquiring the current traffic flow of each subregion through a roadside sensing unit; determining the predicted traffic flow of each subarea at a preset time point according to the current traffic flow of each subarea; determining a predicted congestion value of each sub-area at a preset time point according to the predicted traffic flow; and determining the sub-region with the predicted congestion value not greater than the preset value as a candidate sub-region. Under the scene of vehicle-road cooperation, roadside sensing units are arranged on two sides of each lane on the actual road surface, and specifically comprise a camera, a laser instrument, a GPS antenna, a radar and the like, so that the accurate acquisition of various lanes and vehicle related data on the road surface can be realized, thereby obtaining the information such as real-time traffic flow of each lane, and the information can complete the real-time evaluation or prediction of traffic network traffic flow state through an intelligent algorithm deployed at the road side or the cloud end to obtain the predicted traffic flow of each sub-area at the preset time point, then the predicted traffic flow of each sub-area is used as the input of a predicted congestion degree evaluation model to individually evaluate the congestion degree of each sub-area, that is, for each specific sub-region, the congestion degree at the preset time point t may be obtained, and then the sub-region having the predicted congestion value not greater than the preset value is determined as a candidate sub-region.

In one embodiment, the step of determining the predicted traffic flow of each sub-area at the preset time point according to the current traffic flow of each sub-area includes: acquiring experience passing time of each subregion; determining a preset time point according to the maximum experience passing time or the weighted average experience passing time of all the sub-regions; and determining the predicted traffic flow of each subarea at a preset time point according to the current traffic flow of each subarea. For each subarea, the experience passing time of the subarea can be obtained by counting the running data of all vehicles in the subarea in a certain time period, the experience passing time is the approximate time required by a certain vehicle to drive in from one entrance to drive out from one exit of the subarea, the number of the vehicles in each subarea is more than one, the number of the entrances and the number of the exits are multiple, and the running paths of different vehicles which drive in from the same entrance and drive out from the same exit in the subarea are different, so the required time period is different. When the experience passing time length is obtained, the passing time lengths of all vehicles in the sub-area within a certain period of time need to be integrated, then weighting operation is carried out, and the obtained experience passing time length accords with the running rule of most vehicles, namely after most vehicles drive into a certain entrance of the sub-area, the most vehicles can drive out from a certain exit through the experience passing time length. Because the positions of the sub-areas are different, the positions and the numbers of corresponding inlets and outlets, the number of road sections and intersections in the sub-areas and the complexity of lanes are different, the experience passing time of the sub-areas is also different. After the experience passing time of each sub-area is obtained, the largest one of the experience passing time is used as the maximum experience passing time, or the weighted average of the experience passing time is used as the weighted average experience time, the preset time point is determined according to the maximum experience passing time or the weighted average experience passing time of all the sub-areas, and then the predicted traffic flow of each sub-area at the preset time point is determined according to the current traffic flow.

Taking the sub-areas divided in fig. 3 as an example, assuming that the experience passing time of the area a, the area B and the area C is 20 minutes, the experience passing time of the area D, the area E and the area F is 30 minutes, the experience passing time of the area G and the area H is 40 minutes, the current time is 12 o 'clock at noon, the maximum experience passing time is 40 minutes, the preset time point determined according to the maximum experience passing time is 12 o' clock 40 minutes, and after the traffic flow of 12 o 'clock is obtained, the predicted traffic flow of each sub-area at 12 o' clock 40 minutes is determined. Or setting the experience time length weight of each sub-area in advance, calculating to obtain the weighted experience passing time length, determining a preset time point according to the weighted experience passing time length, and further determining the predicted traffic flow of each sub-area at the preset time point. From the current moment, if the predicted traffic flow after a short time is calculated, because most vehicles do not have enough time to drive out of the current sub-area and only drive in the sub-area, the congestion condition of each sub-area cannot be changed greatly; since the experience passing time length reflects the running time length of the majority of vehicles in each subregion, from the current moment, after the maximum experience passing time length or the weighted average experience passing time length, the majority of vehicles in the target running region already drive into another subregion from one subregion, so that the traffic flow in each subregion is relatively large relative to the current moment, and the probability that the original non-congestion subregion becomes the congestion subregion is relatively large due to the change, so that the predicted traffic flow of the preset time point is obtained, the congestion value of each subregion is further calculated, the method is more accurate in determining the congestion subregion and the non-congestion subregion, and has reference value.

