Path Planning Method, Device and Electronic Device Based on High-Definition Map

Abstract

The present application provides a path planning method, apparatus and electronic device based on a high-precision map. The method includes: obtaining the current position information and destination information of the vehicle; determining a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre-stored high-precision map, wherein the high-precision map is obtained by parsing map data in the Shapefile format, and the high-precision map has a plurality of road nodes corresponding to roads and parking spaces; determining a global road path from the starting node to the destination node in the high-precision map according to a preset path search algorithm, the global road path including a path formed by at least one section of road; and converting the global road path into a global lane path formed by lanes based on the pre-stored correspondence between roads and lanes, so as to be used as the path for the vehicle to travel.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle driving, and in particular, to a path planning method, device, and electronic device based on a high-precision map. Background Art

[0002] As the brain of intelligent vehicles, intelligent driving is a crucial part of realizing the "intelligentization" of the automotive industry. The mass production and implementation of intelligent driving technology, that is, the reliable driving of intelligent driving vehicles on the road, verify the good development prospects of the intelligent driving field. Intelligent driving has higher requirements for map accuracy. Therefore, it is necessary to rely on high-precision maps for autonomous driving. Among them, high-precision maps record lane-level information of the map coverage area, including lane information and related object information. The formats of high-precision maps include Opendrive, Shapefile, NDS (Navigation Data Standard), etc. Currently, for existing lane-level path planning, path planning is usually based on lane-level paths in high-precision maps. If a road contains multiple lanes, it will greatly increase the data computation volume, thus requiring high computing and processing performance of the vehicle. Summary of the Invention

[0003] In view of this, the purpose of the embodiments of the present application is to provide a path planning method, device, and electronic device based on a high-precision map, which can reduce the computation volume when planning a global lane-level path and improve the problem of high requirements for the computing and processing performance of the vehicle during path planning.

[0004] To achieve the above technical objectives, the technical solutions adopted in the present application are as follows:

[0005] In a first aspect, an embodiment of the present application provides a path planning method based on a high-precision map, and the method includes:

[0006] Obtain the current position information and destination information of the vehicle;

[0007] Determine a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre-stored high-precision map, where the high-precision map is obtained by parsing map data in Shapefile format, and there are multiple road nodes corresponding to roads and parking spaces in the high-precision map, and the starting node and the destination node are any two nodes among the multiple road nodes;

[0008] According to a preset path search algorithm, determine a road global path from the starting node to the destination node in the high-precision map, and the road global path includes a path formed by at least one section of road;

[0009] Based on the pre-stored correspondence between roads and lanes, convert the global road path into a global lane path formed by lanes as the path for the vehicle to travel.

[0010] Combined with the first aspect, in some alternative embodiments, determining the starting node corresponding to the current location information and the destination node corresponding to the destination information on the pre-stored high-precision map includes:

[0011] On the high-precision map, determine the road node closest to the current location information as the starting node, and determine the road node closest to the destination information as the destination node.

[0012] Combined with the first aspect, in some alternative embodiments, before determining the global road path from the starting node to the destination node in the high-precision map according to the preset path search algorithm, the method further includes:

[0013] In the high-precision map, determine the road path from the location where the current location information is located to the starting node as the first path segment;

[0014] Determine the road path from the starting node to the second road node as the second path segment, where in the preset coordinate system of the high-precision map, the second road node is the road node that makes the deviation between the heading of the first path segment and the heading of the second path segment less than 180 degrees and is the closest road node to the starting node;

[0015] Determine the road path from the i-th road node to the i + 1-th road node as the i + 1-th path segment, where in the coordinate system of the high-precision map, the i + 1-th road node is the road node that makes the deviation between the heading of the i-th path segment and the heading of the i + 1-th path segment less than 180 degrees and is the closest road node to the i-th road node, and i takes integers greater than or equal to 2 in sequence until the i + 1-th road node is the destination node, so as to obtain the road network data from the starting node to the destination node.

[0016] Combined with the first aspect, in some alternative embodiments, determining the global road path from the starting node to the destination node in the high-precision map according to the preset path search algorithm includes:

[0017] According to the preset path search algorithm, determine a shortest path from the starting node to the destination node from the road network data as the global road path.

