CN116358585A - Path planning navigation method, device and server applied to parking lot - Google Patents
Path planning navigation method, device and server applied to parking lot Download PDFInfo
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- CN116358585A CN116358585A CN202310325509.2A CN202310325509A CN116358585A CN 116358585 A CN116358585 A CN 116358585A CN 202310325509 A CN202310325509 A CN 202310325509A CN 116358585 A CN116358585 A CN 116358585A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
Abstract
The disclosure provides a path planning navigation method, a device and a server applied to a parking lot, and relates to a path planning technology, comprising: acquiring all road sections in a parking lot where a vehicle is currently located, and screening out a plurality of target road sections for vehicles to pass through and topological relations among the target road sections; taking the target road sections as nodes in the directed graph, determining the arrow directions in the directed graph according to the topological relation among the target road sections, and generating the directed graph; determining a longest path contained in the directed graph; and determining the longest path as a target path so that the vehicle can navigate and park according to the target path. In the path planning navigation method, the path planning navigation device and the path planning navigation server applied to the parking lot, the map of the parking lot is converted into the directed graph by utilizing the concept of graph theory, and the longest path in the directed graph is determined, wherein the longest path is the target path. The target path has high path coverage rate and less repeated road sections, and is suitable for a parking lot scene.
Description
Technical Field
The disclosure relates to a path planning technology, and in particular relates to a path planning navigation method, a device and a server applied to a parking lot.
Background
Currently, path planning for an autonomous vehicle generally refers to planning a reasonable path from a starting location to a destination. For a parking lot scene, it is often impossible to accurately know where there is an empty parking space (i.e. no definite destination), so how to make a path planning for the parking lot scene, so as to facilitate the vehicle to find the empty parking space in the process of cruising in the parking lot, and the problem to be solved is needed.
In the prior art, path planning of an automatic driving vehicle requires defining a start position and a destination position, and then finding an optimal (short distance, short time) path.
However, the above method is suitable only for scenes with a clear destination, and is not suitable for parking scenes.
Disclosure of Invention
The disclosure provides a path planning navigation method, a path planning navigation device and a path planning navigation server applied to a parking lot, so as to solve the problem that a path planning mode in the prior art is not applicable to a parking lot scene.
According to a first aspect of the present disclosure, there is provided a path planning navigation method applied to a parking lot, including:
acquiring all road sections in a parking lot where a vehicle is currently located, and screening out a plurality of target road sections for the vehicle to pass through and topological relations among the target road sections;
taking the target road sections as nodes in the directed graph, and determining the arrow direction in the directed graph according to the topological relation among the target road sections to generate the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node;
determining a longest path contained in the directed graph; and determining the longest path as a target path so that the vehicle performs navigation parking according to the target path.
According to a second aspect of the present disclosure, there is provided a path planning navigation apparatus applied to a parking lot, comprising:
the vehicle-mounted parking system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring all road sections in a parking lot where a vehicle is currently located, screening out a plurality of target road sections for the vehicle to pass through and topological relations among the target road sections;
the directed graph generating unit is used for taking the target road segments as nodes in the directed graph, determining the arrow direction in the directed graph according to the topological relation among the target road segments and generating the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node;
a determining unit configured to determine a longest path included in the directed graph; and determining the longest path as a target path so that the vehicle performs navigation parking according to the target path.
According to a third aspect of the present disclosure, there is provided a server comprising a memory and a processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory is used for storing a computer program;
the processor is configured to read the computer program stored in the memory, and execute the path planning navigation method applied to the parking lot according to the first aspect according to the computer program in the memory.
According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement a path planning navigation method as described in the first aspect applied to a parking lot.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a path planning navigation method as described in the first aspect for use in a parking lot.