In the above embodiment, the preset time point is determined through the duration according to experience, but the preset time point is only one example, the application is not limited to this, and the preset time point may be set in other manners, and a person skilled in the art may set the value of the preset time point according to needs and obtain the corresponding preset traffic flow.

S203: and determining a navigation path of the target area according to the travel starting point, the travel end point and the candidate sub-area so as to enable the vehicle to be planned to determine the information of the driving area.

In one embodiment, S203 specifically includes: determining a stroke starting point sub-region where the stroke starting point is located and a stroke end point sub-region where the stroke end point is located according to the stroke starting point and the stroke end point; and determining a target area navigation path connecting the travel starting point sub-area and the travel end point sub-area according to the travel starting point sub-area, the travel end point sub-area and the candidate sub-area, wherein the sub-areas positioned between the travel starting point sub-area and the travel end point sub-area on the target area navigation path are all the candidate sub-areas.

When planning a vehicle, a planning period is set first, and in each planning period, a plurality of vehicles to be planned are subjected to route planning. For a certain vehicle to be planned, the travel starting point and the travel end point of the vehicle are preset places in advance, after specific positions of the travel starting point and the travel end point are obtained, sub-regions where the travel starting point and the travel end point are respectively located can be determined, then the congestion sub-region determined in the previous step is avoided, the sub-region where the travel starting point is located is taken as the travel starting point sub-region, the sub-region where the travel end point is located is taken as the travel end point sub-region, the travel starting point sub-region and the travel end point sub-region are connected, and a target region navigation path of the vehicle to be planned is generated, wherein all sub-regions on the target region navigation path between the travel starting point sub-region and the travel end point sub-region are candidate sub-regions, and the generated target region.

As shown in fig. 5, after the specific positions of the trip start point q1 and the trip end point q2 are obtained, the trip start point sub-area a where the trip start point q1 is located and the trip end point sub-area H where the trip end point q2 is located may be determined, and then the congestion sub-area D and the congestion sub-area E are avoided, so as to generate the target area navigation path 51 connecting the area a and the area H and sequentially passing through a plurality of candidate sub-areas.

The trip start point q1 and the trip end point q2 shown in fig. 5 are not contiguous, and therefore at least one other candidate sub-region needs to be passed between the trip start sub-region and the trip end sub-region. When the stroke start point q1 and the stroke end point q2 are adjacent to each other, other candidate sub-regions do not need to be passed between the two, and the corresponding stroke start point sub-region and stroke end point sub-region can be directly connected.

In one embodiment, S203 specifically includes: determining the shortest connecting track connecting the stroke starting point sub-region and the stroke end point sub-region by a heuristic search method, wherein the sub-regions positioned between the stroke starting point sub-region and the stroke end point sub-region on the shortest connecting track are all candidate sub-regions; and determining a target area navigation path of the vehicle to be planned according to the shortest connecting track. After the target driving area avoids the congestion sub-area, the number of the area navigation paths which can be generated is different according to the different positions of the sub-areas where the travel starting point and the travel ending point are located and the different number of the candidate sub-areas in the target driving area. When the number of candidate sub-areas in the target driving area is small, as shown in fig. 5, only one area navigation path may be generated by connecting the area a of the travel starting point sub-area and the area H of the travel ending point sub-area, and thus the area navigation path is directly used as the target area navigation path 51. When the number of candidate sub-areas in the target driving area is large, the travel starting sub-area and the travel ending sub-area are connected, and a plurality of connection schemes which do not pass through the congestion sub-area are available.

As shown in fig. 6, when the congestion sub-area is only the area E, the area navigation path connecting the area a of the travel starting point sub-area and the area H of the travel ending point sub-area may include a first area navigation path 501 and a second area navigation path 502, where the first area navigation path 501 sequentially passes through the area a, the area B, the area C, the area F and the area H, and the second area navigation path 502 sequentially passes through the area a, the area D, the area G and the area H, and at this time, in order to ensure that the traveling path of the vehicle to be planned is the shortest, the traveling paths of the first area navigation path 501 and the second area navigation path 502 need to be compared, and the shortest one is taken as the target area navigation path.