[0018] Combined with the first aspect, in some alternative embodiments, the method further includes:

[0019] When there is a non - straight road segment in the lane global path, and the distance between adjacent road nodes in the non - straight road segment exceeds a preset distance, according to the preset interpolation algorithm, perform interpolation and point - taking on the non - straight road segment to obtain an interpolated lane global path, so that the distance between adjacent road nodes in the interpolated non - straight road segment is less than the preset distance;

[0020] Use the interpolated lane global path as the target path for the vehicle to travel.

[0021] In combination with the first aspect, in some alternative embodiments, the method further includes:

[0022] Control the vehicle to travel along the path and heading corresponding to each lane in the lane global path.

[0023] In combination with the first aspect, in some alternative embodiments, the preset path search algorithm includes the A* algorithm.

[0024] In a second aspect, the present application further provides a path planning device based on a high - precision map. The device includes:

[0025] An acquisition unit for acquiring the current position information and destination information of the vehicle;

[0026] A node determination unit for determining a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre - stored high - precision map. The high - precision map is obtained by parsing map data in the Shapefile format, and there are multiple road nodes corresponding to roads and parking spaces in the high - precision map. The starting node and the destination node are any two nodes among the multiple road nodes;

[0027] A road path determination unit for determining a road global path from the starting node to the destination node from the high - precision map according to a preset path search algorithm. The road global path includes a path formed by at least one road segment;

[0028] A conversion unit for converting the road global path into a lane global path formed by lanes based on the pre - stored correspondence between roads and lanes, as the path for the vehicle to travel.

[0029] In a third aspect, the present application further provides an electronic device. The electronic device includes a processor and a memory coupled to each other. The memory stores a computer program. When the computer program is executed by the processor, the electronic device executes the above - mentioned method.

[0030] Fourthly, the embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program runs on a computer, the computer is enabled to execute the above-mentioned method.

[0031] The invention adopting the above technical solution has the following advantages:

[0032] In the technical solution provided by the present application, the high-precision map is obtained by parsing map data in the Shapefile format, and there are multiple road nodes corresponding to roads and parking spaces in the high-precision map. In this solution, the starting node corresponding to the current location information and the destination node corresponding to the destination information are determined on the pre-stored high-precision map; according to the preset path search algorithm, the global road path from the starting node to the destination node is determined from the high-precision map, and the global road path includes a path formed by at least one section of road; based on the pre-stored correspondence between roads and lanes, the global road path is converted into a global lane path formed by lanes to be used as the path for the vehicle to travel. In this way, path planning based on the high-precision map in the Shapefile format can be realized. In addition, compared with directly calculating the global lane path, in this solution, by first determining the road-level path and then converting the road-level path into the lane-level path, the calculation amount is greatly reduced, and the problem of high requirements for the operation and processing performance of the vehicle during path planning can be improved. Description of the Drawings

[0033] The present application can be further illustrated by the non-limiting embodiments given in the drawings. It should be understood that the following drawings only show some embodiments of the present application, so it should not be regarded as a limitation of the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a schematic flowchart of the path planning method based on the high-precision map provided by the embodiment of the present application.

[0035] Figure 2 It is a visualization schematic diagram of the high-precision map data provided by the embodiment of the present application.

[0036] Figure 3 It is a schematic diagram of the road network after the A-star algorithm raster map is converted provided by the embodiment of the present application.

[0037] Figure 4 For Figure 2 The simulation diagram of the global path planning based on the map shown.

[0038] Figure 5 It is a block diagram of the path planning device based on the high-precision map provided by the embodiment of the present application.

[0039] Icons: 200 - Path planning device; 210 - Acquisition unit; 220 - Node determination unit; 230 - Road path determination unit; 240 - Conversion unit. Detailed implementation manners

[0040] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that in the description of the drawings or the specification, similar or identical parts are denoted by the same reference numerals, and the implementation manners not illustrated or described in the drawings are in the forms known to those of ordinary skill in the art. In the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0041] An embodiment of the present application provides an electronic device, which may include a processing module and a storage module. A computer program is stored in the storage module. When the computer program is executed by the processing module, the electronic device can execute the corresponding steps in the following path planning method based on a high-precision map.

