Parking space searching method and device and vehicle

Abstract

The invention relates to a parking space searching method and device and a vehicle, and relates to the technical field of vehicles, and the method comprises the steps: building a global map based on the sensing information in a vehicle sensing range corresponding to a vehicle position under the condition that the vehicle position is not in a coverage area of a high-precision map; determining a target exploration point; wherein the target exploration point is an optimal exploration point selected from sensing boundary points in a global map; based on a first preset condition, adopting a fast search random tree algorithm to plan a target planning path from the vehicle position to the target exploration point on the global map; wherein the first preset condition is used for enabling tree nodes sampled by the fast search random tree algorithm to tend to fall into a neighborhood of the target exploration point; and controlling the vehicle to cruise along the target planned path to search for available parking spaces. Therefore, parking space exploration can be efficiently, accurately and intelligently realized under the condition of no high-precision map coverage.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, in particular to the field of vehicle automatic driving technology, and specifically to a parking space search method, device and vehicle. Background Art

[0002] As autonomous driving technology continues to advance into advanced scenarios, valet parking has become a core service for smart cars. By integrating high-precision maps with multi-sensor data, valet parking enables precise parking space search and route planning in known map environments. However, traditional valet parking relies heavily on maps, making it ineffective in unknown map scenarios due to a lack of information about parking space distribution and aisle topology. Therefore, there is a need to explore effective methods for intelligent parking space exploration in unknown map environments. Summary of the Invention

[0003] This application provides a parking space search method, device, and vehicle to at least solve the technical problem in related technologies that it is difficult to achieve intelligent parking space exploration in an unknown map environment. The technical solution of this application is as follows: In a first aspect, the present application provides a parking space search method, comprising: when a vehicle position is not within the coverage area of ​​a high-precision map, constructing a global map based on perception information within the vehicle perception range corresponding to the vehicle position; wherein the perception information includes but is not limited to perception boundary points and obstacles that are not blocked by obstacles; determining a target exploration point; wherein the target exploration point is the best exploration point selected from the perception boundary points in the global map; based on a first preset condition, using a fast search random tree algorithm to plan a target planning path from the vehicle position to the target exploration point on the global map; wherein the first preset condition is used to make the tree nodes sampled by the fast search random tree algorithm tend to fall within the neighborhood of the target exploration point; controlling the vehicle to cruise along the target planning path to search for available parking spaces.

[0004] According to the above technical means, the present application can construct a global map based on the perception information within the vehicle's perception range when the vehicle is in an unknown map environment (not in the area covered by the high-precision map), and select the best target exploration point from it, thereby utilizing the fast search random tree algorithm to plan the target planning path from the vehicle position to the target exploration point according to the first preset condition, and the tree nodes tend to fall within the neighborhood of the target exploration point, thereby avoiding the problem of blind exploration of parking spaces or inability to carry out intelligent exploration due to lack of effective map information in an unknown environment, and realizing parking space exploration efficiently, accurately and intelligently.

[0005] In one possible implementation, if no available parking space is found when the vehicle cruises to the target exploration point, the above steps of constructing, determining, planning, and cruising are re-executed based on the vehicle position after cruising until an available parking space is found.

[0006] According to the above technical means, when the vehicle cruises to the target exploration point and still has not found an available parking space, the present application can re-execute the steps of constructing a global map, determining the target exploration point, planning the target path, and controlling the vehicle cruise based on the vehicle position after the cruise, thereby avoiding the problem of search interruption or falling into local invalid search due to the limited single search range or deviation in the selection of the target exploration point, thereby searching for available parking spaces more comprehensively and efficiently, and improving the integrity and success rate of the parking space search.

[0007] In one possible implementation, a global map is constructed based on perception information within a vehicle perception range corresponding to a vehicle position, including: constructing a local map corresponding to the vehicle perception range based on the perception information; in the absence of a first exploration point, determining the local map as a global map; wherein the first exploration point is an exploration point closest to the vehicle position that the vehicle passes through after parking planning.

[0008] According to the above technical means, the present application can first construct a local map based on the perception information within the vehicle's perception range, thereby quickly integrating effective information in the vehicle's current environment. Then, when there is no first exploration point (i.e., the nearest exploration point of the vehicle's parking path after planning), the local map is directly determined as the global map, reducing unnecessary computing resource consumption and data processing volume, and improving the efficiency and response speed of the parking space search process.

[0009] In one possible implementation, a global map is constructed based on perception information within a vehicle perception range corresponding to a vehicle position, and the method further includes: in the presence of a first exploration point, fusing a local map with a historical global map to obtain a fused map; wherein the historical global map is obtained by integrating a target tree node into a global map established at the first exploration point, and the target tree node is a tree node sampled when a fast search random tree algorithm is used to plan a path from the first exploration point to the vehicle position; based on the target search range, the fused map is modified to obtain a global map, wherein the target search range is defined based on the vehicle position.

[0010] According to the above technical means, this application can, when there is a first exploration point, fuse the local map constructed based on the current perception information with the historical global map, and then modify the fused map based on the target search range defined by the vehicle position, to prevent the map information from being too complicated and distracting the search effort, and obtain a global map including the key search area, thereby effectively improving the efficiency and accuracy of subsequent parking space searches.

[0011] In one possible implementation, based on the target search range, the fused map is modified to obtain a global map, including: determining a gain value for each tree node in the fused map; deleting the first exploration point and tree nodes in the fused map whose gain value is less than a preset gain value and is outside the target search range to obtain a global map; wherein the gain value of each tree node is determined based on the distance between each tree node and other tree nodes in the fused map and / or the number of tree nodes contained in the neighborhood of each tree node.