As can be seen from fig. 6, when the number of congested sub-areas in the target driving area is large, the more the schemes of the area navigation paths can be obtained, and if all the corresponding area navigation paths are calculated for each vehicle to be planned, and then the target area navigation path with the shortest path is determined, more time and effort are spent. Therefore, the shortest connecting track for connecting the travel starting point sub-area and the travel destination sub-area in the target driving area is directly determined through a heuristic search method, the congested sub-area is taken as an obstacle to avoid during determination, when the travel starting point sub-area and the travel destination sub-area are not adjacent, sub-areas positioned between the travel starting point sub-area and the travel destination sub-area on the shortest connecting track are candidate sub-areas, and finally a target area navigation path of the vehicle to be planned is determined according to the shortest connecting track, wherein the target area navigation path is the optimal area navigation path of the vehicle to be planned among different sub-areas.

In the embodiment of the application, the heuristic search method refers to evaluating each searched position when searching in the state space to obtain the best position, and then searching from the position to the target, so that a large number of meaningless search paths can be omitted, and the efficiency is improved. In each heuristic search method, the improved a-algorithm is preferably used for generating the target area navigation path, and other heuristic search methods or a machine model can be adopted for calculation and generation.

And a plurality of vehicles to be planned are arranged in each planning period, so that a heuristic search method can be used for generating corresponding target area navigation paths for all the vehicles to be planned.

S204: and when the vehicle to be planned drives into the current sub-area, acquiring the traffic flow load of each road section in the current sub-area.

After the target area navigation path is generated, vehicles to be planned sequentially enter the sub-areas from the travel starting point sub-area along the target area navigation path until the travel end point is reached, the sub-areas are sequentially determined as current sub-areas, and when the vehicles enter each current sub-area to be ready for running, the traffic flow load of each road section in each current sub-area is firstly obtained.

In one embodiment, S204 specifically includes: acquiring the current traffic flow of each road section in the current sub-area and the predicted traffic flow of all planned vehicles on each road section; and determining the traffic load of each road section in the current sub-area according to the current traffic flow and the predicted traffic flow. The method comprises the steps that a plurality of crossed lanes are included in a current sub-area, and traffic flows in a plurality of road sections of each lane are different, the current traffic flow of each road section in the current sub-area and the predicted traffic flow of all planned vehicles in each road section are obtained firstly, wherein the current traffic flow of each road section is the sum of the actual traffic flow caused by the driving of the current manned vehicle and the actual traffic flow caused by the driving of other planned automatic driving vehicles, the predicted traffic flow is the traffic flow which is caused by the driving of other planned automatic driving vehicles along respective generated target lane navigation paths and passes through each road section on the target lane navigation paths in the future. For the planned vehicles, the parameters such as the driving route, the driving speed and the like can be obtained in advance, so that the sections to which the planned vehicles will drive in the future can be determined according to the parameters, and further the sections to which the traffic flow of the sections will change at the future time point of the planned vehicles can be determined. The predicted traffic flow and the current actual traffic flow are comprehensively considered, the traffic load of all road sections in the target driving area can be obtained, and the numerical value obtained by comprehensively considering the predicted traffic flow and the current actual traffic flow is more accurate.

S205: and determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the area navigation path, the current position of the vehicle to be planned and the traffic flow load of each road section in the current sub-area, so that the vehicle to be planned determines the information of the driving lane.

When the vehicle to be planned enters each current sub-area to be driven, the server determines a lane navigation path with the minimum comprehensive traffic flow load and the shortest path from a plurality of crossed lanes in the current sub-area according to the whole traffic network load in the current sub-area, and the lane navigation path is used as a target lane navigation path of the vehicle to be planned in the current sub-area.

In one embodiment, S205 specifically includes: acquiring navigation paths of all candidate lanes in the current sub-area; determining a plurality of lane navigation paths of the vehicle to be planned in the current sub-area from all the candidate lane navigation paths according to the target area navigation path and the current position of the vehicle to be planned; and determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the traffic load of each road section in the current sub-area.

As shown in fig. 7, the diagram of the sub-area C is a schematic diagram of the sub-area C, there are 7 lanes in the sub-area C, which are a first transverse lane 71, a second transverse lane 72, a third transverse lane 73, a fourth transverse lane 74, a first longitudinal lane 75, a sixth longitudinal lane 76 and a seventh longitudinal lane 77, respectively, each lane intersects with each other to form 10 intersections in the sub-area C, and vehicles traveling in the sub-area C can enter other sub-areas through each lane. When a vehicle to be planned enters a C area, all candidate lane navigation paths in the C area are obtained firstly, the candidate lane navigation paths comprise all paths of any two sub-areas adjacent to the C area through the C area, in the figure 7, the sub-areas adjacent to the C area only include a B area and an F area, the B area is on the left side of the C area, the F area is on the lower side of the C area, 4 inlets are formed when the vehicle enters the C area from the B area, the 4 inlets are a, B, C and d respectively, 3 outlets are formed when the vehicle enters the F area from the C area, and the 3 outlets are e, F and g respectively. Taking the area C as an example in fig. 7, if the area E in fig. 3 is the area E, the distribution positions and the number of corresponding entrances and exits will be more since the area E is respectively adjacent to the area B, the area D, the area H and the area F, and the number of the candidate lane navigation paths in the generated candidate lane navigation path set R will also be more.