[0042] The electronic device is a hardware device installed or deployed on a vehicle. The electronic device may further include other hardware modules or functional modules. For example, the electronic device may further include a positioning module for positioning the vehicle. The positioning module can collect real-time position data of the vehicle, so that the vehicle can achieve autonomous driving and path planning based on the real-time position data.

[0043] In this embodiment, the vehicle or the car may be, but is not limited to, an electric vehicle or other cars that support autonomous driving.

[0044] Please refer to Figure 1 , the present application also provides a path planning method based on a high-precision map (hereinafter simply referred to as the path planning method), which can be applied to the above-mentioned electronic device, and each step of the method is executed or implemented by the electronic device. Among them, the path planning method may include the following steps:

[0045] Step 110, acquiring the current position information and destination information of the vehicle;

[0046] Step 120, determining a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre-stored high-precision map, where the high-precision map is obtained by parsing map data in the Shapefile format, and the high-precision map has a plurality of road nodes corresponding to roads and parking spaces, and the starting node and the destination node are any two nodes among the plurality of road nodes;

[0047] Step 130: Determine a global road path from the starting node to the destination node in the high-precision map according to a preset path search algorithm. The global road path includes a path formed by at least one section of road.

[0048] Step 140: Based on the pre-stored correspondence between roads and lanes, convert the global road path into a global lane path formed by lanes to serve as the path for the vehicle to travel.

[0049] The following will elaborate on each step of the path planning method in detail as follows:

[0050] In step 110, the electronic device on the vehicle can collect the current position information of the vehicle through the positioning module to serve as the real-time position coordinates of the vehicle. The destination information can be flexibly selected by the vehicle owner user according to the actual situation by triggering the HMI (Human Machine Interface) module. For example, the destination information can be a designated parking space in a parking lot.

[0051] Exemplarily, for the parking lot in a residential community, if it is a private parking space, the vehicle owner user can specify the corresponding parking space in the high-precision map as the target parking space according to the position of their own parking space in the parking garage, and the position information of the target parking space is the destination information.

[0052] If the parking space does not belong to a private parking space and can be flexibly parked by other vehicle owners, the destination information can be the position information of any vacant parking space.

[0053] In step 120, in the map data in Shapefile format, each Object mainly contains three types of files:.shp,.shx,.dbf. In this embodiment, the open-source tool QGIS can be used to visually display the map data, and the python library pyshp can be used to parse and store the map data in Shapefile format. A schematic diagram of the high-precision map obtained after parsing can be as Figure 2 shown. The corresponding road nodes (nodes) are pre-recorded in the map data in Shapefile format. After parsing the map data, in the presented high-precision map, there are multiple road nodes corresponding to roads and parking spaces. The starting node and the destination node can be any two nodes among the multiple road nodes.

[0054] For example, step 120 may include: determining the road node closest to the current position information as the starting node on the high-precision map, and determining the road node closest to the destination information as the destination node.

[0055] Before step 130, if the high-precision map has not been converted into a directed road network data, it is necessary to convert the high-precision map into road network data. For example, before step 130, the method may further include:

[0056] In the high-precision map, determine the road path from the position where the current position information is located to the starting node as the first path segment;

[0057] Determine the road path from the starting node to the second road node as the second path segment, where, in the preset coordinate system of the high-precision map, the second road node is the road node that makes the deviation between the heading of the first path segment and the heading of the second path segment less than 180 degrees and is the closest to the starting node;

[0058] Determine the road path from the i-th road node to the i + 1-th road node as the i + 1-th path segment, where, in the coordinate system of the high-precision map, the i + 1-th road node is the road node that makes the deviation between the heading of the i-th path segment and the heading of the i + 1-th path segment less than 180 degrees and is the closest to the i-th road node, and i takes integers greater than or equal to 2 in sequence until the i + 1-th road node is the destination node, so as to obtain the road network data from the starting node to the destination node.

[0059] It can be understood that the preset coordinate system of the high-precision map can be flexibly determined according to the actual situation and can be used to calculate the deviation angle between the heading of the current road segment and the heading of the next road segment. The deviation angle can be the deviation angle of the current heading along the clockwise direction from the next heading.