[0012] According to the above technical means, the present application can modify the fused map by determining the gain value of each tree node based on the distance between the tree node and other tree nodes in the fused map and / or the number of tree nodes in the neighborhood, and deleting the first exploration point and the tree nodes outside the target search range and with a gain value less than the preset value, thereby avoiding the presence of too many low-value and redundant tree nodes in the fused map that interfere with the search process, improving the quality and practicality of the global map, and thereby improving the efficiency and accuracy of parking space search.

[0013] In one possible implementation, determining the target exploration point includes: determining whether a first perception boundary point exists, wherein the first perception boundary point is a boundary point located within the target search range; and determining the target exploration point from the first perception boundary point when it is determined that the first perception boundary point exists.

[0014] According to the above technical means, the present application can first determine whether there is a first perception boundary point within the target search range, avoid blindly performing subsequent complex screening operations in the absence of relevant valid boundary points, and can quickly focus on valuable boundary points within the target search range, and accurately determine the target exploration points therefrom, thereby improving the efficiency of target exploration point determination.

[0015] In one possible implementation, determining a target exploration point from the first perception boundary points includes: determining a perception boundary point with the highest exploration value among the first perception boundary points as the target exploration point; wherein the exploration value of the first perception boundary point is determined based on the distance between the vehicle position and the first perception boundary point, and the angle between the first orientation of the vehicle and the second orientation of the vehicle position pointing to the first perception boundary point.

[0016] According to the above technical means, the present application can determine the exploration value based on the distance between the vehicle position and the first perception boundary point and the angle between the vehicle's first direction and the second direction pointing to the first perception boundary point, and select the point with the highest exploration value as the target exploration point, avoiding the problem of unreasonable vehicle driving path and low exploration efficiency due to arbitrary selection of exploration points, and enabling the vehicle to plan the exploration route in a more efficient manner, giving priority to exploring the areas that are most valuable for searching for parking spaces, thereby improving the success rate and speed of the overall parking space search.

[0017] In one possible implementation, determining the target exploration point further includes: determining the target exploration point from the second perception boundary points when it is determined that the first perception boundary point does not exist; wherein the second perception boundary point is a perception boundary point in the global map that is outside the target search range.

[0018] According to the above technical means, the present application can determine the target exploration point from the second perception boundary point outside the target search range when there is no first perception boundary point within the target search range, thereby increasing the possibility of finding available parking spaces and improving the success rate of parking space search.

[0019] In one possible implementation, a target exploration point is determined from the second perception boundary points, including: determining a connection path with the shortest path length between the vehicle position and each second perception boundary point based on a branch and bound method; wherein the connection path connects the vehicle position and the corresponding second perception boundary point through multiple tree nodes in a global map; and determining the perception boundary point with the shortest connection path length among the second perception boundary points as the target exploration point.

[0020] According to the above technical means, the present application can determine the connection path with the shortest path length between the vehicle position and each second perception boundary point based on the branch and bound method, and use the corresponding second perception boundary point as the target exploration point, thereby avoiding the waste of time and resources caused by blindly exploring areas outside the target search range. It can screen out the most worthy points to explore from the perception boundary points outside the range in an efficient and accurate manner, thereby improving the efficiency of parking space search.

[0021] In one possible implementation, based on a first preset condition, a fast search random tree algorithm is used to plan a target planning path from the vehicle position to the target exploration point on a global map, including: based on the first preset condition, a fast search random tree algorithm is used to generate multiple planning branches from the vehicle position to the target exploration point; the planning branch with the highest exploration value among the multiple planning branches is determined as the target planning path; wherein the exploration value of the planning branch is related to at least one of the following: the forward distance of the edge between two adjacent tree nodes in the planning branch relative to the direction of the vehicle; the angle between different edges in the planning branch; the distance between the tree node in the planning branch and the vehicle position; the similarity between the planning branch and the planned path from the first exploration point to the vehicle position; the number of unknown voxels in the neighborhood of the tree node in the planning branch.

[0022] According to the above technical means, the present application can generate multiple planning branches by using a fast search random tree algorithm based on the first preset condition, and select the branch with the highest value as the target planning path based on the exploration value related to the forward distance, edge angle, tree node distance, path similarity, number of unknown voxels, etc., to avoid the situation where a single path planning may fall into a local optimum or not meet the actual exploration needs, and can plan a path that is more in line with the vehicle driving characteristics, more efficient and more meaningful for exploration.

[0023] In one possible implementation, the first preset condition includes: when the target value is less than or equal to a preset numerical threshold, randomly generating a tree node within the neighborhood of the target exploration point; and when the target value is greater than the preset numerical threshold, randomly generating a tree node outside the neighborhood of the target exploration point; wherein the target value is a randomly generated value within a preset numerical range.

[0024] According to the above technical means, the present application can flexibly decide to randomly generate tree nodes within or outside the neighborhood of the target exploration point by comparing the target value with the preset numerical threshold, thereby avoiding the excessive concentration or dispersion of tree nodes generated by the fast search random tree algorithm during path planning, thereby effectively finding a high-quality path from the vehicle position to the target exploration point.

[0025] In a second aspect, the present application provides a parking space search device, comprising: a construction unit, a determination unit, a planning unit and a control unit; the construction unit is used to construct a global map based on the perception information within the vehicle perception range corresponding to the vehicle position when the vehicle position is not in the coverage area of ​​the high-precision map; wherein the perception information includes but is not limited to perception boundary points and obstacles that are not blocked by obstacles; the determination unit is used to determine the target exploration point; wherein the target exploration point is the best exploration point selected from the perception boundary points in the global map; the planning unit is used to plan a target planning path from the vehicle position to the target exploration point on the global map based on a first preset condition and using a fast search random tree algorithm; wherein the first preset condition is used to make the tree nodes sampled by the fast search random tree algorithm tend to fall within the neighborhood of the target exploration point; the control unit is used to control the vehicle to cruise along the target planning path to search for available parking spaces.