The path planning navigation method, device and server applied to the parking lot, provided by the disclosure, comprise the following steps: acquiring all road sections in a parking lot where a vehicle is currently located, and screening out a plurality of target road sections for vehicles to pass through and topological relations among the target road sections; taking the target road sections as nodes in the directed graph, determining the arrow directions in the directed graph according to the topological relation among the target road sections, and generating the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node; determining a longest path contained in the directed graph; and determining the longest path as a target path so that the vehicle can navigate and park according to the target path. In the path planning navigation method, the path planning navigation device and the path planning navigation server applied to the parking lot, the map of the parking lot is converted into the directed graph by utilizing the concept of graph theory, and the longest path in the directed graph is determined, wherein the longest path is the target path. The target path obtained by the scheme has high path coverage rate for the parking lot map, so that more selectable parking spaces can be provided for users correspondingly; and repeated road sections are few, so that the efficiency of searching the parking spaces by the user can be improved to a certain extent, and the method is suitable for a parking lot scene.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present disclosure, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart illustrating a path planning navigation method applied to a parking lot according to an exemplary embodiment of the present disclosure;
fig. 2 is a flow chart illustrating a path planning navigation method applied to a parking lot according to another exemplary embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating a process of determining strong connected components contained in a directed graph according to an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating a process of determining strong connected components contained in a directed graph according to another exemplary embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating a process of determining strong connected components contained in a directed graph according to yet another exemplary embodiment of the present disclosure;
FIG. 6 is a schematic diagram illustrating a process of determining strong connected components contained in a directed graph according to yet another exemplary embodiment of the present disclosure;
FIG. 7 is a schematic diagram of a directed acyclic graph according to an exemplary embodiment of the disclosure;
FIG. 8 is a schematic diagram of determining the longest path contained in a directed acyclic graph, according to an example embodiment of the disclosure;
fig. 9 is a block diagram of a path planning navigation apparatus applied to a parking lot according to an exemplary embodiment of the present disclosure;
fig. 10 is a block diagram of a server shown in an exemplary embodiment of the present disclosure.
Detailed Description
Currently, path planning for an autonomous vehicle generally refers to planning a reasonable path from a starting location to a destination. For a parking lot scene, it is often impossible to accurately know where there is an empty parking space (i.e. no definite destination), so how to make a path planning for the parking lot scene, so as to facilitate the vehicle to find the empty parking space in the process of cruising in the parking lot, and the problem to be solved is needed.
In the prior art, path planning of an automatic driving vehicle requires defining a start position and a destination position, and then finding an optimal (short distance, short time) path. The path planning of the outdoor local area, such as the scenes of outdoor sweeper, agro-farming vehicle, outdoor sweeping robot and the like, requires high path coverage rate.
However, the conventional route planning method for an automatically driven vehicle is suitable for a scene with a clear destination, and is not suitable for a parking lot scene. The outdoor local area path planning, such as outdoor sweeper, agro-vehicle, outdoor sweeping robot, etc., is different from the parking lot scene, for example, some parking lots have only one entrance without exit path, and the vehicle is inconvenient to turn around in situ, so the parking lot scene may not cover the paths. Therefore, the path coverage rate in the parking lot scene is not as high as that of the scenes such as an outdoor sweeper, an agricultural vehicle, an outdoor sweeping robot and the like.
In order to solve the above technical problems, in the solution provided by the present disclosure, a map of a parking lot may be converted into a directed graph by using a concept of graph theory, and then a longest path in the directed graph is determined, where the longest path is a target path. The target path obtained by the scheme has high path coverage rate for the parking lot map, so that more selectable parking spaces can be provided for users correspondingly; and repeated road sections are few, so that the efficiency of searching the parking spaces by the user can be improved to a certain extent, and the method is suitable for a parking lot scene.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) related to the present disclosure are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and be provided with corresponding operation entries for the user to select authorization or rejection.
The following describes the technical solutions of the present disclosure and how the technical solutions of the present disclosure solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present disclosure will be described below with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a path planning navigation method applied to a parking lot according to an exemplary embodiment of the present disclosure.
As shown in fig. 1, the route planning navigation method applied to a parking lot provided in this embodiment includes:
The execution subject of the method provided by the present disclosure may be a server.
Specifically, all road segments in the parking lot where the vehicle is currently located can be obtained from the map of the parking lot. The map of the parking lot may be stored in the server in advance.
Specifically, a plurality of target road sections available for vehicles to pass can be screened from all the obtained road sections. For example, the walking road section and the dead-same road section may be identified in all the obtained road sections, and the identified walking road section and the dead-same road section may be removed to obtain the target map. The target map comprises a plurality of target road sections and topological relations among the target road sections.