[0060] In this embodiment, the electronic device unit can obtain the road (link) near the target parking space. The electronic device can, according to the principle of the shortest distance, select the poi closest to the target parking space (destination) in the high-precision map, and obtain the road (link) closest to the target parking space according to the corresponding relationship between the poi and the road (link) pre-recorded in the high-precision map. In this way, it is beneficial to quickly find the path from the starting node to the road that can reach the closest to the target parking space.

[0061] In this embodiment, the electronic device can select the next node based on the nearest node (referring to the starting node) of the current position of the vehicle. The method for selecting the next node can be as follows: Calculate the course (the first course) from the current position to the first node, and the course (the second course) from the first node to the next node. In the coordinate system of the high-precision map, if the course deviation between the two is less than 180 degrees (for example, the first course is in the clockwise direction and the deviation angle from the second course is less than 180°), then the selected node is taken as the next target node. If the first course is in the clockwise direction and the deviation angle from the second course exceeds 180°, then re-select the next node so that the selected next node satisfies a course deviation less than 180 degrees. In this way, the road network data from the starting node to the destination node can be obtained, which can be as Figure 3 shown.

[0062] In this embodiment, Figure 3 the road network data shown is a directed grid graph, and each road segment has corresponding distance information. For example, in Figure 3 , it includes 8 road nodes, and the numbers on the line connecting two adjacent nodes can be understood as the distance of this section of the path. Node 1 is used as the starting node, and node 8 is used as the destination node (i.e., the end point).

[0063] After converting the high-precision map into the road network data of the grid graph based on the starting node and the destination node, the shortest path from the starting node to the destination node can be quickly determined from the road network data based on the following step 130.

[0064] In step 130, the preset path search algorithm can be but is not limited to the A* algorithm (referred to as the A* algorithm), or other path planning algorithms, which can be used to calculate the shortest path between any two nodes in the road network data, and no specific limitation is made here.

[0065] Step 130 may include: determining a shortest path from the starting node to the destination node from the road network data according to the preset path search algorithm, so as to serve as the global road path.

[0066] It can be understood that after importing the Shapefile map data into the QGIS visualization tool, there are several discrete nodes (nodes) on the roads of the high-precision map displayed on the visualization interface, and the discrete nodes separate the continuous roads into roads (links) of unequal lengths, as Figure 2 shown. Then, the A* algorithm can be used to perform a global search based on the grid graph, which is essentially a directed graph search based on nodes, as Figure 3As shown in the figure. Based on each road section in the grid map, each Link segment can be abstracted into a node corresponding to a directed graph, so as to traverse the nodes and use the A* algorithm to achieve global path planning based on roads. Please refer to Figure 4 , for a simulation diagram of global path planning for the map shown in Figure 2 . The global path shown is the global path based on lanes. Among them, Figure 4 the units of the numbers on the horizontal and vertical axes in the map are both meters, indicating the length and width of the map.

[0067] The implementation process of road global path planning can be as follows: In the road network data, perform path search from the starting node to the destination node to obtain the first target road (link), and traverse all subsequent roads (links) in the road network data until the road (link) closest to the target parking space is searched. Store the information of the searched target road (link), and select the shortest path among all paths as the global path based on the link. This global path is the road global path.

[0068] In step 140, the corresponding relationship between the road and the lane is set flexibly in advance according to the actual situation. It can be understood that the path between two adjacent and connected road nodes is a section of road, and each section of road can be set with a unique number. If a section of road has multiple parallel lanes, each lane can be set with a lane identifier for easy distinction. The unique number of each section of road is associated with the lane identifier of this road.

[0069] It can be understood that in this solution, by first determining the road-level path and then converting the road-level path into a lane-level path, the basic data required for path calculation is only the nodes and sections of 1 road. Among them, the amount of operation required to convert the road-level path into a lane-level path is relatively small. When directly calculating the lane global path based on the existing lane-level map paths, nodes and other data, if a road contains N lanes (N is an integer greater than 1), the basic data required for path calculation is the nodes and sections of each of the N lanes. The basic data is N times that of this solution, and the amount of operation will increase exponentially. Therefore, the method provided in this application is beneficial to reducing the amount of data calculation in the path planning process.