[0026] In one possible implementation, the construction unit, determination unit, planning unit and control unit are respectively used to re-execute the above-mentioned construction, determination, planning and cruising steps based on the vehicle position after cruising when no available parking space is found when the vehicle cruises to the target exploration point, until an available parking space is found.

[0027] In one possible implementation, the construction unit is specifically used to: construct a local map corresponding to the vehicle's perception range based on the perception information; in the absence of a first exploration point, determine the local map as a global map; wherein the first exploration point is the exploration point closest to the vehicle's position that the vehicle passes through after parking planning.

[0028] In one possible implementation, a construction unit is specifically used to: in the presence of a first exploration point, fuse the local map and the historical global map to obtain a fused map; wherein the historical global map is obtained by integrating a target tree node into the global map established at the first exploration point, and the target tree node is a tree node sampled when planning a path from the first exploration point to the vehicle position using a fast search random tree algorithm; based on a target search range, modify the fused map to obtain a global map, wherein the target search range is defined based on the vehicle position.

[0029] In one possible implementation, a construction unit is specifically used to: determine a gain value of each tree node in a fused map; delete a first exploration point and tree nodes in the fused map whose gain value is less than a preset gain value and is outside a target search range, to obtain a global map; wherein the gain value of each tree node is determined based on the distance between each tree node and other tree nodes in the fused map and / or the number of tree nodes contained in the neighborhood of each tree node.

[0030] In one possible implementation, the determination unit is specifically used to: determine whether there is a first perception boundary point, wherein the first perception boundary point is a boundary point located within the target search range; and when it is determined that the first perception boundary point exists, determine the target exploration point from the first perception boundary point.

[0031] In one possible implementation, the determination unit is specifically used to: determine the perception boundary point with the highest exploration value among the first perception boundary points as the target exploration point; wherein the exploration value of the first perception boundary point is determined based on the distance between the vehicle position and the first perception boundary point, and the angle between the first orientation of the vehicle and the second orientation of the vehicle position pointing to the first perception boundary point.

[0032] In one possible implementation, the determination unit is specifically configured to: determine a target exploration point from a second perception boundary point when it is determined that the first perception boundary point does not exist; wherein the second perception boundary point is a perception boundary point in the global map that is outside the target search range.

[0033] In one possible implementation, the determination unit is specifically used to: determine, based on a branch and bound method, a connection path with the shortest path length between the vehicle position and each second perception boundary point; wherein the connection path connects the vehicle position and the corresponding second perception boundary point through multiple tree nodes in a global map; and determine the perception boundary point with the shortest connection path length among the second perception boundary points as the target exploration point.

[0034] In one possible implementation, the planning unit is specifically used to: based on a first preset condition, use a fast search random tree algorithm to generate multiple planning branches from the vehicle position to the target exploration point; determine the planning branch with the highest exploration value among the multiple planning branches as the target planning path; wherein the exploration value of the planning branch is related to at least one of the following: the forward distance of the edge between two adjacent tree nodes in the planning branch relative to the direction of the vehicle; the angle between different edges in the planning branch; the distance between the tree node in the planning branch and the vehicle position; the similarity between the planning branch and the planned path from the first exploration point to the vehicle position; the number of unknown voxels in the neighborhood of the tree node in the planning branch.

[0035] In a third aspect, the present application provides a vehicle, comprising the parking space search device in the second aspect.

[0036] In a fourth aspect, the present application provides an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation method thereof.

[0037] In a fifth aspect, the present application provides a computer-readable storage medium, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0038] In a sixth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation method thereof.

[0039] It should be noted that the technical effects brought about by any implementation method in the second to sixth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0040] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0042] Figure 1 is a schematic structural diagram of a parking space search system according to an exemplary embodiment; Figure 2 is a flow chart showing a method for searching a parking space according to an exemplary embodiment; Figure 3 is a schematic diagram of a parking space search process according to an exemplary embodiment; Figure 4 This is a schematic diagram of a fast search random tree construction process according to an exemplary embodiment; Figure 5 is a schematic diagram showing a result of a rapid search for a random tree construction according to an exemplary embodiment; Figure 6 is a schematic diagram showing a process of determining a target planning path according to an exemplary embodiment; Figure 7 is a block diagram of a parking space search device according to an exemplary embodiment; Figure 8 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] In order to enable ordinary people in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0044] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0045] In the embodiments of this application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0046] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0047] The parking space search method provided in the embodiments of the present application can be applied to a vehicle. A vehicle may also be referred to as a transportation tool (vehicle), mobile carrier, electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), fuel cell vehicle (FCV), autonomous vehicle, intelligent and connected vehicle (ICV), driverless vehicle, etc.

[0048] In the embodiments of this application, the vehicle may be a sedan, a sport utility vehicle (SUV), a truck, an electric vehicle, a motorcycle, a tricycle, a special vehicle (such as an ambulance, fire truck, or police car), a driverless taxi, an intelligent connected bus, an autonomous logistics vehicle, an electric truck, etc. Furthermore, this method is also applicable to various specialized vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, and port vehicles. This application does not impose any specific limitations on this.

[0049] like Figure 1 As shown, the parking space search system provided by the present application includes a parking space search device 101 and a data acquisition device 102 .

[0050] Optionally, Figure 1 A communication connection can be established between the parking space search device 101 and the data collection device 102.

[0051] In practical applications, the parking space search device 101 may be communicatively connected to one or more data collection devices 102 .

[0052] For ease of understanding, this application takes the communication connection between a parking space search device 101 and a data acquisition device 102 as an example for explanation.