After the candidate lane navigation path set R is generated, a plurality of lane navigation paths of the vehicle to be planned in the current sub-area are determined from all the candidate lane navigation paths according to the target area navigation path and the current position of the vehicle to be planned. Assuming that the direction of the target area navigation path in fig. 7 is a B-C-F area, and the lane from which the to-be-planned autopilot lane exits the B area is a second transverse lane 72, after the current position of the to-be-planned vehicle is obtained, the only entry B from which the B area enters the C area can be determined, and according to the direction of the target area navigation path, which sub-area the to-be-planned vehicle is to travel to, i.e., a plurality of exits can be determined, then all the lane navigation paths from the entry B and exiting from the exits e, F, and g are the lane navigation paths of the to-be-planned vehicle in the C area are selected from all the candidate lane navigation paths in the candidate lane navigation path set R, and a lane navigation path set Ri is generated according to these lane navigation paths, and then the to-be-planned vehicle can enter the F area from the entry B through any one of the lane navigation paths in Ri.

After the lane navigation path set Ri is generated, a target lane navigation path of the vehicle to be planned in the current sub-area is determined and generated according to the traffic load of each road section in the current sub-area, and the target lane navigation path is the path with the minimum comprehensive load and the shortest path in all the lane navigation paths. Based on the load condition obtained in S204, a heuristic search method is used to select an entry road marking navigation path from the lane navigation path set Ri for each vehicle to be planned, and the path lengths of all the autonomous vehicles and the traffic network loads in the sub-areas are used as two minimized objective functions, and finally the target lane navigation paths determined for each vehicle to be planned are the optimal lane navigation paths corresponding to the vehicle to be planned, with the minimum load and the shortest path. Through the method, as shown in fig. 7, the target lane navigation path 701 can be finally obtained in the area C, when the vehicle to be planned runs along the target lane navigation path 701 in the area C, the path is shortest, and the vehicle to be planned runs smoothly due to a small load, and the time taken is also shortest.

In each heuristic search method, the target lane navigation path is preferably generated based on a category variable constraint multi-objective evolutionary algorithm, wherein the category variable is traffic load and path length, and other heuristic search methods or a machine model mode can be adopted for calculation and generation.

In one embodiment, after S205, the method further includes: and sending the target lane navigation path to the vehicle to be planned so that the vehicle to be planned runs in the current sub-area along the target lane navigation path.

For each vehicle to be planned, starting from the journey starting sub-area to ending from the journey ending sub-area, generating a target lane navigation path in the current sub-area when entering each current sub-area, sending the generated target lane navigation path to the vehicle to be planned, enabling the vehicle to be planned to run in the current sub-area along the target area navigation path and the target lane navigation path, and driving into the next sub-area until reaching the journey ending point. And finishing the overall planning of each vehicle to be planned in the current planning period.

In one embodiment, after S205, the method further includes: judging whether a new planning period is started; and if so, re-executing the determination operation of the candidate sub-area, and re-executing the generation operation of the navigation path of the target area and the determination operation of the navigation path of the target lane according to the current position and the travel end point of the vehicle to be planned. Since there are multiple vehicles to be planned in each planning cycle, there will be some vehicles to be planned that have not yet reached the starting point of the journey at the end of one planning cycle. After a period of time elapses in the current planning cycle, it is first determined whether to start a new planning cycle, if the new planning cycle has not started, all vehicles to be planned continue to travel along the current target lane navigation path, if the new planning cycle has started, the real-time information of each vehicle to be planned, including the sub-region where the vehicle is located, the current position, and the like, is updated, and when each vehicle to be planned enters another current sub-region from one current sub-region, in the new planning cycle, the operation of determining the candidate sub-regions is re-executed, and the operation of generating the target region navigation path and the operation of determining the target lane navigation path are re-executed according to the current position and the trip end point of the vehicle to be planned, that is, the operations of S202 to S205 are repeated.