[0070] When performing lane-level path conversion, the current position information of the vehicle includes the lane position where the vehicle is currently located. That is, the current position information includes the lane number of the vehicle on the road. In this way, based on the pre-stored corresponding relationship between the road and the lane, the road global path can be converted into a lane global path formed by lanes and corresponding to the lane where the vehicle is currently located.

[0071] Exemplarily, the electronic device can obtain the corresponding lane information according to the information of each segment (link) of the road global path. Since each road (link) corresponds to one or more lanes, combined with the lane where the vehicle is currently located, the "LANE_NO" attribute of the lane can be judged, and the lane corresponding to the first road (link) can be obtained. When selecting lanes subsequently, the overall trend of the lane global path can be combined to flexibly select the corresponding lanes, so that the planning of the lane-level global path can be realized. Among them, the "LANE_NO" attribute can be understood as the number of the lane.

[0072] As an alternative implementation, the method may further include:

[0073] When there is a non-straight section in the lane global path and the distance between adjacent road nodes in the non-straight section exceeds a preset distance, according to the preset interpolation algorithm, interpolation points are taken for the non-straight section to obtain an interpolated lane global path, so that the distance between adjacent road nodes in the interpolated non-straight section is less than the preset distance;

[0074] Take the interpolated lane global path as the target path for the vehicle to travel.

[0075] It can be understood that the preset distance can be flexibly determined according to the actual situation. For example, the preset distance can be 0.3 meters. During the process of autonomous driving, it is usually necessary to adjust the heading of the vehicle in real time based on the determined lane global path. If the entire lane global path is a straight section, the heading of the vehicle can be maintained unchanged during the process of autonomous driving. If there are curved sections in the lane global path and the distance between adjacent nodes in the curved sections is relatively large, interpolation operations need to be performed to add new nodes in the curved sections and re-plan the heading of the vehicle on each lane based on the new nodes.

[0076] In this embodiment, to achieve a more accurate vehicle control effect, the node spacing in the lane global path can be set to be less than 0.3 m, and each node includes the heading and curvature corresponding to the path point. As known from the QGIS visualization interface (such as Figure 2 shown), the nodes on the lane are uneven. If uniform trajectory points are to be obtained, a preset interpolation algorithm can be used to take interpolation points on the lane global path. In the lane global path, there are two scenarios: straight lanes and curved lanes, and the corresponding curvature and heading values are different. On a straight lane: the curvature is 0, and the heading is the same as that of the first node; on a curved lane: interpolation is performed on the curved lane to add new nodes, the curvature is consistent with the curved path, and after adding new nodes, the heading of any node can be the direction pointing to the next node. Among them, the preset interpolation algorithm can be flexibly determined according to the actual situation and is not specifically limited here.

[0077] As an optional implementation, the method may further include: controlling the vehicle to travel along the path and heading corresponding to each lane in the lane global road.

[0078] After calculating the lane global path, the vehicle can control the vehicle to drive in the direction of each lane based on the position and heading of each lane in the lane global path, so that the vehicle can accurately drive to the destination along the lane global path. For example, in the application scenario of a parking lot, the above method is helpful for the vehicle to automatically drive to the target parking space.

[0079] Based on the above design, this solution can be based on Shapefile vector map data, through abstract conversion ideas, the link (road) connecting two adjacent nodes (discrete points on the road) on the Shapefile map can be abstracted into nodes and paths corresponding to the raster map, and global path planning can be realized by searching for the link, and finally, according to the attribute relationship between the link (road) and the lane (lane), the global path based on the lane (lane) is obtained. The present invention uses the open source visualization tool QGIS, combined with the python software library pyshp, to extract the basic map elements required for global path planning, such as: link (road), lane (lane), poi (parking spot), node (discrete point on the road), etc., and find the attribute relationship of different elements, so as to obtain the association relationship of different map elements and realize global path planning. The method provided by the present invention is not limited to the use in the automatic valet parking (AVP) system, but can also be extended to a higher level intelligent driving system based on high-precision maps.

[0080] Please refer to Figure 5 , the present application also provides a path planning device based on a high-precision map (hereinafter referred to as a path planning device). The path planning device 200 includes at least one software function module that can be stored in a storage module in the form of software or firmware or solidified in the operating system (OS) of the electronic device. The processing module is used to execute the executable modules stored in the storage module, such as the software function modules and computer programs included in the path planning device 200.