[0053] Optionally, Figure 1 The parking space search device 101 and the data acquisition device 102 may be functional modules integrated into the same device, or may be devices independently provided. This application does not impose any restrictions on this.

[0054] It's easy to understand that when parking space search device 101 and data acquisition device 102 are functional modules integrated into the same device, the communication between them is that between internal modules. In this case, the communication process between them is the same as the communication process when parking space search device 101 and data acquisition device 102 are independently configured.

[0055] For ease of understanding, this application is mainly described by taking the example of the parking space search device 101 and the data collection device 102 being independently configured.

[0056] Figure 1 The data collection device 102 can collect perception information within the vehicle perception range corresponding to the vehicle position and send the perception information to the parking space search device 101. The parking space search device 101 can construct a global map based on the perception information and determine a target exploration point. Then, based on the first preset condition, a fast search random tree algorithm is used to plan a target planning path from the vehicle position to the target exploration point on the global map, so as to further control the vehicle to cruise along the target planning path to search for an available parking space.

[0057] Optionally, Figure 1 The parking space search device 101 may be a terminal, a server, or other types of electronic devices. Figure 1 The figure shown in the figure is only an example of the device form of the parking space search device 101 and does not constitute a limitation thereto.

[0058] When parking space search device 101 is a terminal, the terminal can be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing device connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN). The terminal can be a mobile terminal, such as a computer with a mobile terminal, or a mobile device built into a group fault detection system that exchanges voice and / or data with a radio access network, such as a mobile phone, tablet computer, laptop computer, netbook, or personal digital assistant (PDA). This application does not impose any restrictions on this.

[0059] When the parking space search device 101 is a server, the server can be a single server, or a server cluster composed of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. This application does not impose any restrictions on this.

[0060] It should be noted that the structure illustrated in the embodiments of this application does not limit the group fault detection system. It may include more or fewer components than shown, or some components may be combined or separated, or arranged differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0061] For ease of understanding, the parking space search method provided in this application is described in detail below with reference to the accompanying drawings.

[0062] Figure 2 FIG. 1 is a flow chart showing a method for searching a parking space according to an exemplary embodiment. Figure 2 As shown, the parking space search method includes the following steps: S201-S204.

[0063] S201. When the vehicle position is not within the coverage area of ​​the high-precision map, a global map is constructed based on the perception information within the vehicle perception range corresponding to the vehicle position.

[0064] The perception information may include but is not limited to perception boundary points that are not blocked by obstacles and obstacles.

[0065] Optionally, the sensory information can be set based on actual needs. For example, the sensory information can include walls, curbs, cones, etc., or ground markings and lane markings. This application does not impose specific restrictions on this.

[0066] In one possible implementation, when the parking space search device does not store a high-precision map corresponding to the current environment, a request message can be sent to the user through the in-vehicle interactive interface or the user terminal to ask whether to start map-free valet parking, and the corresponding operation can be performed after the user confirms.

[0067] For example, the parking space search device may notify the user through the in-vehicle interactive interface or user terminal, stating, "The vehicle's location has not been entered into the HD map. Please confirm whether to activate the map-free valet parking mode." If the user confirms the activation of the map-free valet parking mode, the parking space search device may output a prompt message through the in-vehicle interactive interface or user terminal stating, "In map-free mode, the vehicle may trigger a safety pause due to environmental complexity exceeding the algorithm's boundaries. The user is required to take over control at any time," and activate the map-free valet parking mode.

[0068] In one possible implementation, the parking space search device may be in communication with an onboard sensor. The parking space search device may collect sensing information via the onboard sensor in response to a non-mapped parking request.

[0069] Optionally, the vehicle-mounted sensor can be configured as needed. For example, the vehicle-mounted sensor can be a laser radar, a camera, an ultrasonic radar, or a combination of a laser radar, a camera, and an ultrasonic radar. This application does not impose any specific restrictions on this.

[0070] In one possible implementation, the parking space search device may construct a local map corresponding to the vehicle's perception range based on the perception information, and determine the local map as the global map if the first exploration point does not exist. The first exploration point is the exploration point closest to the vehicle's location that the vehicle passes through after parking planning.

[0071] That is, the parking space search device can determine the local map as the global map when there is no historical global map.

[0072] Among them, the historical global map can be obtained by integrating the target tree node into the global map established at the first exploration point, and the target tree node can be the tree node sampled when planning the path from the first exploration point to the vehicle position using the fast search random tree algorithm.

[0073] Alternatively, the parking space search device may fuse the local map with the historical global map to obtain a fused map when the first exploration point exists. The parking space search device may modify the fused map based on the target search range to obtain a global map.

[0074] The target search range is defined based on the vehicle's position. The target search area can be a local area in three-dimensional space with the vehicle's current position as the center point.

[0075] Optionally, the target search range can be set according to actual needs. For example, the target search range can be smaller than the vehicle perception range or equal to the vehicle perception range. This application does not impose specific restrictions on this.

[0076] Specifically, the parking space search device can determine the gain value of each tree node in the fused map, and then delete the first exploration point in the fused map and the tree nodes in the fused map whose gain value is less than the preset gain value and is outside the target search range to obtain a global map.

[0077] The gain value of each tree node is determined based on the distance between each tree node and other tree nodes in the fused map and / or the number of tree nodes included in the neighborhood of each tree node.

[0078] In one possible implementation, the gain of each tree node is negatively correlated with the distance between that node and other tree nodes in the fused map. The gain of each tree node is also negatively correlated with the number of tree nodes within its neighborhood. In other words, the shorter the distance between a tree node and other tree nodes in the fused map, the higher the gain, and the greater the number of tree nodes within a node's neighborhood, the higher the gain. This prevents fragmentation in the constructed global map and makes it difficult to form continuous paths.