In the prior art, no specific scheme capable of solving path planning in a large range exists at present. Although some solutions exist relating to the planning of autonomous vehicles, they have not been successfully applied in the context of vehicle access systems. In the prior art, some technical schemes are solutions or improvements based on the traditional manned vehicle planning problem, but problems of inapplicability of problem models, dimension disasters, solving efficiency and the like generally exist when the method is used for automatically planning vehicle paths in a large range; some solutions are designed based on machine learning models (e.g., reinforcement learning), but due to differences in application scenarios, model inputs, etc., the applicability of these models and the generalization ability of the models cannot be verified. In addition, in the prior art, the navigation paths with the shortest time consumption or the shortest distance are often allocated to the vehicles to be planned from the perspective of the vehicles to be planned, but the navigation paths cannot be generated from the perspective of the whole traffic network at the same time so as to avoid the congested area in the traffic network and achieve load balancing.

In the path planning method, when planning the vehicle, the target driving area is divided into sub-areas and the predicted congestion value of each sub-area is calculated respectively, the vehicle planning problem in a large area can be divided into a plurality of vehicle planning problems in a small area, so that a large number of vehicle planning scenes can be met, according to the predicted congestion value, the whole congestion sub-area can be avoided when determining the area navigation path of the vehicle among the sub-areas, the congestion risk of the vehicle is reduced, subsequently, in each candidate sub-area through which the target area navigation path passes, the vehicle load conditions of all road sections in the candidate sub-area are synthesized, the target navigation path of the lane in each candidate sub-area is obtained, and the vehicle can obtain a better lane navigation path in each candidate sub-area, namely, the traffic flow load of each sub-area and each road section is synthesized when planning the vehicle, the method can simultaneously achieve the effects of avoiding the sub-areas of congestion and realizing load balance of the whole traffic network, and is also suitable for large-scale vehicle planning scenes, so that the method is high in applicability.

Referring to fig. 8, fig. 8 is a second schematic flow chart of a path planning method according to an embodiment of the present application, where the method includes:

s801: and starting.

S802: and (4) dividing the map area. The map is used for reflecting the actual road conditions on the ground and providing driving guide for the driving of the automatic driving vehicle, the target driving area in the map corresponds to the target driving area on the actual ground, and after the target driving area is divided in the map, the adjacent relation among the sub-areas can be obtained. Dividing a target driving area into a plurality of sub-areas in a map, wherein each sub-area comprises a plurality of intersecting lanes, each lane intersection point forms an intersection, and the part of each lane between adjacent intersections forms a road section.

S803: and introducing the predicted traffic flow to evaluate the congestion degree. After sub-areas are divided, the current traffic flow of each sub-area is obtained through a roadside sensing unit, real-time assessment or prediction of traffic flow states of a traffic network is completed through an intelligent algorithm deployed on the roadside or at the cloud, the predicted traffic flow of each sub-area at a preset time point is obtained, then the predicted traffic flow of each sub-area is used as input of a predicted congestion degree assessment model, congestion degree of each sub-area is individually assessed, a predicted congestion value of each sub-area at the preset time point is obtained, the predicted congestion value is compared with the preset value, a congested sub-area and a non-congested sub-area are determined, when a vehicle to be planned is planned, the vehicle is controlled to run only in the non-congested sub-area, the whole congested sub-area can be avoided, and congestion risks of the vehicle are reduced.

S804: optimizing the upper layer: and generating a target area navigation path between the sub-areas. When planning a vehicle, a planning period is set first, and in each planning period, a plurality of vehicles to be planned are subjected to route planning. For a certain vehicle to be planned, the travel starting point and the travel end point of the vehicle are preset places in advance, after specific positions of the travel starting point and the travel end point are obtained, sub-regions where the travel starting point and the travel end point are respectively located can be determined, then the congestion sub-region determined in the previous step is avoided, the sub-region where the travel starting point is located is taken as the travel starting point sub-region, the sub-region where the travel end point is located is taken as the travel end point sub-region, the travel starting point sub-region and the travel end point sub-region are connected, and a target region navigation path of the vehicle to be planned is generated, wherein all sub-regions on the target region navigation path between the travel starting point sub-region and the travel end point sub-region are candidate sub-regions, and the generated target region.