[0081] The path planning device 200 includes an acquisition unit 210, a node determination unit 220, a road path determination unit 230 and a conversion unit 240. The functions of each unit may be as follows:

[0082] The acquisition unit 210 is used to acquire the current location information and destination information of the vehicle;

[0083] A node determination unit 220, configured to determine a starting node corresponding to the current location information and a destination node corresponding to the destination information on a pre-stored high-precision map, where the high-precision map is obtained by parsing map data in Shapefile format, and the high-precision map has a plurality of road nodes corresponding to roads and parking spaces, and the starting node and the destination node are any two nodes among the plurality of road nodes;

[0084] A road path determination unit 230, configured to determine a global road path from the starting node to the destination node in the high-precision map according to a preset path search algorithm, where the global road path includes a path formed by at least one section of road;

[0085] A conversion unit 240, configured to convert the global road path into a global lane path formed by lanes based on a pre-stored correspondence between roads and lanes, so as to be used as the path for the vehicle to travel.

[0086] Optionally, the node determination unit 220 may be configured to: determine, on the high-precision map, the road node closest to the current location information as the starting node, and determine the road node closest to the destination information as the destination node.

[0087] Optionally, the path planning device 200 may further include a road network determination unit. Before determining the global road path from the starting node to the destination node in the high-precision map according to the preset path search algorithm, the road network determination unit is configured to:

[0088] In the high-precision map, determine the road path from the location where the current location information is located to the starting node as the first section of path;

[0089] Determine the road path from the starting node to the second road node as the second section of path, where, in the preset coordinate system of the high-precision map, the second road node is the road node that makes the deviation between the heading of the first section of path and the heading of the second section of path less than 180 degrees and is the closest to the starting node;

[0090] Determine the road path from the i-th road node to the (i + 1)-th road node as the (i + 1)-th section of path, where, in the coordinate system of the high-precision map, the (i + 1)-th road node is the road node that makes the deviation between the heading of the i-th section of path and the heading of the (i + 1)-th section of path less than 180 degrees and is the closest to the i-th road node, and i takes integers greater than or equal to 2 in sequence until the (i + 1)-th road node is the destination node, so as to obtain the road network data from the starting node to the destination node.

[0091] Optionally, the road path determination unit 230 can be used to: determine a shortest path from the starting node to the destination node from the road network data according to a preset path search algorithm, and use it as the global road path.

[0092] Optionally, the path planning device 200 may further include an interpolation unit for:

[0093] When there is a non-straight road segment in the lane global path and the distance between adjacent road nodes in the non-straight road segment exceeds a preset distance, perform interpolation to obtain points on the non-straight road segment according to a preset interpolation algorithm, so as to obtain an interpolated lane global path, such that the distance between adjacent road nodes in the interpolated non-straight road segment is less than the preset distance; use the interpolated lane global path as the target path for the vehicle to travel.

[0094] Optionally, the path planning device 200 may further include a control unit for controlling the vehicle to travel along the path and heading corresponding to each lane in the lane global path.

[0095] In this embodiment, the processing module may be an integrated circuit chip with signal processing capabilities. The above-mentioned processing module may be a general-purpose processor. For example, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.

[0096] The storage module may be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the storage module can be used to store high-precision maps, the correspondence between roads and lanes, etc. Of course, the storage module can also be used to store programs, and the processing module executes the program after receiving an execution instruction.

[0097] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic device can refer to the corresponding processes of the steps in the foregoing methods, and will not be elaborated herein.

[0098] The embodiments of the present application also provide a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer is enabled to execute the path planning method described in the above embodiments.

[0099] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) and includes several instructions for enabling a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0100] In summary, the embodiments of the present application provide a path planning method, device, and electronic device based on a high-precision map. The high-precision map is obtained by parsing map data in the Shapefile format, and there are multiple road nodes corresponding to roads and parking spaces in the high-precision map. This solution determines a starting node corresponding to the current position information and a destination node corresponding to the destination information on the pre-stored high-precision map; according to a preset path search algorithm, a global road path from the starting node to the destination node is determined from the high-precision map, and the global road path includes a path formed by at least one section of road; based on the pre-stored correspondence between roads and lanes, the global road path is converted into a global lane path formed by lanes to be used as the path for the vehicle to travel. In this way, path planning based on a high-precision map in the Shapefile format can be achieved. In addition, compared with directly calculating the global lane path, this solution first determines the road-level path and then converts the road-level path into the lane-level path, greatly reducing the calculation amount and being able to improve the problem of high requirements for the computing and processing performance of the vehicle during path planning.