[0079] In one possible implementation, a tree node newly added to the global map satisfies the following first formula:

[0080] in, Can be used to represent tree nodes newly added to the global map. Can be used to characterize path. Can be used to characterize Exploration value. Can be used to represent other tree nodes in the global map. Can be used to characterize P point and The Euclidean distance between them. It can be used to represent the preset distance threshold. Can be used to characterize P point and The ratio of the distance on the graph to the Euclidean distance. It can be used to represent a preset ratio threshold.

[0081] S202: Determine the target exploration point.

[0082] Among them, the target exploration point is the best exploration point selected from the perception boundary points in the global map.

[0083] In one possible implementation, the parking space search device may determine whether a first perception boundary point exists.

[0084] The first perception boundary point may be a boundary point located within the target search range. The first boundary point satisfies the following second to fourth formulas:

[0085]

[0086]

[0087] Among them, F L Can be used to characterize the first perception boundary point. F It can be used to represent the target search range. P can be used to represent the tree node. FOV (P) can be used to represent the field of view of point P, that is, the search range of the vehicle in the tree node. P B Can be used to represent the current position of the vehicle. FOV (P B ) can be used to characterize P B The field of view, that is, the vehicle's perception range.

[0088] The first formula can be used to characterize the perception boundary point in the target search range as the first perception boundary point. The second and third formulas can be used to characterize that the first perception boundary point must be within the field of view of at least one tree node or within the vehicle perception range.

[0089] In a possible implementation, the parking space search device may determine a target exploration point from the first perception boundary points when determining that there are first perception boundary points.

[0090] Specifically, the parking space search device may determine the perception boundary point with the highest exploration value among the first perception boundary points as the target exploration point.

[0091] The exploration value of the first perception boundary point can be determined based on the distance between the vehicle's position and the first perception boundary point, as well as the angle between the vehicle's first orientation and the vehicle's second orientation pointing toward the first perception boundary point. This allows the parking space search device to ensure that the vehicle reaches the first perception boundary point with the minimum cost (shortest path, turns), i.e., by minimizing the vehicle's turns and travel paths.

[0092] In another possible implementation method, the parking space search device can determine the target exploration point from the second perception boundary point when it is determined that the first perception boundary point does not exist. That is, the parking space search device can determine the target exploration point from the perception boundary point outside the target search range in the global map when there is no perception boundary point within the target search range.

[0093] The second perception boundary point may be a perception boundary point located outside the target search range in the global map.

[0094] Specifically, the parking space search device can determine the connection path with the shortest path length between the vehicle position and each second perception boundary point based on the branch and bound method, thereby determining the perception boundary point with the shortest connection path length among the second perception boundary points as the target exploration point.

[0095] The connection path may be a path connecting the vehicle position and the corresponding second perception boundary point through multiple tree nodes in the global map.

[0096] Specifically, the parking space search device can use a branch-and-bound method based on the traveling salesman problem to efficiently traverse the second perception boundary points. First, the global map and boundary point set are dynamically updated. Each second perception boundary point is mapped to a dynamic distance weight between cities in the branch-and-bound method to construct a cost matrix. Subsequently, the branch-and-bound method is used to search for the optimal path, rapidly generating a prioritized sequence of second perception boundary points to be visited. Finally, the currently optimal second perception boundary point is identified as the target exploration point.

[0097] S203 : Based on the first preset condition, a fast search random tree algorithm is used to plan a target planning path from the vehicle position to the target exploration point on the global map.

[0098] The first preset condition can be used to make the tree nodes sampled by the fast search random tree algorithm tend to fall into the neighborhood of the target exploration point.

[0099] In one possible implementation, the parking space search device may generate multiple planned branches from the vehicle position to the target exploration point using a fast search random tree algorithm based on the first preset condition.

[0100] Specifically, the parking space search device may randomly generate a tree node within the neighborhood of the target exploration point when the target value is less than or equal to a preset numerical threshold. Furthermore, the parking space search device may randomly generate a tree node outside the neighborhood of the target exploration point when the target value is greater than the preset numerical threshold.

[0101] The target value may be a randomly generated value within a preset value range.

[0102] Optionally, the preset numerical range can be set according to actual needs. For example, the preset numerical range can be 0-1, or the preset numerical range can be 0-2. This application does not impose specific restrictions on this.

[0103] Optionally, the preset numerical threshold value can be set according to the preset numerical range and actual needs. For example, when the preset numerical range is 0-1, the preset numerical threshold value can be 0.7 or 0.6; or, when the preset numerical range is 0-2, the preset numerical threshold value can be 1.5 or 1.4. This application does not impose specific limitations on this.

[0104] In one possible implementation, the parking space search device may determine a planned branch with the highest exploration value among multiple planned branches as the target planned path.

[0105] The exploration value of a planned branch is related to at least one of the following: the distance between two adjacent tree nodes in the planned branch relative to the vehicle's orientation; the angle between different edges in the planned branch; the distance between the tree node in the planned branch and the vehicle's location; the similarity between the planned branch and the planned path from the first exploration point to the vehicle's location; and the number of unknown voxels in the neighborhood of the tree node in the planned branch.

[0106] In one possible implementation, the exploration value of the planned branch satisfies the following fifth and sixth formulas:

[0107]

[0108] in, It can be used to represent the planned branch from the vehicle's current position to the target exploration point. Can be used to characterize Exploration value. Can be used to characterize The jth tree node in . DTW( ) can be used to characterize The gain value corresponding to the similarity between the planned path from the first exploration point to the vehicle position. Can be used to characterize The penalty parameter corresponding to the angle between different edges in . It can be used to characterize the exploration value of point P. Can be used to characterize The number of unknown voxels in the neighborhood of the tree node P, that is, the area of ​​the unknown region. Can be used to characterize The distance between the tree node P and the vehicle position. Can be used to characterize The penalty parameter corresponding to the forward distance of the edge between two adjacent tree nodes relative to the vehicle's orientation.