Wherein, the link refers to the minimum unit of the road on the map.
The walking road section refers to a road section marked on the map and capable of being walked only by people and not capable of being walked by vehicles.
The dead-end road section refers to a road section which can only enter and cannot go out, namely a road section with only one inlet and no outlet.
102, taking a target road section as a node in the directed graph, and determining the arrow direction in the directed graph according to the topological relation among the target road sections to generate the directed graph; the road section where the vehicle is currently located is taken as a starting road section, and a node in the directed graph corresponding to the starting road section is taken as a starting node.
Wherein the directed graph is a directional graph comprising a vertex and a set of directed edges, each directed edge being connected to a pair of ordered vertices.
Specifically, the target road segments can be used as nodes in the directed graph, edges connecting the nodes in the directed graph and the arrow directions (i.e., the directions of the edges) of the edges can be determined according to the topological relation among the target road segments, and the directed graph can be further generated.
Specifically, a road section where the vehicle is currently located may be taken as a starting road section, and a node in the directed graph corresponding to the starting road section may be taken as a starting node.
Specifically, the longest path included in the directed graph may be determined in a preset manner. The longest path may then be determined as the target path. The vehicle may navigate the park according to the target path.
Specifically, the target path may be sent to a map application for display by the map application.
Specifically, the autonomous vehicle can use the target path to cruise in the parking lot, and can combine with the autonomous function to complete automatic parking. This process may be either driver control or automatic parking.
(1) If the situation that the driver controls the vehicle is the case, the driver can select the idle parking spaces on two sides of the road at any time in the cruising process to park. If the automatic parking function is selected at this time, the vehicle can be automatically parked in the appointed parking space. If the driver does not select a parking space all the time, the driver is prompted to have reached the cruising end when the vehicle is driving to the end of the route.
(2) If the driver does not control the vehicle, the automatic parking space selecting function can be selected, and the automatic parking is performed once the idle parking space is found in the cruising process. If the automatic parking space selecting function is not selected, the driver is required to appoint the parked parking space in the cruising process, and then automatic parking is carried out. If the automatic parking space selection is not selected, and the driver does not always specify the parking space in the cruising process, when the vehicle runs to the route end point, the driver is prompted to reach the cruising end point, and the vehicle is automatically parked.
The path planning navigation method applied to the parking lot comprises the following steps: acquiring all road sections in a parking lot where a vehicle is currently located, and screening out a plurality of target road sections for vehicles to pass through and topological relations among the target road sections; taking the target road sections as nodes in the directed graph, determining the arrow directions in the directed graph according to the topological relation among the target road sections, and generating the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node; determining a longest path contained in the directed graph; and determining the longest path as a target path so that the vehicle can navigate and park according to the target path. According to the scheme, the map of the parking lot is converted into the directed graph by utilizing the concept of graph theory, and the longest path in the directed graph is determined, wherein the longest path is the target path. The target path obtained by the scheme has high path coverage rate for the parking lot map, so that more selectable parking spaces can be provided for users correspondingly; and repeated road sections are few, so that the efficiency of searching the parking spaces by the user can be improved to a certain extent, and the method is suitable for a parking lot scene.
Fig. 2 is a flowchart illustrating a path planning navigation method applied to a parking lot according to another exemplary embodiment of the present disclosure.
As shown in fig. 2, the route planning navigation method applied to the parking lot provided in the present embodiment includes:
Specifically, the principle and implementation of step 201 are similar to those of step 101, and will not be described again.
Specifically, after step 202, step 203 may be performed, and step 205 may also be performed.
Specifically, the principle and implementation of step 202 are similar to those of step 102, and will not be described again.
In step 203, if the directed graph is determined to be a non-strong connected graph, the strong connected components contained in the directed graph are determined according to a preset manner.
In the directed graph G, if there is at least one path between two vertices, the two vertices are said to be strongly connected (strongly connected). If every two vertices of the directed graph G are strongly connected, G is said to be a strongly connected graph. If the directed graph G is not a strong connectivity graph, G is a non-strong connectivity graph. The extremely strong connected subgraph of the non-strong connected graph directed graph is called the strong connected component (strongly connected components).
Specifically, if the directed graph is determined to be a non-strong connected graph, all strong connected components contained in the directed graph may be determined according to a preset manner.
204, generating a directed acyclic graph according to each strong connected component, and determining the longest path contained in the directed acyclic graph by using a preset mode; and determining the longest path as a target path so that the vehicle can navigate and park according to the target path.
Specifically, each strong connected component can be taken as a whole, and then the strong connected components are connected according to the connection relation among the strong connected components, so that a directed acyclic graph is generated.
Wherein, the directed acyclic graph refers to a loop-free directed graph.
Specifically, all paths included in the directed acyclic graph may be searched for and a longest path may be selected from the paths by using a preset manner, and the longest path may be determined as a target path. Further, the vehicle can perform navigation parking according to the target path.
In one implementation, the strongly connected components are used as nodes in the directed acyclic graph, and the direction of the arrow in the directed acyclic graph is determined according to the direction of the arrow between the strongly connected components, so as to generate the directed acyclic graph.
Specifically, if the directed graph is determined to be a non-strong connected graph, all strong connected components included in the directed graph may be determined according to a preset manner.
The preset mode may be a Tarjan algorithm. The Tarjan algorithm is an algorithm for solving strong connected components in a directed graph, which is invented by Robert tower upward (Robert Tarjan).
Specifically, the Tarjan algorithm is an algorithm based on depth-first search of the graph, and each strong connected component can be used as a subtree in the search tree. During searching, unprocessed nodes in the current search tree are added into a stack, and whether the node from the top of the stack to the stack is a strong communication component can be judged during backtracking.
Defining DFN (u) as the sequence number (time stamp) searched by node u, and the sub-tree of Low (u) as u or u can trace back to the sequence number of the node in the earliest stack. From the definition, it follows that when DFN (u) =low (u), all nodes on the search subtree rooted at u are one strongly connected component.
In the directed graph shown in fig. 3, let node 1 be the starting node, start depth-first exploration from node 1, add the traversed nodes to the stack, and have 1,3,5,6 in the stack. And DFN [6] =low [6] =4, then the strongly connected component {6} is found.
Next, returning to node 5, DFN [5] =low [5] =3, as shown in fig. 4, so {5} is a strongly connected component.
Then, returning to node 3, as shown in FIG. 5, the search continues to node 4, adding 4 to the stack. Node 4 is found to be the backward edge of node 1, node 1 is also in the stack, so LOW [4] =1. Node 6 has been popped, no longer accesses 6, returns to 3, (3, 4) is a branch edge, so LOW [3] = LOW [4] = 1.
Continuing back to node 1, as shown in fig. 6, node 2 is last accessed. The access edges (2, 4), 4 are also in the stack, so LOW [2] =4. Returning to 1, it is found that DFN [1] =LOW [1], all nodes in the stack are fetched to form a strongly connected component {1,3,4,2}
Finally, the strongly connected components are obtained as subgraphs {1,2,3,4}, {5}, and {6}.
It should be noted that the above graph theory is to illustrate point-to-point relationship in the graph, and in path planning, the goal is to pass through the most link, so in modeling, the link needs to be regarded as the point in the graph theory, and the relationship between the link and the link (i.e. the impassable) needs to be regarded as the arrow direction in the graph theory. Then, the tarjan algorithm is used to find all the strong connected components in the target map of the parking lot.
Each strong connected component can then be regarded as a whole, and each strong connected component is taken as a node in the directed acyclic graph, edges in the directed acyclic graph are determined according to the arrow directions between the strong connected components, and the arrow directions (i.e., the directions of the edges), thereby generating the directed acyclic graph.
Specifically, as shown in fig. 7, the strong connected component in the directed graph is scaled. And synthesizing the points in the same strong connected component into the same new node, wherein the value of the new node is equal to the sum of all node weights in the strong connected component. The length of the target road segment corresponding to the node may be used as the weight of the node. After the pinch point, a new graph (directed acyclic graph) is obtained from the original graph (directed graph).
Determining the sum of the lengths of the target road sections corresponding to the strong communication components as the weight value of the strong communication components; searching a path with the maximum weight value in the directed acyclic graph; the path with the largest weight contains at least one target strong communication component.
Specifically, the sum of the lengths of the target paths corresponding to all the nodes in the strong connected component can be used as the weight of the strong connected component.
Specifically, all paths in the directed acyclic graph may be searched in a preset manner. For example, the longest path may be explored in the new graph starting from the starting link. Depth-first traversal (Depth First Search, DFS) methods may be employed, briefly for each possible branch path going deep enough to go no further, detailed process:
nodes of the depth traversal graph search branches of the tree as deep as possible. When the edge of node v has been found, the search will trace back to the starting node that found the edge of node v. The entire process iterates until all nodes are accessed. The DFS traversal process of the directed acyclic graph as shown in fig. 8 is: a. b, d, e, f, c. Whereas the DFS traversal process of the directed acyclic graph shown in fig. 7 is: {1,2,3,4}, {5}, and {6}.
And then selecting a path with the maximum weight from the paths in the searched directed acyclic graph, wherein the path with the maximum weight comprises at least one target strong communication component.
Determining a first path containing each target node in the target strong communication component according to a preset mode; the first paths are connected to generate a target path.
Specifically, according to the node contained in the found path with the largest weight, if the node is a new point synthesized by the strong communication components, the new node is replaced by the strong communication components, and a target path is generated. As shown in the directed acyclic graph of fig. 7, the paths found with the greatest weights include {1,2,3,4}, {5}, and {6}.
Specifically, the first path including each node in the target strong communication component may be determined by using a preset manner. And then, connecting the first paths according to the topological relation among the strong connected components of each target in the directed acyclic graph, and generating target paths.
In one implementation, a first number of arrows entering the target node is determined, and a second number of arrows exiting the target node is determined.
Specifically, each target strong communication component includes at least one target node. A first number of arrows entering the target node and a second number of arrows exiting the target node corresponding to each target node may be calculated.
And if the first quantity is determined not to be equal to the second quantity, supplementing the arrow corresponding to the target node, enabling the supplemented first quantity to be equal to the second quantity, and updating the target strong communication component.
Specifically, if it is determined that the first number corresponding to the target node is not equal to the second number, the edge corresponding to the target node (i.e., the arrow corresponding to the target node) may be supplemented, so that the first number corresponding to the supplemented target node is equal to the second number, and the strong-connectivity component is updated.
Optionally, if the first number is determined to be greater than the second number, supplementing an arrow exiting from the target node such that the supplemented second number is equal to the first number, and updating the target strong-connectivity component.
Specifically, if the first number of target nodes is determined to be greater than the second number, the arrow exiting from the target node may be supplemented, so that the second number after supplementation is equal to the first number, and the target strong-connectivity component is updated.
If the first quantity is determined to be smaller than the second quantity, supplementing the arrow entering the target node, enabling the first quantity after supplementing to be equal to the second quantity, and updating the target strong communication component.
Specifically, if the first number of the target nodes is determined to be smaller than the second number, the arrow entering the target nodes may be supplemented, so that the first number after supplementation is equal to the second number, and the target strong communication component is updated.
For example, the target strong connected component {1,2,3,4} is included in the directed graph as shown in FIG. 6. Wherein the first number corresponding to the node 1 is 1, and the second number is 2; the first number corresponding to the node 2 and the second number are 1; the first number corresponding to the node 3 and the second number are 1; the first number of nodes 4 corresponds to 2 and the second number 1. Thus, an edge may be added between node 4 and node 1, the arrow direction of the edge pointing from node 4 to node 1, and the target strong-connectivity component {1,2,3,4} updated. The first number of nodes in the updated target strong connectivity component {1,2,3,4} is equal to the second number.
And processing the updated target strong communication component by using an Euler algorithm to acquire a first path containing each target node in the target strong communication component.
Specifically, the updated target strong-connectivity component may be processed by using an euler algorithm, so as to obtain a first path including each target node in the target strong-connectivity component, where the first path traverses all edges in the target strong-connectivity component, and each edge passes only once.
For example, from the target strong-connectivity component {1,2,3,4} in the directed graph as shown in FIG. 6, the updated target strong-connectivity component {1,2,3,4}. The updated strong connectivity component {1,2,3,4} of the target may be processed using an euler algorithm, and the obtained first path may be: 1241241.
in one implementation, the first paths are connected by the shortest distance according to the topological relation between the target road segments, and the target paths are generated.
Specifically, after the first paths corresponding to the strong communication components of the targets are obtained, the shortest distance connecting the first paths can be found according to the topological relation between the target paths corresponding to the strong communication components of the targets, and the first paths are connected to generate the target paths.
Further, the vehicle can perform navigation parking according to the target path.
Step 205, if the directed graph is determined to be a strong communication graph, determining a second path including each node in the strong communication graph according to a preset mode, and determining the second path as a target path; so that the vehicle can navigate and park according to the target path.
Specifically, if the directed graph is determined to be a strong communication graph, a second path including each node in the strong communication graph may be determined according to a preset manner, and the second path may be determined to be a target path. Further, the vehicle can perform navigation parking according to the target path.
Specifically, the principle and implementation of determining the second path are similar to those of determining the first path, and are not repeated.
Fig. 9 is a block diagram of a path planning navigation apparatus applied to a parking lot according to an exemplary embodiment of the present disclosure.
As shown in fig. 9, a path planning navigation apparatus 900 applied to a parking lot provided by the present disclosure includes:
an obtaining unit 910, configured to obtain all road segments in a parking lot where a vehicle is currently located, and screen out a plurality of target road segments available for the vehicle to pass through, and a topological relation between the target road segments;
a directed graph generating unit 920, configured to take the target road segments as nodes in the directed graph, determine an arrow direction in the directed graph according to a topological relation between the target road segments, and generate the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node;
a determining unit 930, configured to determine a longest path included in the directed graph; and determining the longest path as a target path so that the vehicle can navigate and park according to the target path.
A determining unit 930, configured to determine, if the directed graph is determined to be a non-strong connected graph, a strong connected component included in the directed graph according to a preset manner;
and generating a directed acyclic graph according to each strong connected component, and determining the longest path contained in the directed acyclic graph by utilizing a preset mode.
A determining unit 930, configured to specifically determine, using the strong connected components as nodes in the directed acyclic graph, an arrow direction in the directed acyclic graph according to arrow directions between the strong connected components, and generate the directed acyclic graph;
determining the sum of the lengths of the target road sections corresponding to the strong communication components as the weight value of the strong communication components; searching a path with the maximum weight value in the directed acyclic graph; the path with the maximum weight value comprises at least one target strong communication component;
determining a first path containing each target node in the target strong communication component according to a preset mode; the first paths are connected to generate a target path.
A determining unit 930, specifically configured to determine a first number of arrows entering the target node and determine a second number of arrows exiting the target node;
if the first quantity is determined to be not equal to the second quantity, supplementing the arrow corresponding to the target node, enabling the supplemented first quantity to be equal to the second quantity, and updating the target strong communication component;
and processing the updated target strong communication component by using an Euler algorithm to acquire a first path containing each target node in the target strong communication component.
A determining unit 930, specifically configured to supplement the arrow exiting from the target node if it is determined that the first number is greater than the second number, so that the second number after the supplement is equal to the first number, and update the target strong communication component;
if the first quantity is determined to be smaller than the second quantity, supplementing the arrow entering the target node, enabling the first quantity after supplementing to be equal to the second quantity, and updating the target strong communication component.
The determining unit 930 is specifically configured to connect each first path with the shortest distance according to the topological relation between each target road segment, and generate a target path.
The determining unit 930 is further configured to determine, if the directed graph is determined to be a strong communication graph, a second path including each node in the strong communication graph according to a preset manner, and determine the second path as a target path.
Fig. 10 is a block diagram of a server shown in an exemplary embodiment of the present disclosure.
As shown in fig. 10, the server provided in this embodiment includes:
a memory 1001;
a processor 1002; and
a computer program;
wherein a computer program is stored in the memory 1001 and configured to be executed by the processor 1002 to implement any of the path planning navigation methods applied to a parking lot as described above.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program that is executed by a processor to implement any of the path planning navigation methods applied to a parking lot as above.
The present embodiment also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, implements any of the above path planning navigation methods applied to a parking lot.
The embodiment also provides a software development kit, which comprises a map data engine; the map data engine comprises any path planning navigation device applied to the parking lot.
The embodiment also provides a vehicle, which comprises any path planning navigation device applied to the parking lot.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A path planning navigation method applied to a parking lot, comprising:
acquiring all road sections in a parking lot where a vehicle is currently located, and screening out a plurality of target road sections for the vehicle to pass through and topological relations among the target road sections;
taking the target road sections as nodes in the directed graph, and determining the arrow direction in the directed graph according to the topological relation among the target road sections to generate the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node;
determining a longest path contained in the directed graph; and determining the longest path as a target path so that the vehicle performs navigation parking according to the target path.
2. The method of claim 1, wherein the determining the longest path contained in the directed graph comprises:
if the directed graph is determined to be a non-strong communication graph, determining strong communication components contained in the directed graph according to a preset mode;
and generating a directed acyclic graph according to each strong connected component, and determining the longest path contained in the directed acyclic graph by using a preset mode.
3. The method according to claim 2, wherein the generating a directed acyclic graph from each of the strong connected components and determining a longest path included in the directed acyclic graph by a preset manner, and determining the longest path as a target path, comprises:
the strong connected components are used as nodes in the directed acyclic graph, the arrow direction in the directed acyclic graph is determined according to the arrow direction between the strong connected components, and the directed acyclic graph is generated;
determining the sum of the lengths of the target road sections corresponding to the strong communication components as the weight of the strong communication components; searching a path with the maximum weight value in the directed acyclic graph; the path with the maximum weight value comprises at least one target strong communication component;
determining a first path containing each target node in the target strong communication component according to a preset mode; and connecting the first paths to generate a target path.
4. A method according to claim 3, wherein determining the first path including each target node in the target strong communication component according to a predetermined manner comprises:
determining a first number of arrows entering the target node and determining a second number of arrows exiting the target node;
if the first quantity is determined not to be equal to the second quantity, supplementing the arrow corresponding to the target node, enabling the supplemented first quantity to be equal to the second quantity, and updating the target strong communication component;
and processing the updated target strong communication component by using an Euler algorithm to acquire a first path containing each target node in the target strong communication component.
5. The method of claim 4, wherein supplementing the arrow corresponding to the target node if the first number is determined not to be equal to the second number, such that the supplemented first number is equal to the second number, comprises:
if the first number is determined to be greater than the second number, supplementing an arrow going out from the target node so that the supplemented second number is equal to the first number, and updating the target strong communication component;
and if the first quantity is determined to be smaller than the second quantity, supplementing an arrow entering the target node, enabling the supplemented first quantity to be equal to the second quantity, and updating the target strong communication component.
6. A method according to claim 3, wherein said concatenating each of said first paths generates a target path comprising:
and connecting the first paths with the shortest distance according to the topological relation among the target road sections to generate target paths.
7. The method of claim 1, wherein the determining the longest path contained in the directed graph comprises:
if the directed graph is determined to be the strong communication graph, determining a second path comprising all nodes in the strong communication graph according to a preset mode, and determining the second path as a target path.
8. A path planning navigation device applied to a parking lot, characterized by comprising:
the vehicle-mounted parking system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring all road sections in a parking lot where a vehicle is currently located, screening out a plurality of target road sections for the vehicle to pass through and topological relations among the target road sections;
the directed graph generating unit is used for taking the target road segments as nodes in the directed graph, determining the arrow direction in the directed graph according to the topological relation among the target road segments and generating the directed graph; taking a road section where the vehicle is currently located as a starting road section, and taking a node in the directed graph corresponding to the starting road section as a starting node;
a determining unit configured to determine a longest path included in the directed graph; and determining the longest path as a target path so that the vehicle performs navigation parking according to the target path.
9. A software development kit comprising a map data engine; wherein the map data engine comprises the path planning navigation device applied to the parking lot as claimed in claim 8.
10. A vehicle comprising the route planning navigation device according to claim 8 applied to a parking lot.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116644877A (en) * | 2023-07-26 | 2023-08-25 | 广东电网有限责任公司江门供电局 | Distribution network line fire drop point inspection route planning method and related device |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116644877A (en) * | 2023-07-26 | 2023-08-25 | 广东电网有限责任公司江门供电局 | Distribution network line fire drop point inspection route planning method and related device |
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