[0101] In the embodiments provided in the present application, it should be understood that the disclosed devices, systems, and methods can also be implemented in other ways. The device, system, and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of systems, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the part of the module, program segment, or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions. Additionally, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0102] The above description is only for the embodiments of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A path planning method based on a high-precision map, characterized in that, The method includes: Obtaining the current position information and destination information of the vehicle; Determining a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre-stored high-precision map, where the high-precision map is obtained by parsing map data in Shapefile format, and there are multiple road nodes corresponding to roads and parking spaces in the high-precision map, and the starting node and the destination node are any two nodes among the multiple road nodes; Determining a global road path from the starting node to the destination node in the high-precision map according to a preset path search algorithm, where the global road path includes a path formed by at least one section of road; Based on the pre-stored correspondence between roads and lanes, converting the global road path into a global lane path formed by lanes as the path for the vehicle to travel.

2. The method according to claim 1, characterized in that Determining a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre-stored high-precision map includes: Determining the road node closest to the current position information as the starting node and the road node closest to the destination information as the destination node on the high-precision map.

3. The method according to claim 1, wherein Before determining a global road path from the starting node to the destination node in the high-precision map according to a preset path search algorithm, the method further includes: Determining the road path from the position where the current position information is located to the starting node in the high-precision map as the first section of the path; Determining the road path from the starting node to the second road node as the second section of the path, where in the preset coordinate system of the high-precision map, the second road node is the road node closest to the starting node such that the deviation between the heading of the first section of the path and the heading of the second section of the path is less than 180 degrees; Determining the road path from the i-th road node to the i + 1-th road node as the i + 1-th section of the path, where in the coordinate system of the high-precision map, the i + 1-th road node is the road node closest to the i-th road node such that the deviation between the heading of the i-th section of the path and the heading of the i + 1-th section of the path is less than 180 degrees, and i sequentially takes integers greater than or equal to 2 until the i + 1-th road node is the destination node to obtain the road network data from the starting node to the destination node.

4. The method according to claim 3, characterized in that Determining a global road path from the starting node to the destination node in the high-precision map according to a preset path search algorithm includes: Determining a shortest path from the starting node to the destination node from the road network data according to a preset path search algorithm as the global road path.

5. The method according to claim 1, wherein The method further includes: When there is a non-straight section in the global lane path and the distance between adjacent road nodes in the non-straight section exceeds a preset distance, performing interpolation to obtain points on the non-straight section according to a preset interpolation algorithm to obtain an interpolated global lane path, so that the distance between adjacent road nodes in the interpolated non-straight section is less than the preset distance. Use the interpolated lane global path as the target path for the vehicle to travel along.

6. The method according to claim 1, characterized in that, The method further includes: Controlling the vehicle to travel along the path and heading corresponding to each lane in the lane global path.

7. The method according to any one of claims 1-6, characterized in that, The preset path search algorithm includes the A* algorithm.

8. A path planning device based on a high-precision map, characterized in that, The device includes: An acquisition unit, configured to acquire the current position information and destination information of the vehicle; A node determination unit, configured to determine a starting node corresponding to the current position information and a destination node corresponding to the destination information on a pre-stored high-precision map, where the high-precision map is obtained by parsing map data in the Shapefile format, and the high-precision map has a plurality of road nodes corresponding to roads and parking spaces, and the starting node and the destination node are any two nodes among the plurality of road nodes; A road path determination unit, configured to determine a road global path from the starting node to the destination node in the high-precision map according to a preset path search algorithm, where the road global path includes a path formed by at least one section of road; A conversion unit, configured to convert the road global path into a lane global path formed by lanes based on the pre-stored correspondence between roads and lanes, so as to be used as the path for the vehicle to travel.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory coupled to each other. The memory stores a computer program. When the computer program is executed by the processor, the electronic device executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the computer program runs on a computer, the computer executes the method according to any one of claims 1-7.

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