[0109] S204: Control the vehicle to cruise along the target planned path to search for an available parking space.

[0110] In one possible implementation, the parking space search device may control the vehicle to cruise along the target planned path according to the coordinates of multiple tree nodes in the target planned path to search for an available parking space.

[0111] In one possible implementation, the parking space search device may control the vehicle to park if a parking space is found. Alternatively, if no available parking space is found after the vehicle cruises to a target exploration point, the parking space search device may re-execute the aforementioned steps of constructing, determining, planning, and cruising based on the vehicle's position after cruising until an available parking space is found.

[0112] Based on the above technical solution, the present application can construct a global map based on the perception information within the vehicle's perception range when the vehicle is in an unknown map environment (not in the area covered by the high-precision map), and select the best target exploration point from it, thereby utilizing a fast search random tree algorithm to plan a target planning path from the vehicle position to the target exploration point according to the first preset condition, and the tree nodes tend to fall within the neighborhood of the target exploration point, thereby avoiding the problem of blind exploration of parking spaces or inability to carry out intelligent exploration due to lack of effective map information in an unknown environment, and realizing parking space exploration efficiently, accurately and intelligently.

[0113] In some embodiments, as Figure 3 As shown, it is a schematic diagram of a parking space search process provided by this application.

[0114] In one possible implementation, the process begins, and the parking space search device can control the vehicle to cruise to the selected parking space in the presence of a high-definition map. During the cruise, the parking space search device can establish a global map based on a rapid search random tree and a first perception boundary point. The parking space search device can determine whether the selected parking space is empty. If so, it can perform automatic parking and park the vehicle in the parking space. Otherwise, it can search for a parking space based on the high-precision map. The parking space search device can determine whether there is an empty parking space in the high-precision map. If so, it can perform automatic parking and park the vehicle in the parking space. Otherwise, the parking space search device can determine whether there is a second perception boundary point. If so, it can determine the target exploration point based on the second perception boundary point in the global map. The parking space search device can control the vehicle to cruise to the target exploration point. Alternatively, if there is no second perception boundary point, the parking space search device can determine whether there is a first perception point. If so, it can plan the target planning path based on the first perception boundary point. If not, the process ends.

[0115] When the vehicle reaches the target exploration point, the parking space search device can plan a target planning path based on the first perception boundary point. The parking space search device can cruise along the target planning path and determine whether there is an empty parking space. If so, it performs automatic parking, parks the vehicle in the parking space, and ends the process. Otherwise, the parking space search device can determine whether the first perception boundary point exists. If the first perception boundary point exists, the parking space search device can plan the target planning path based on the first perception boundary point. Otherwise, it determines whether a second perception boundary point exists.

[0116] The parking space search device can determine the target exploration point based on the second perception boundary point in the global map when the second perception boundary exists, otherwise the process ends.

[0117] In another possible implementation, the parking space search device may establish a fast search random tree and first perception boundary points based on a high-definition map to create a global map. The parking space search device may then determine a second perception boundary point. If a second perception boundary point exists, the device may determine a target exploration point based on the second perception boundary point in the global map. If a second perception boundary point does not exist, the device may determine whether the first perception boundary point exists.

[0118] In some embodiments, as Figure 4 , which is a schematic diagram of a fast search random tree construction process provided by this application.

[0119] In one possible implementation, the parking space search device may determine the vehicle position and the target exploration point, prune the previously constructed fast search random tree, and rebuild the fast search random tree based on the remaining nodes.

[0120] The parking space search device can determine a target value and a preset value threshold, and can determine whether the target value is less than or equal to the preset value threshold.

[0121] The parking space search device can randomly generate a tree node within the neighborhood of the target exploration point when the target value is less than or equal to a preset value threshold, and randomly generate a tree node outside the neighborhood of the target exploration point when the target value is greater than the preset value threshold.

[0122] The parking space search device can determine whether the number of generation times is less than a preset number threshold. If so, the target value is re-determined; if not, the process ends.

[0123] In some embodiments, as Figure 5 , which is a schematic diagram of a rapid search random tree construction result provided by this application.

[0124] In one possible implementation, Figure 5 This includes a fast search random tree that is continuously generated as the vehicle moves from point A to point B. Figure 5 It also includes the target search range of the vehicle at point B, the original tree node, the newly added tree node, and multiple first perception boundary points and second perception boundary points.

[0125] In some embodiments, as Figure 6 As shown, this is a flow chart of determining a target planning path provided by this application.

[0126] In one possible implementation, a parking space search device may determine a vehicle position and multiple first perception boundary points. The parking space search device may determine a target exploration point from the first perception boundary points. The parking space search device may employ a fast search random tree algorithm based on a first preset condition to generate multiple planned branches from the vehicle position to the target exploration point. The parking space search device may determine that the optimal exploration value is 0. The parking space search device may determine the exploration value of each planned branch.

[0127] Determining the exploration value of each planned path can include: The parking space search device can count from 1 to N, where N is the number of planned paths. The parking space search device can calculate the exploration value of the i-th planned path and determine whether the exploration value of the i-th planned path is greater than the optimal exploration value. If so, the exploration value of the i-th planned path is determined as the optimal exploration value. Otherwise, the device determines whether i equals N. If i equals N, the planned path corresponding to the optimal exploration value is determined as the target planned path. Otherwise, i+1 is used.

[0128] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, the parking space search device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] In the embodiments of the present application, the parking space search device or electronic device can be divided into functional modules according to the above method. For example, the parking space search device or electronic device can include functional modules corresponding to the functional divisions, or two or more functions can be integrated into a single processing module. The above integrated modules can be implemented in the form of hardware or software functional modules. It should be noted that the module division in the embodiments of the present application is schematic and is only a logical functional division. In actual implementation, other division methods may be used.

