CN118392202A - Global optimal path planning method, electronic equipment and medium - Google Patents
Global optimal path planning method, electronic equipment and medium Download PDFInfo
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- CN118392202A CN118392202A CN202410429392.7A CN202410429392A CN118392202A CN 118392202 A CN118392202 A CN 118392202A CN 202410429392 A CN202410429392 A CN 202410429392A CN 118392202 A CN118392202 A CN 118392202A
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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
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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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
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
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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
- G01C21/3453—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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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
The invention discloses a global optimal path planning method, electronic equipment and a medium. The method may include: the navigation module receives the task information, and performs road-level global path planning through the map information to obtain a directed road number set; extracting all lane information corresponding to the road numbers in the directional road number set, and constructing a lane-level global path; and extracting a navigation path, and circularly acquiring a subsequent lane until the last planned road is reached or the length meets the requirement of the road lifting length. According to the global static map and the starting point and end point information issued by the business layer, road-level global paths and lane-level global paths are designed with the shortest distance and the number of intersections to be passed as the costs, and a global path with the minimum cost value is planned, so that the requirement of unmanned vehicles on complex environments is met.
Description
Technical Field
The present invention relates to the field of path planning, and in particular, to a global optimal path planning method, electronic device, and medium.
Background
The path planning is to find the optimal path from the starting point to the target point according to a specific optimization strategy (such as minimum cost, shortest planning time, shortest path finding, etc.) on the premise of ensuring that the obstacle can be avoided. In the face of complex and changeable environment maps, the path planning algorithm required by the unmanned vehicle has the capability of coping with complex environment changes, and meanwhile, the time cost is reduced, and a single algorithm is often difficult to be suitable for different environments.
At present, a global optimal path planning method needs to be developed.
The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a global optimal path planning method, electronic equipment and a medium, which can be used for designing a road-level global path and a lane-level global path according to a global static map and starting point and end point information issued by a business layer and with the cost of the shortest distance and the number of intersections, so as to plan a global path with the minimum cost value and meet the requirement of an unmanned vehicle on a complex environment.
In a first aspect, an embodiment of the present disclosure provides a global optimal path planning method, including:
The navigation module receives the task information, and performs road-level global path planning through the map information to obtain a directed road number set;
extracting all lane information corresponding to the road numbers in the directed road number set, and constructing a lane-level global path;
And extracting a navigation path, and circularly acquiring a subsequent lane until the last planned road is reached or the length meets the requirement of the road lifting length.
Preferably, the task information includes start point information and end point information.
Preferably, the road-level global path planning includes:
And taking the road at the starting point as a node, taking the road at the starting point as the starting point, taking the road length and the number of passing intersections as cost values, judging the connection relation of the roads according to the topological relation of lanes in the roads, constructing a directed road topological relation from the starting point to the end point by adopting an Astar algorithm, planning a road-level global path with the minimum cost value, and obtaining the directed road number set.
Preferably, the Astar algorithm is:
f(n)＝g(n)+h(n)
the function f (n) is the current cost of the node n, the function g (n) is the actual cost from the initial node to the current node n, and the function h (n) is a heuristic function which is the estimated cost of the shortest path from the current node n to the target node.
Preferably, constructing the lane-level global path includes:
And constructing a lane-level global path by taking each lane of all lane information as a node and taking the minimum lane change cost as a constraint condition according to the lane topological relation.
Preferably, the lane change cost assignment rule is:
Taking each extracted lane as a node, and if a preamble successor relationship exists between the nodes, setting the cost value as 0; if the nodes have the same-direction parallel relation and are in a broken line lane change, the cost value is 10; if the node type is a turn, the cost value is 2.
Preferably, constructing the lane-level global path includes:
And searching forward by using the final stopping lane as a starting point through a Dijkstra algorithm to search for the current lane, and selecting a recommended lane which can reach the lower searching lane as the current road if adjacent lanes belong to the same road.
Preferably, the Dijkstra algorithm is:
Calculating the total movement cost of each node from the starting point, and establishing a priority queue;
For all nodes to be traversed, placing the nodes into the priority queue and sequencing the nodes according to the cost;
And in the process of algorithm operation, selecting the node with the smallest cost from the priority queue as the next traversal every time until reaching the end point.
In a second aspect, embodiments of the present disclosure further provide an electronic device, including:
A memory storing executable instructions;
And the processor runs the executable instructions in the memory to realize the global optimal path planning method.
In a third aspect, the disclosed embodiments also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the global optimal path planning method.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the present invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
Fig. 1 shows a flow chart of the steps of a global optimal path planning method.
Fig. 2 shows a flow chart of the steps of a global optimal path planning method according to an embodiment of the invention.
Fig. 3 shows a schematic diagram of an example of a lane according to an embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
In order to facilitate understanding of the solution and the effects of the embodiments of the present invention, three specific application examples are given below. It will be understood by those of ordinary skill in the art that the examples are for ease of understanding only and that any particular details thereof are not intended to limit the present invention in any way.
Example 1
Fig. 1 shows a flow chart of steps according to a global optimal path planning method.
As shown in fig. 1, the global optimal path planning method includes: step 101, a navigation module receives task information, and performs road-level global path planning through map information to obtain a directed road number set; 102, extracting all lane information corresponding to the road numbers in the directional road number set, and constructing a lane-level global path; and 103, extracting a navigation path, and circularly acquiring a subsequent lane until the last planned road is reached or the length meets the requirement of the length of the extracted road.
In one example, the task information includes start point information and end point information.
In one example, the road level global path plan includes:
The road is taken as a node, the road with the starting point is taken as the starting point, the road length and the number of passing intersections are taken as cost values, the connection relation of the road is judged according to the topological relation of lanes in the road, an Astar algorithm is adopted to construct a directed road topological relation from the starting point to the end point, a road grade global path with the minimum cost value is planned, and a directed road number set is obtained.
In one example, the Astar algorithm is:
f(n)＝g(n)+h(n)
the function f (n) is the current cost of the node n, the function g (n) is the actual cost from the initial node to the current node n, and the function h (n) is a heuristic function which is the estimated cost of the shortest path from the current node n to the target node.
In one example, constructing the lane-level global path includes:
And constructing a lane-level global path by taking each lane of all lane information as a node and taking the minimum lane change cost as a constraint condition according to the lane topological relation.
In one example, the lane-change cost assignment rule is:
Taking each extracted lane as a node, and if a preamble successor relationship exists between the nodes, setting the cost value as 0; if the nodes have the same-direction parallel relation and are in a broken line lane change, the cost value is 10; if the node type is a turn, the cost value is 2.
In one example, constructing the lane-level global path includes:
And searching forward by using the final stopping lane as a starting point through a Dijkstra algorithm to search for the current lane, and selecting a recommended lane which can reach the lower searching lane as the current road if adjacent lanes belong to the same road.
In one example, the Dijkstra algorithm is:
Calculating the total movement cost of each node from the starting point, and establishing a priority queue;
For all nodes to be traversed, placing the nodes into a priority queue and sequencing the nodes according to the cost;
In the process of algorithm operation, the node with the smallest cost is selected from the priority queue as the next traversal every time until reaching the end point.
Specifically, the equipment needed by the scheme is as follows: high-precision map, combined inertial navigation and automatic driving of the vehicle.
High-precision map: providing road numbers, road boundaries, number of lanes, intersection numbers, intersection boundaries, lane numbers, lane line types, lane boundaries, lane types, lane topological relations, traffic light information and the like.
Combined inertial navigation: positioning information of the autonomous vehicle is provided.
Task information: providing start point and end point information.
Fig. 2 shows a flow chart of the steps of a global optimal path planning method according to an embodiment of the invention.
As shown in fig. 2, the navigation module receives the start point and the end point provided by the task information and the high-precision map information, and performs road-level global path planning. The road is taken as a node, the road with the starting point is taken as the starting point, the road length and the number of passing intersections are taken as cost values, the connection relation of the road is judged according to the topological relation of lanes in the road, an Astar algorithm is adopted to construct a directed road topological relation from the starting point to the end point, a road-level global path with the minimum cost value is planned, and a directed road number set is stored. The shorter the road length, the fewer the number of intersections passed, and the lower the cost value. The road length selection rule is as follows: length of leftmost lane in parallel road. The Astar algorithm is:
f(n)＝g(n)+h(n)
The function f (n) is the current cost of the node n, the function g (n) is the actual cost from the initial node to the current node n, the function h (n) is a heuristic function, and the estimated cost for the shortest path from the current node n to the target node is a predicted value. Furthermore, when the cost function is obtained by adding two function values of h (n), g (n), the two function values must use the same distance measurement unit.
And extracting all lane information corresponding to the road numbers in the directional road number set, taking each lane as a node, and constructing a lane-level global path according to the constraint condition that the lane change cost is minimum according to the lane topology relation.
In order to finally reach the rightmost lane of the terminal road, the vehicle is searched forward by using the last stop lane as a starting point through Dijkstra algorithm when searching, the vehicle is searched to the starting lane (the lane where the vehicle is located), and if adjacent lanes belong to the same road, a recommended lane which can reach the lower search lane is selected as the recommended lane under the current road. The roundabout lanes are special, if the intersection of the entering roundabout and the exiting roundabout is not adjacent according to the traffic rules, the roundabout is merged into the roundabout first after entering the roundabout, so that the lanes close to the left side are selected as recommended lanes if two adjacent planning lanes are checked to be roundabout lanes.
Lane cost value assignment rule: taking each extracted lane as a node, and if a preamble successor relationship exists between the nodes, setting the cost value as 0; if the nodes have the same-direction parallel relation and are changed into a broken line, the cost value is 10 (can be calibrated); if the node type is a turn, the cost value is 2 (calibratable).
In Dijkstra's algorithm, the total movement cost of each node from the start point needs to be calculated. At the same time, a priority queue structure is also required. And placing the nodes to be traversed into a priority queue to sort the nodes according to the cost. In the process of algorithm operation, the node with the minimum cost is selected from the priority queue every time and used as the next traversal. Until the endpoint is reached.
The navigation path is extracted according to a three-lane model, a lane in which the vehicle is currently located and a lane which belongs to a road planning result and is in a certain path length behind the current lane are taken as current navigation lanes in the three-lane model, a lane on the left side of the current lane is taken as a left navigation lane in the three-lane model, and a lane on the right side of the current lane is taken as a right navigation lane in the three-lane model. And circularly acquiring the subsequent lanes until the last planned road is reached or the length meets the road lifting length requirement.
The extracted path information is periodically sent to a decision planning module to be used as an important reference for judging the driving intention of the vehicle and the local path planning.
Fig. 3 shows a schematic diagram of an example of a lane according to an embodiment of the invention.
As shown in fig. 3, if the vehicle arrives at 5B from 4a, the algorithm used according to the present solution calculates the final route as (1_1_2_2_1_2_3_1_2_1_2_4_1_1_1_1_5_1_1_6_1_1_6_1_1_2_7_1_1_ 8_1 _1-8_1 _2_ 8_1 _3-9_1 _2) with 1 intersection, 5 (calibratable) total cost, 5, (3_1_2-4_1_2) as a successor, and 0, so the cost from the vehicle to station a is 0; (4_1_ -2-4_1_ -1) is the same-direction dotted line channel change, and the cost is 10, so the total channel change times in the route are 4 times, and the total channel change cost is 40; (6_1_ -2-7_1_ -1-8_1 _ -1) is a turn, the cost value is 2, so the total turns in the route are 2 times, and the total turn cost is 4; (4_1_ -1-5_1_ -1-6_1_ -1) is u-turn, which is not considered in this scheme. Thus, the total cost of the vehicle from 4 to 5 at A is 59 (5+0+10+40+4).
Example 2
The present disclosure provides an electronic device including: a memory storing executable instructions; and the processor runs executable instructions in the memory to realize the global optimal path planning method.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is for storing non-transitory computer readable instructions. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example 3
Embodiments of the present disclosure provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the global optimum path planning method.
A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the present disclosure described above.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention has been given for the purpose of illustrating the benefits of embodiments of the invention only and is not intended to limit embodiments of the invention to any examples given.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (10)
1. A global optimum path planning method, comprising:
The navigation module receives the task information, and performs road-level global path planning through the map information to obtain a directed road number set;
extracting all lane information corresponding to the road numbers in the directed road number set, and constructing a lane-level global path;
And extracting a navigation path, and circularly acquiring a subsequent lane until the last planned road is reached or the length meets the requirement of the road lifting length.
2. The global optimal path planning method according to claim 1, wherein the task information includes start point information and end point information.
3. The global optimal path planning method according to claim 1, wherein the road-level global path planning comprises:
And taking the road at the starting point as a node, taking the road at the starting point as the starting point, taking the road length and the number of passing intersections as cost values, judging the connection relation of the roads according to the topological relation of lanes in the roads, constructing a directed road topological relation from the starting point to the end point by adopting an Astar algorithm, planning a road-level global path with the minimum cost value, and obtaining the directed road number set.
4. A global optimum path planning method according to claim 3, wherein the Astar algorithm is:
f(n)＝g(n)+h(n)
the function f (n) is the current cost of the node n, the function g (n) is the actual cost from the initial node to the current node n, and the function h (n) is a heuristic function which is the estimated cost of the shortest path from the current node n to the target node.
5. The global optimal path planning method of claim 1, wherein constructing a lane-level global path comprises:
And constructing a lane-level global path by taking each lane of all lane information as a node and taking the minimum lane change cost as a constraint condition according to the lane topological relation.
6. The global optimal path planning method of claim 5, wherein the lane-change cost assignment rule is:
Taking each extracted lane as a node, and if a preamble successor relationship exists between the nodes, setting the cost value as 0; if the nodes have the same-direction parallel relation and are in a broken line lane change, the cost value is 10; if the node type is a turn, the cost value is 2.
7. The global optimal path planning method of claim 5, wherein constructing a lane-level global path comprises:
And searching forward by using the final stopping lane as a starting point through a Dijkstra algorithm to search for the current lane, and selecting a recommended lane which can reach the lower searching lane as the current road if adjacent lanes belong to the same road.
8. The global optimal path planning method according to claim 7, wherein the Dijkstra algorithm is:
Calculating the total movement cost of each node from the starting point, and establishing a priority queue;
For all nodes to be traversed, placing the nodes into the priority queue and sequencing the nodes according to the cost;
And in the process of algorithm operation, selecting the node with the smallest cost from the priority queue as the next traversal every time until reaching the end point.
9. An electronic device, the electronic device comprising:
A memory storing executable instructions;
A processor executing the executable instructions in the memory to implement the global optimum path planning method of any of claims 1-8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the global optimum path planning method according to any one of claims 1-8.
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