S805: optimizing the lower layer: and generating a target lane navigation path in the sub-area. After the target area navigation path is generated, a vehicle to be planned sequentially enters each sub-area from the starting point sub-area of the travel route along the target area navigation path until the end point of the travel route is reached, the sub-areas are sequentially determined as current sub-areas, when the vehicle enters each current sub-area to be driven, the server determines a lane navigation path with the minimum traffic load and the shortest path from a plurality of lanes intersected in the current sub-area according to the whole traffic network load in the current sub-area, and the lane navigation path is used as the target lane navigation path of the vehicle to be planned in the current sub-area.

S806: it is determined whether a new planning cycle is to begin. If so, re-execute S803 to S805. And if a new planning period is started, updating the real-time information of each vehicle to be planned, including the sub-region where the vehicle is located, the current position and the like, and when each vehicle to be planned enters another current sub-region from one current sub-region, re-executing the congestion degree evaluation, the upper-layer optimization and the lower-layer optimization in the new planning period.

If not, go to step S807: and (6) ending.

Through the steps, the map area is divided, and in each planning period, congestion degree evaluation and double-layer optimization are performed, so that the effects of avoiding a congestion sub-area and realizing load balance of the whole traffic network can be achieved simultaneously, and the method is also suitable for a large-scale vehicle planning scene, and therefore, the applicability is strong.

Correspondingly, fig. 9 is a schematic structural diagram of a path planning apparatus provided in an embodiment of the present application, please refer to fig. 9, where the path planning apparatus includes:

the first determining module 110 is configured to determine a target driving area according to a trip start point and a trip end point of a vehicle to be planned, where the target driving area includes a plurality of sub-areas, and the sub-areas include a plurality of intersecting lanes;

the second determining module 120 is configured to determine candidate sub-regions according to predicted congestion values of all sub-regions in the target driving region at a preset time point;

the third determining module 130 is configured to determine a target area navigation path according to the travel starting point, the travel end point, and the candidate sub-area, so that the vehicle to be planned determines information of a driving area;

the obtaining module 140 is configured to obtain traffic loads of road segments in the current sub-area when a vehicle to be planned drives into the current sub-area;

the fourth determining module 150 is configured to determine a target lane navigation path of the vehicle to be planned in the current sub-area according to the area navigation path, the current position of the vehicle to be planned, and traffic flow loads of road segments in the current sub-area, so that the vehicle to be planned determines information of a driving lane.

In one embodiment, the second determining module 120 includes:

the first acquisition submodule is used for acquiring the current traffic flow of each sub-area through the roadside sensing unit;

the first determining submodule is used for determining the predicted traffic flow of each subarea at a preset time point according to the current traffic flow of each subarea;

the second determining submodule is used for determining the predicted congestion value of each sub-area at a preset time point according to the predicted traffic flow;

and the third determining submodule is used for determining the sub-region with the predicted congestion value not greater than the preset value as a candidate sub-region.

In one embodiment, the first determining submodule is used for acquiring experience passing time of each sub-area; and determining a preset time point according to the maximum experience passing time or the weighted average experience passing time of all the sub-areas, and determining the predicted traffic flow of each sub-area at the preset time point according to the current traffic flow of each sub-area.

In one embodiment, the third determining module 130 includes:

the fourth determining submodule is used for determining a stroke starting point sub-area where the stroke starting point is located and a stroke end point sub-area where the stroke end point is located according to the stroke starting point and the stroke end point;

and the fifth determining sub-module is used for determining a target area navigation path connecting the travel starting point sub-area and the travel end point sub-area according to the travel starting point sub-area, the travel end point sub-area and the candidate sub-area, wherein all sub-areas positioned between the travel starting point sub-area and the travel end point sub-area on the target area navigation path are the candidate sub-areas.

In one embodiment, the fifth determining submodule is configured to determine, by using a heuristic search method, a shortest connecting track connecting the stroke starting sub-region and the stroke ending sub-region, where all sub-regions located between the stroke starting sub-region and the stroke ending sub-region on the shortest connecting track are candidate sub-regions; and determining a target area navigation path of the vehicle to be planned according to the shortest connecting track.

In one embodiment, the obtaining module 140 includes:

the second obtaining submodule is used for obtaining the current traffic flow of each road section in the current sub-area and the predicted traffic flow of all planned vehicles on each road section;

and the sixth determining submodule is used for determining the traffic load of each road section in the current sub-area according to the current traffic flow and the predicted traffic flow.