[0130] Figure 7 FIG. 1 is a block diagram of a parking space search device according to an exemplary embodiment. Figure 7 The parking space search device includes: a construction unit 701, a determination unit 702, a planning unit 703 and a control unit 704.

[0131] In one possible implementation, the construction unit 701 is configured to construct a global map based on perception information within a vehicle perception range corresponding to the vehicle position when the vehicle position is not within the coverage area of ​​the high-precision map.

[0132] In a possible implementation, the determining unit 702 is configured to determine a target exploration point.

[0133] In a possible implementation, the planning unit 703 is configured to plan a target planning path from the vehicle position to the target exploration point on the global map using a fast search random tree algorithm based on a first preset condition.

[0134] In one possible implementation, the control unit 704 is configured to control the vehicle to cruise along the target planned path to search for an available parking space.

[0135] In one possible implementation, the construction unit 701, the determination unit 702, the planning unit 703 and the control unit 704 are respectively used to re-execute the above-mentioned construction, determination, planning and cruising steps based on the vehicle position after cruising, when no available parking space is found when the vehicle cruises to the target exploration point, until an available parking space is found.

[0136] In one possible implementation, the construction unit 701 is specifically configured to: construct a local map corresponding to the vehicle's perception range based on the perception information, and determine the local map as the global map if the first exploration point does not exist.

[0137] In one possible implementation, the construction unit 701 is specifically configured to: when the first exploration point exists, fuse the local map with the historical global map to obtain a fused map; and modify the fused map based on a target search range to obtain a global map, wherein the target search range is defined based on the vehicle position.

[0138] In one possible implementation, the construction unit 701 is specifically configured to: determine a gain value of each tree node in the fused map, delete the first exploration point and tree nodes in the fused map whose gain values ​​are less than a preset gain value and are outside the target search range, and obtain a global map.

[0139] In a possible implementation, the determining unit 702 is specifically configured to: determine whether a first perception boundary point exists, and if it is determined that the first perception boundary point exists, determine a target exploration point from the first perception boundary point.

[0140] In a possible implementation, the determining unit 702 is specifically configured to determine a perception boundary point with the highest exploration value among the first perception boundary points as a target exploration point.

[0141] In a possible implementation, the determining unit 702 is specifically configured to: determine a target exploration point from the second perception boundary points when it is determined that the first perception boundary point does not exist.

[0142] In one possible implementation, the determining unit 702 is specifically configured to determine, based on a branch and bound method, a connection path with the shortest path length between the vehicle position and each second perception boundary point, and determine the perception boundary point with the shortest connection path length among the second perception boundary points as a target exploration point.

[0143] In one possible implementation, the planning unit 703 is specifically configured to: generate multiple planned paths from the vehicle position to the target exploration point using a fast search random tree algorithm based on a first preset condition, and determine the path with the highest exploration value among the multiple planned paths as the target planned path.

[0144] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0145] Figure 8 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 8 As shown, the electronic device includes but is not limited to: a processor 801 and a memory 802 .

[0146] The memory 802 is used to store executable instructions of the processor 801. It is understandable that the processor 801 is configured to execute instructions to implement the parking space search method in the above embodiment.

[0147] It should be noted that those skilled in the art can understand that Figure 8 The electronic device structure shown in the figure does not limit the electronic device, and the electronic device may include Figure 8 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0148] The processor 801 is the control center of the electronic device. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802 and calling data stored in the memory 802, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 801 may include one or more processing units. Optionally, the processor 801 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly handles wireless communications. It is understood that the above-mentioned modem processor may not be integrated into the processor 801.

[0149] Memory 802 can be used to store software programs and various data. Memory 802 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and application programs required by at least one functional module (such as a determination unit, a processing unit, etc.). Furthermore, memory 802 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0150] In an exemplary embodiment, a computer-readable storage medium including instructions is further provided, such as a memory 802 including instructions. The above instructions can be executed by a processor 801 of an electronic device to implement the method in the above embodiment.

[0151] In actual implementation, Figure 7 The functions of the construction unit 701, the determination unit 702, the planning unit 703 and the control unit 704 can all be represented by Figure 8 The processor 801 in the embodiment calls the computer program stored in the memory 802. The specific execution process can be referred to the description of the method part in the above embodiment, which will not be repeated here.

[0152] Alternatively, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, or an optical data storage device. In an exemplary embodiment, the present application further provides a computer program product comprising one or more instructions, which may be executed by the processor 801 of the electronic device to perform the method in the above embodiment.

[0153] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0154] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0156] Units described as separate components may or may not be physically separate, and components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0157] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0158] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The software product is stored in a storage medium and includes a number of instructions for causing a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or optical disk, etc. Various media that can store program code.

[0159] An embodiment of the present application provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer is caused to execute the parking space search method in the above method embodiment.

[0160] An embodiment of the present application further provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer is caused to execute the parking space search method in the method flow shown in the above method embodiment.

[0161] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a register, a hard disk, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In the embodiments of the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or device.

[0162] Since the parking space search device, computer-readable storage medium, and computer program product in the embodiments of the present application can be applied to the above method, the technical effects that can be obtained can also refer to the above method embodiments, and the embodiments of the present application will not be repeated here.

[0163] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A parking space search method, characterized in that: The method comprises: If the vehicle's position is not within the coverage area of ​​the HD map, a local map is constructed based on the perception information within the vehicle's perception range corresponding to the vehicle's position; wherein the perception information includes but is not limited to perception boundary points not blocked by obstacles and obstacles; The local map and the historical global map are integrated to construct a global map; wherein the historical global map is obtained by integrating a target tree node into the global map established at the first exploration point, the target tree node being a tree node sampled when planning a path from the first exploration point to the vehicle location using a fast search random tree algorithm; the first exploration point being the exploration point closest to the vehicle location that the vehicle passes through after parking planning; Determining a target exploration point; wherein the target exploration point is an optimal exploration point selected from the perception boundary points in the global map; Based on a first preset condition, using the fast search random tree algorithm, planning a target planning path from the vehicle position to the target exploration point on the global map; wherein the first preset condition is used to make the tree nodes sampled by the fast search random tree algorithm tend to fall within the neighborhood of the target exploration point; The vehicle is controlled to cruise along the target planned path to search for an available parking space.

2. The parking space search method according to claim 1, characterized in that: The method further comprises: If the available parking space is still not found when the vehicle cruises to the target exploration point, the above steps of constructing, determining, planning and cruising are re-executed based on the vehicle position after cruising until the available parking space is found.

3. The parking space search method according to claim 1, characterized in that: The fusing of the local map and the historical global map to construct a global map includes: In the absence of the first exploration point, determining the historical global map as an empty map; The empty map is merged with the local map to obtain the global map.

4. The parking space search method according to claim 3, characterized in that: The fusing of the local map and the historical global map to construct a global map includes: Fusing the local map with the historical global map to obtain a fused map; Based on a target search range, the fused map is modified to obtain the global map, wherein the target search range is defined based on the vehicle position.

5. The parking space search method according to claim 4, characterized in that: The step of modifying the fused map based on the target search range to obtain the global map includes: Determining a gain value of each tree node in the fused map; Deleting the first exploration point and the tree nodes in the fusion map whose gain values ​​are less than a preset gain value and are outside the target search range, to obtain the global map; The gain value of each tree node is determined based on the distance between each tree node and other tree nodes in the fused map and / or the number of tree nodes included in the neighborhood of each tree node.

6. The parking space search method according to any one of claims 1 to 5, characterized in that: Determining the target exploration point includes: Determining whether there is a first perception boundary point, wherein the first perception boundary point is a boundary point located within the target search range; In a case where it is determined that the first perception boundary point exists, the target exploration point is determined from the first perception boundary point.

7. The parking space search method according to claim 6, characterized in that: The determining the target exploration point from the first perception boundary point includes: Determining the perception boundary point with the highest exploration value among the first perception boundary points as the target exploration point; The exploration value of the first perception boundary point is determined based on the distance between the vehicle position and the first perception boundary point, and the angle between the first orientation of the vehicle and the second orientation of the vehicle position pointing to the first perception boundary point.

8. The parking space search method according to claim 6, characterized in that: The determining of the target exploration point further includes: In a case where it is determined that the first perception boundary point does not exist, determining the target exploration point from the second perception boundary points; The second perception boundary point is a perception boundary point in the global map that is outside the target search range.

9. The parking space search method according to claim 8, characterized in that: The determining the target exploration point from the second perception boundary point includes: Determining, based on a branch and bound method, a connection path with the shortest path length between the vehicle position and each of the second perception boundary points; wherein the connection path connects the vehicle position and the corresponding second perception boundary points through a plurality of tree nodes in the global map; The perception boundary point with the smallest connection path length among the second perception boundary points is determined as the target exploration point.

10. The parking space search method according to claim 3 or 4, characterized in that: The method of planning a target planning path from the vehicle position to the target exploration point on the global map by using a fast search random tree algorithm based on the first preset condition includes: Based on the first preset condition, a fast search random tree algorithm is used to generate multiple planned branches from the vehicle position to the target exploration point; Determine the planning branch with the highest exploration value among the plurality of planning branches as the target planning path; The exploration value of the planned branch road is related to at least one of the following: The forward distance of the edge between two adjacent tree nodes in the planned branch relative to the direction of the vehicle; The angles between different sides of the planned branch road; The distance between the tree node in the planned branch and the vehicle position; similarity between the planned branch road and the planned path from the first exploration point to the vehicle position; The number of unknown voxels in the neighborhood of the tree node in the planning branch.

11. The parking space search method according to claim 10, characterized in that: The first preset condition includes: When the target value is less than or equal to a preset value threshold, a tree node is randomly generated in the neighborhood of the target exploration point; and, when the target value is greater than a preset value threshold, randomly generating a tree node outside the neighborhood of the target exploration point; The target value is a randomly generated value within a preset value range.

12. A parking space search device, characterized in that: The device comprises: a construction unit, a determination unit, a planning unit and a control unit; The construction unit is configured to construct a local map based on perception information within a vehicle perception range corresponding to the vehicle position when the vehicle position is not within the coverage area of ​​the high-precision map; wherein the perception information includes but is not limited to perception boundary points not blocked by obstacles and obstacles; The construction unit is further configured to fuse the local map with the historical global map to construct a global map; wherein the historical global map is obtained by integrating a target tree node into the global map established at the first exploration point, the target tree node being a tree node sampled when planning a path from the first exploration point to the vehicle position using a fast search random tree algorithm; the first exploration point being the exploration point closest to the vehicle position that the vehicle passes through after parking planning; The determining unit is configured to determine a target exploration point; wherein the target exploration point is an optimal exploration point selected from the perception boundary points in the global map; The planning unit is configured to plan a target planning path from the vehicle position to the target exploration point on the global map using the fast search random tree algorithm based on a first preset condition; wherein the first preset condition is configured to cause the tree nodes sampled by the fast search random tree algorithm to tend to fall within a neighborhood of the target exploration point; The control unit is used to control the vehicle to cruise along the target planned path to search for an available parking space.

13. A vehicle, characterized in that: The parking space search device according to claim 12 is included.

Images