In one embodiment, the fourth determination module 150 includes:

the third acquisition sub-module is used for acquiring all candidate lane navigation paths in the current sub-area;

the seventh determining submodule is used for determining a plurality of lane navigation paths of the vehicle to be planned in the current sub-area from all the candidate lane navigation paths according to the target area navigation paths and the current position of the vehicle to be planned;

and the eighth determining submodule is used for determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the traffic load of each road section in the current sub-area.

In one embodiment, the eighth determining submodule is configured to determine, according to the traffic load of each road segment in the current sub-area, a target lane navigation path with the minimum comprehensive load and the shortest path from the multiple lane navigation paths by a heuristic search method.

In an embodiment, the path planning apparatus of the present application further includes a re-execution module, configured to operate after the fourth determination module 150, where the re-execution module is configured to determine whether to start a new planning cycle; and if so, re-executing the determination operation of the candidate sub-area, and re-executing the determination operation of the target area navigation path and the determination operation of the target lane navigation path according to the current position and the travel destination of the vehicle to be planned.

Different from the prior art, the path planning device provided by the application divides a target driving area into sub-areas and respectively calculates the predicted congestion value of each sub-area when planning the vehicle, can divide a vehicle planning problem in a large area into a plurality of vehicle planning problems in a small area, therefore, a large number of vehicle planning scenes can be satisfied, according to the predicted congestion value, the whole congestion sub-area can be avoided when determining the area navigation path of the vehicle among the sub-areas, the congestion risk of the vehicle is reduced, subsequently, in each candidate sub-area where the target area navigation path passes, the vehicle load conditions of all road sections in the candidate sub-area are integrated to obtain the target lane navigation path of a lane in each candidate sub-area, and the vehicle can obtain a better lane navigation path in each candidate sub-area, the method and the system integrate traffic flow loads of all sub-areas and all road sections when planning the vehicles, can achieve the effects of avoiding the sub-areas with congestion and realizing load balance of the whole traffic network, are also suitable for large-scale vehicle planning scenes, and therefore are high in applicability.

Accordingly, embodiments of the present application also provide an electronic device, as shown in fig. 10, which may include components such as a radio frequency circuit 1001, a memory 1002 including one or more computer readable storage media, an input unit 1003, a display unit 1004, a sensor 1005, an audio circuit 1006, a WiFi module 1007, a processor 1008 including one or more processing cores, and a power supply 1009. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 10 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:

the rf circuit 1001 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, receives downlink information of a base station and then sends the received downlink information to the one or more processors 1008 for processing; in addition, data relating to uplink is transmitted to the base station. The memory 1002 may be used to store software programs and modules, and the processor 1008 executes various functional applications and data processing by operating the software programs and modules stored in the memory 1002. The input unit 1003 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

The display unit 1004 may be used to display information input by or provided to a user as well as various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof.

The electronic device may also include at least one sensor 1005, such as a light sensor, a motion sensor, and other sensors. The audio circuitry 1006 includes speakers, which may provide an audio interface between the user and the electronic device.

WiFi belongs to short-distance wireless transmission technology, and the electronic device can help the user send and receive e-mails, browse web pages, access streaming media and the like through the WiFi module 1007, and provides wireless broadband internet access for the user. Although fig. 10 shows the WiFi module 1007, it is understood that it does not belong to the essential constitution of the electronic device, and may be omitted entirely as needed within the scope of not changing the essence of the application.

The processor 1008 is a control center of the electronic device, connects various parts of the entire mobile phone by various interfaces and lines, and performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 1002 and calling data stored in the memory 1002, thereby integrally monitoring the mobile phone.

The electronic device also includes a power source 1009 (e.g., a battery) for providing power to the various components, which may preferably be logically coupled to the processor 1008 via a power management system, such that the power management system may manage charging, discharging, and power consumption.

Although not shown, the electronic device may further include a camera, a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 1008 in the electronic device loads an executable file corresponding to a process of one or more application programs into the memory 1002 according to the following instructions, and the processor 1008 runs the application programs stored in the memory 1002, so as to implement the following functions:

determining a target driving area according to a travel starting point and a travel end point of a vehicle to be planned, wherein the target driving area comprises a plurality of sub-areas, and the sub-areas comprise a plurality of crossed lanes; determining candidate sub-regions according to the predicted congestion values of all the sub-regions in the target driving region at a preset time point; determining a target area navigation path according to the travel starting point, the travel end point and the candidate sub-area so as to enable the vehicle to be planned to determine the information of the driving area; when a vehicle to be planned drives into a current sub-area, acquiring traffic flow loads of all road sections in the current sub-area; and determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the area navigation path, the current position of the vehicle to be planned and the traffic flow load of each road section in the current sub-area, so that the vehicle to be planned determines the information of the driving lane.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.

To this end, an embodiment of the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to implement the following functions:

The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.

Wherein the computer-readable storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

Since the instructions stored in the computer-readable storage medium can execute the steps in any method provided in the embodiments of the present application, the beneficial effects that can be achieved by any method provided in the embodiments of the present application can be achieved, for details, see the foregoing embodiments, and are not described herein again.

The path planning method, the path planning device, the electronic device, and the storage medium provided in the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understand the technical solution and the core idea of the present application; those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure as defined by the appended claims.

Claims

1. A path planning method is characterized by comprising the following steps:

2. The path planning method according to claim 1, wherein the step of determining candidate sub-regions according to the predicted congestion values of all sub-regions in the target driving region at the preset time point comprises:

acquiring the current traffic flow of each subregion through a roadside sensing unit;

determining the predicted traffic flow of each subarea at a preset time point according to the current traffic flow of each subarea;

determining a predicted congestion value of each sub-area at a preset time point according to the predicted traffic flow;

and determining the sub-region with the predicted congestion value not greater than the preset value as a candidate sub-region.

3. The path planning method according to claim 1, wherein the step of determining the predicted traffic flow of each sub-area at the preset time point according to the current traffic flow of each sub-area comprises:

acquiring experience passing time of each subregion;

determining a preset time point according to the maximum experience passing time or the weighted average experience passing time of all the sub-regions;

and determining the predicted traffic flow of each subarea at the preset time point according to the current traffic flow of each subarea.

4. The path planning method according to claim 1, wherein the step of determining the navigation path of the target area according to the starting point of the trip, the ending point of the trip and the candidate sub-areas comprises:

determining a stroke starting point sub-region where the stroke starting point is located and a stroke end point sub-region where the stroke end point is located according to the stroke starting point and the stroke end point;

and determining a target area navigation path connecting the travel starting point sub-area and the travel end point sub-area according to the travel starting point sub-area, the travel end point sub-area and the candidate sub-area, wherein all sub-areas positioned between the travel starting point sub-area and the travel end point sub-area on the target area navigation path are candidate sub-areas.

5. The path planning method according to claim 4, wherein the step of determining a navigation path of the target area connecting the travel start sub-area and the travel end sub-area comprises:

determining a shortest connecting track connecting the stroke starting point sub-region and the stroke end point sub-region by a heuristic search method, wherein sub-regions positioned between the stroke starting point sub-region and the stroke end point sub-region on the shortest connecting track are candidate sub-regions;

and determining a target area navigation path of the vehicle to be planned according to the shortest connecting track.

6. The path planning method according to claim 1, wherein the step of obtaining traffic loads of road segments in the current sub-area comprises:

acquiring the current traffic flow of each road section in the current sub-area and the predicted traffic flow of all planned vehicles on each road section;

and determining the traffic load of each road section in the current sub-area according to the current traffic flow and the predicted traffic flow.

7. The path planning method according to claim 6, wherein the step of determining the target lane navigation path of the vehicle to be planned in the current sub-area according to the area navigation path, the current position of the vehicle to be planned and the traffic load of each road segment in the current sub-area comprises:

acquiring all candidate lane navigation paths in the current sub-area;

determining a plurality of lane navigation paths of the vehicle to be planned in the current sub-area from all the candidate lane navigation paths according to the target area navigation path and the current position of the vehicle to be planned;

and determining a target lane navigation path of the vehicle to be planned in the current sub-area according to the traffic load of each road section in the current sub-area.

8. The path planning method according to claim 7, wherein the step of determining and generating the target lane navigation path of the vehicle to be planned in the current sub-area according to the traffic load of each road segment in the current sub-area comprises:

and determining a target lane navigation path with the minimum comprehensive load and the shortest path from a plurality of lane navigation paths by a heuristic search method according to the traffic load of each road section in the current sub-area.

9. The path planning method according to claim 1, wherein the step of determining a target lane navigation path of the vehicle to be planned within the current sub-area such that the vehicle to be planned determines driving lane information further comprises:

judging whether a new planning period is started;

and if so, re-executing the determination operation of the candidate sub-area, and re-executing the determination operation of the target area navigation path and the determination operation of the target lane navigation path according to the current position and the travel destination of the vehicle to be planned.

10. A path planning apparatus, characterized in that the path planning apparatus comprises: