Vehicle parking control method, device, equipment, medium, product and vehicle

Abstract

The invention discloses a vehicle parking control method, device and equipment, a medium, a product and a vehicle, and relates to the technical field of vehicles. The method comprises the following steps: acquiring first azimuth information and parking drivable area information of a vehicle, wherein the parking drivable area information comprises second azimuth information of a target parking space; according to the first azimuth information and the parking drivable area information, third azimuth information of parking track segmentation points is determined; according to the preset minimum turning radius of the vehicle, the first azimuth information, the second azimuth information and the third azimuth information, a target parking track of the vehicle is generated, and the target parking track is used for indicating that the vehicle travels from the position corresponding to the first azimuth information to the position corresponding to the third azimuth information and then travels to the position corresponding to the second azimuth information; and controlling the vehicle to run according to the target parking track. According to the embodiment of the invention, the large vehicle can be controlled to complete parking with as little operation as possible.

Description

Technical Field

[0001] This application belongs to the field of vehicle technology, and in particular relates to a vehicle parking control method, device, equipment, medium, product and vehicle. Background Technology

[0002] Automated parking is a crucial function of autonomous driving technology, allowing vehicles to automatically find parking spaces and complete the parking maneuver without human driver intervention. While automated parking technology is widely used in passenger vehicles, large vehicles (such as trucks and buses) face greater challenges due to their size, larger turning radii, and more confined parking spaces. This increased spatial complexity makes parking operations more complicated, rendering existing automated parking technologies for passenger vehicles unsuitable for larger vehicles. Therefore, how to precisely control large vehicles within limited spaces to complete parking with minimal intervention has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0003] This application provides a vehicle parking control method, device, equipment, medium, product, and vehicle, which can control large vehicles to complete parking with as few operations as possible.

[0004] In a first aspect, embodiments of this application provide a vehicle parking control method, the method comprising:

[0005] Obtain the first location information and parking drivable area information of the vehicle, wherein the parking drivable area information includes the second location information of the target parking space;

[0006] Based on the first location information and the parking drivable area information, determine the third location information of the parking trajectory segment points;

[0007] Based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information, a target parking trajectory for the vehicle is generated. The target parking trajectory is used to indicate that the vehicle travels from the position corresponding to the first orientation information to the position corresponding to the third orientation information, and then travels to the position corresponding to the second orientation information.

[0008] Control the vehicle to drive along the target parking trajectory.

[0009] Secondly, embodiments of this application provide a vehicle parking control device, the device comprising:

[0010] The first acquisition module is used to acquire the first location information and parking drivable area information of the vehicle, wherein the parking drivable area information includes the second location information of the target parking space;

[0011] The first determining module is used to determine the third location information of the parking trajectory segment points based on the first location information and the parking drivable area information;

[0012] The generation module is used to generate a target parking trajectory for the vehicle based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information. The target parking trajectory is used to instruct the vehicle to travel from the position corresponding to the first orientation information to the position corresponding to the third orientation information, and then to the position corresponding to the second orientation information.

[0013] The control module is used to control the vehicle to drive according to the target parking trajectory.

[0014] Thirdly, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the vehicle parking control method as described above.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the vehicle parking control method described in any of the above claims.

[0016] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the vehicle parking control method as described in any of the above claims.

[0017] Sixthly, embodiments of this application provide a vehicle comprising at least one of the following:

[0018] The vehicle parking control device described above;

[0019] Electronic devices as described above;

[0020] The computer-readable storage medium as described above;

[0021] The computer program product described above.

[0022] The vehicle parking control method, apparatus, device, medium, product, and vehicle of this application embodiment can acquire the vehicle's first orientation information and parking drivable area information, the parking drivable area information including the second orientation information of the target parking space; determine the third orientation information of the parking trajectory segment points based on the first orientation information and the parking drivable area information; generate the vehicle's target parking trajectory based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information, the target parking trajectory is used to instruct the vehicle to travel from the position corresponding to the first orientation information to the position corresponding to the third orientation information, and then to the position corresponding to the second orientation information; control the vehicle to travel according to the target parking trajectory. Thus, in this application embodiment, by designing parking trajectory segment points through the first orientation information and the parking drivable area information, the parking trajectory can be decomposed into multiple easily operable trajectories, reducing the complexity of operation for large vehicles due to their large size and turning radius, thereby achieving precise control of large vehicles within a limited space to complete parking with as few operations as possible. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of the vehicle parking control method provided in the embodiments of this application;

[0025] Figure 2 This is a schematic diagram of the parking and driving area provided in the embodiments of this application;

[0026] Figure 3 This is a schematic diagram of the target parking space provided in an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the parking trajectory segment points provided in the embodiments of this application;

[0028] Figure 5 This is a schematic diagram of a scenario embodiment provided in this application;

[0029] Figure 6 This is a schematic diagram of the vehicle parking control device provided in the embodiments of this application;

[0030] Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0031] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0033] Automated parking is a crucial function of autonomous driving technology, allowing vehicles to automatically find parking spaces and complete the parking maneuver without human driver intervention. While automated parking technology is widely used in passenger vehicles, large vehicles (such as trucks and buses) face greater challenges due to their size, larger turning radii, and more confined parking spaces. This increased spatial complexity makes parking operations more complicated, rendering existing automated parking technologies for passenger vehicles unsuitable for larger vehicles. Therefore, how to precisely control large vehicles within limited spaces to complete parking with minimal intervention has become a pressing technical problem for those skilled in the art.

[0034] To address the problems of the prior art, embodiments of this application provide a vehicle parking control method, apparatus, device, medium, product, and vehicle. The vehicle parking control method provided in this application embodiment will be described first below.

[0035] Figure 1 A schematic flowchart of a vehicle parking control method according to an embodiment of this application is shown. Figure 1 As shown, a vehicle parking control method may include the following steps S101 to S104:

[0036] S101. Obtain the vehicle's first location information and parking drivable area information, the parking drivable area information including the second location information of the target parking space;

[0037] S102. Based on the first location information and the parking drivable area information, determine the third location information of the parking trajectory segment points;

[0038] S103. Based on the preset minimum turning radius of the vehicle, first position information, second position information and third position information, generate the target parking trajectory of the vehicle. The target parking trajectory is used to indicate that the vehicle travels from the position corresponding to the first position information to the position corresponding to the third position information, and then travels to the position corresponding to the second position information.

[0039] S104. Control the vehicle to drive along the target parking trajectory.

[0040] The vehicle parking control method of this application embodiment can acquire the vehicle's first orientation information and parking drivable area information, the parking drivable area information including the second orientation information of the target parking space; determine the third orientation information of the parking trajectory segment points based on the first orientation information and the parking drivable area information; generate the vehicle's target parking trajectory based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information, the target parking trajectory is used to instruct the vehicle to travel from the position corresponding to the first orientation information to the position corresponding to the third orientation information, and then to the position corresponding to the second orientation information; control the vehicle to travel according to the target parking trajectory. Thus, in this application embodiment, by designing parking trajectory segment points through the first orientation information and the parking drivable area information, the parking trajectory can be decomposed into multiple easily operable trajectories, reducing the complexity of operation for large vehicles due to their large size and turning radius, thereby achieving precise control of large vehicles within a limited space to complete parking with as few operations as possible.

[0041] In S101, the aforementioned vehicle can be a large vehicle with a large volume and turning radius, such as a truck or bus. Of course, in this embodiment, the vehicle is not limited to large vehicles and can also be applied to other vehicles.

[0042] The aforementioned first orientation information describes the vehicle's position and direction. Specifically, the first orientation information may include first position sub-information and first heading sub-information. The first position sub-information can be used to indicate the vehicle's location. The first heading sub-information can be used to indicate the vehicle's direction (such as heading angle).

[0043] The aforementioned drivable parking area information describes the area a vehicle can drive through during parking. The boundaries of the drivable parking area serve as hard constraints to ensure the vehicle does not collide with these boundaries during parking. The drivable parking area information may include second-direction information about the target parking space, where the target parking space is the specific area where the vehicle is planned to park. The second-direction information may specifically include second-position sub-information and second-heading sub-information. The second-position sub-information indicates the location of the target parking space. The second-heading sub-information indicates the direction of the target parking space (e.g., heading angle).

[0044] The acquisition of the vehicle's first location information and parking drivable area information, as exemplarily, can be achieved through a sensing unit or a high-precision map unit.

[0045] In S102, the aforementioned parking trajectory segmentation points can decompose the vehicle's parking trajectory into multiple easily operable trajectories, reducing the complexity of operation for large vehicles due to their large size and turning radius, thereby enabling precise control of large vehicles within a limited space to complete parking with as few operations as possible.

[0046] The aforementioned third-party information can specifically include third position sub-information and third heading sub-information. The third position sub-information can be used to indicate the location of the parking trajectory segment point. The third heading sub-information can be used to indicate the direction (e.g., heading angle) of the parking trajectory segment point.

[0047] The aforementioned determination of the third location information of the parking trajectory segment point based on the first location information and the parking drivable area information can, for example, involve determining the relative direction information of the vehicle relative to the target parking space based on the first heading sub-information in the first location information and the second heading sub-information in the second location information; determining the third heading sub-information of the parking trajectory segment point based on the relative direction information; and determining the third position sub-information of the parking trajectory segment point based on the parking drivable area information and the third heading sub-information. The third location information includes the third heading sub-information and the third position sub-information.

[0048] In S103, the minimum turning radius of the aforementioned vehicle can refer to the minimum turning radius that the vehicle can complete when the front wheel steering angle reaches its maximum value during a turn. The minimum turning radius varies between different vehicles.

[0049] The aforementioned target parking trajectory can be a set of waypoints for a complete parking trajectory, used to indicate that the vehicle travels from the location corresponding to the first location information to the location corresponding to the third location information, and then to the location corresponding to the second location information. The target parking trajectory may include a first trajectory and a second trajectory.

[0050] The above-mentioned method generates the target parking trajectory of the vehicle based on the preset minimum turning radius, first position information, second position information, and third position information. For example, it can generate a first trajectory of the vehicle based on the first position information, the third position information, and the preset minimum turning radius of the vehicle. The first trajectory is the trajectory from the position corresponding to the first position information to the position corresponding to the third position information. A second trajectory of the vehicle is generated based on the second position information, the third position information, and the minimum turning radius. The second trajectory is the trajectory from the position corresponding to the third position information to the position corresponding to the second position information. The first trajectory and the second trajectory are then concatenated to obtain the target parking trajectory of the vehicle.

[0051] In S104, the aforementioned controlled vehicle travels according to the target parking trajectory. For example, the controlled vehicle can travel according to the attributes of each path point in the target parking trajectory. The attributes of the path points may include acceleration, curvature, position, heading angle, and path arc length.

[0052] In some embodiments, the above-described S102 may specifically include:

[0053] Based on the first heading sub-information in the first orientation information and the second heading sub-information in the second orientation information, the relative orientation information of the vehicle with respect to the target parking space is determined.

[0054] Based on the relative direction information, determine the third heading sub-information of the parking trajectory segment points;

[0055] Based on the parking drivable area information and the third heading sub-information, the third position sub-information of the parking trajectory segment points is determined. The third position sub-information includes the third heading sub-information and the third position sub-information.

[0056] The relative orientation information of the vehicle with respect to the target parking space mentioned above. yaw It can be the heading angle (location) in the first heading sub-information of the first bearing information. yaw The difference between the heading angle in the second heading sub-information of the second orientation information and the heading angle in the second orientation information is parkplot_center yaw That is, flu yaw =location yaw -parkplot_center yaw .

[0057] The third heading sub-information for determining the parking trajectory segment points based on the relative direction information, as exemplified above, could be if sin(flu yaw If sin(flu) < 0, then the current vehicle position is in the lane closest to the target parking space; otherwise, the current vehicle position is in the lane furthest from the target parking space. yawWhen the value is less than 0, to facilitate large vehicles parking with minimal maneuvering, the parking trajectory segment point should be selected to the upper right of the target parking space. Otherwise, the parking trajectory segment point should be selected to the upper left of the target parking space.

[0058] The above-mentioned determination of the third position sub-information of the parking trajectory segment points based on the parking drivable area information and the third heading sub-information can be exemplified by discretizing the area associated with the target parking space in the direction corresponding to the third heading sub-information based on the parking drivable area information to obtain the position sub-information of multiple discrete points; generating trajectory curves for each discrete point based on the second position sub-information in the second orientation information and the position sub-information of each discrete point; and determining the position sub-information of the discrete point corresponding to the optimal trajectory curve among the multiple trajectory curves as the third position sub-information of the parking trajectory segment points, where the optimal trajectory curve is the trajectory curve corresponding to the minimum evaluation value among the multiple trajectory curves.

[0059] In this embodiment, by combining the first and second orientation information, the relative direction information of the vehicle with respect to the target parking space can be determined more accurately. Based on this, the third heading sub-information of the parking trajectory segment point can be determined. Then, based on the parking drivable area information and the third heading sub-information, the third position sub-information of the parking trajectory segment point can be determined. This helps to generate a more accurate parking trajectory, thereby improving parking accuracy.

[0060] In some embodiments, the determination of the third position sub-information of the parking trajectory segment points based on the parking drivable area information and the third heading sub-information may specifically include:

[0061] Based on the parking drivable area information, the area associated with the target parking space in the direction corresponding to the third heading sub-information is discretized to obtain the location sub-information of multiple discrete points;

[0062] Based on the second position sub-information in the second orientation information and the position sub-information of each discrete point, generate the trajectory curve of each discrete point;

[0063] The position sub-information of the discrete point corresponding to the optimal trajectory curve among multiple trajectory curves is determined as the third position sub-information of the parking trajectory segment point. The optimal trajectory curve is the trajectory curve corresponding to the minimum evaluation value among multiple trajectory curves.

[0064] The above-mentioned discretization process is performed on the area associated with the target parking space in the direction corresponding to the third heading sub-information based on the parking drivable area information, to obtain the position sub-information of multiple discrete points. For example, it can be based on sin(flu yaw If ) < 0, that is, when the parking trajectory segment point is selected to the upper right of the target parking space, the right side range of the target parking space is discretized with a preset resolution of Δx, Δy to obtain the position sub-information of multiple discrete points.

[0065] The above method generates trajectory curves for each discrete point based on the second position sub-information in the second orientation information and the position sub-information of each discrete point. For example, this can be achieved by starting from the center point of the target parking space indicated by the second position sub-information, and connecting the discrete points within the right-hand range, with each discrete point as the endpoint, to obtain the RS curve, i.e., the trajectory curve. The RS curve considers forward and reverse motion (such as the movement of a vehicle in reverse gear) and is used to plan a path with orientation requirements between two points. This method consists of multiple circular arcs (based on the vehicle's minimum turning radius) and a straight line segment, resulting in 48 possible connection methods. JA Reeds and LA Shepp proved that, in the absence of obstacles, any two points can always be connected by one or more RS curves.

[0066] The aforementioned optimal trajectory curve can be the trajectory curve corresponding to the minimum evaluation value among multiple trajectory curves. The third position sub-information of the parking trajectory segment points can be the position sub-information of the discrete points corresponding to the optimal trajectory curve among multiple trajectory curves.

[0067] In this embodiment, by discretizing the area associated with the target parking space in the direction corresponding to the third heading sub-information, the position sub-information of multiple discrete points is obtained. Combined with the second position sub-information in the second orientation information and the position sub-information of each discrete point, the trajectory curve of each discrete point is generated. Finally, the optimal trajectory curve of the target parking space in the area associated with the direction corresponding to the third heading sub-information is found. Based on the position sub-information of the discrete point corresponding to the optimal trajectory curve, the third position sub-information of the parking trajectory segment points can be accurately obtained, thereby further realizing the precise control of large vehicles in a limited space to complete parking with as few operations as possible.

[0068] In some embodiments, the trajectory curve may include path length, minimum turning radius, path curvature, and path deviation. Specifically, determining the position sub-information of the discrete point corresponding to the optimal trajectory curve among multiple trajectory curves as the third position sub-information of the parking trajectory segment point may include:

[0069] The evaluation value of each trajectory curve is determined based on its path length, minimum turning radius, curvature, and deviation.

[0070] Among multiple trajectory curves, the trajectory curve corresponding to the minimum evaluation value is determined as the optimal trajectory curve;

[0071] The position sub-information of the discrete points corresponding to the optimal trajectory curve is determined as the third position sub-information of the parking trajectory segment points.

[0072] The path lengths mentioned above represent the total distance a vehicle travels during the parking process. A shorter path length indicates a shorter distance traveled, typically signifying a more efficient route.

[0073] The minimum turning radius mentioned above can refer to the smallest turning radius among all the turning segments on the path. A smaller minimum turning radius means that the path requires greater vehicle maneuverability and usually implies more complex turning maneuvers.

[0074] The aforementioned path curvature refers to the degree of bending of the path. The greater the curvature, the more curved the path, which usually means that the vehicle needs to adjust its direction more frequently.

[0075] The aforementioned path deviation refers to the degree to which the vehicle deviates from the planned path during operation. The smaller the deviation, the closer the vehicle is to the planned path, which generally means better path tracking performance.

[0076] The above-mentioned evaluation value of each trajectory curve is determined based on the path length, minimum turning radius, path curvature, and path deviation. For example, different curves can be evaluated using an objective function formula, the objective function J of which is as follows:

[0077] J = w1L + w2R + w3S + w4O

[0078] in:

[0079] (L) represents the path length, used to reduce the total distance a vehicle travels during parking.

[0080] (R) represents the minimum turning radius in the path, ensuring that the path meets the vehicle's turning capabilities.

[0081] (S) represents the curvature of the path, which can reduce the violent movement of the vehicle when turning, improving comfort and safety.

[0082] (O) indicates the degree of deviation between the path and the desired orientation. The path is planned based on the initial and final orientation of the vehicle to ensure that the vehicle conforms to the desired orientation during parking.

[0083] (w1, w2, w3, w4) are weighting coefficients used to adjust the importance of different objective items.

[0084] In practical applications, these weighting coefficients can be adjusted according to specific parking needs and vehicle characteristics to obtain the optimal route planning results.

[0085] In this embodiment, each trajectory curve is evaluated by its path length, minimum turning radius, curvature, and deviation, which can accurately obtain the evaluation value of each trajectory curve. Then, the trajectory curve with the minimum evaluation value is determined as the optimal trajectory curve among multiple trajectory curves. Finally, based on the position sub-information of the discrete points corresponding to the optimal trajectory curve, the third position sub-information of the parking trajectory segment points is further accurately obtained.

[0086] In some embodiments, the above-described S103 may specifically include:

[0087] Based on the first position information, the third position information, and the preset minimum turning radius of the vehicle, a first trajectory of the vehicle is generated. The first trajectory is the trajectory from the position corresponding to the first position information to the position corresponding to the third position information.

[0088] Based on the second location information, the third location information, and the minimum turning radius, a second trajectory of the vehicle is generated. The second trajectory is the trajectory from the position corresponding to the third location information to the position corresponding to the second location information.

[0089] By splicing the first trajectory with the second trajectory, the target parking trajectory of the vehicle is obtained.

[0090] The aforementioned first trajectory can be the trajectory from the position corresponding to the first location information to the position corresponding to the third location information.

[0091] The aforementioned second trajectory can be the trajectory of traveling from the position corresponding to the third-party location information to the position corresponding to the second-party location information.

[0092] The first trajectory of the vehicle is generated based on the first azimuth information, the third azimuth information, and the preset minimum turning radius of the vehicle. For example, it can be generated by using a hybrid A-star method, taking the position corresponding to the first azimuth information as the starting point and the position corresponding to the third azimuth information as the ending point, combining the continuous state of the vehicle with each cell node, and considering the minimum turning radius of the vehicle as a constraint condition. The first trajectory includes multiple trajectory points.

[0093] The second trajectory of the vehicle is generated based on the second and third location information and the minimum turning radius. The generation process of the first trajectory is the same and will not be repeated here. The second trajectory also includes multiple trajectory points.

[0094] The above-mentioned concatenation of the first trajectory and the second trajectory to obtain the target parking trajectory of the vehicle can be exemplified by converting each path point in the path point array of the first trajectory and the second trajectory from the FLU coordinate system relative to the target parking space to the UTM coordinate system, and filling each converted path point into a trajectory message.

[0095] First, it iterates through the array of path points, processing each path point one by one. For each point, it calculates the original FLU coordinates and heading angle. x ,point y ,point yaw Convert to UTM coordinate system (trajectory) x ,trajectory y ,trajectory yaw ).

[0096] trajectory x =parkplot_center x +cos(parkplot_center yaw )*point x -sin(parkplot_center yaw )*point y

[0097] trajectory y

[0098] =parkplot_center y +sin(parkplot_center yaw )*point x +cos(parkplot_center yaw )*point y

[0099] trajectory yaw =point yaw +parkplot_center yaw

[0100] After the conversion is complete, the attributes of each path point are updated, such as acceleration, curvature, position, and heading angle. Then, the distance from each path point to the previous point is calculated, and the arc length of the path is accumulated accordingly. Finally, the processed path points are concatenated to obtain the target parking trajectory.

[0101] In this embodiment, the minimum turning radius of the vehicle is considered as a constraint. Based on the first orientation information, the second orientation information, and the third orientation information, the first trajectory and the second trajectory of the vehicle are generated. By splicing the first trajectory and the second trajectory, a target parking trajectory that is more consistent with the actual driving situation of the vehicle can be obtained, further ensuring that the vehicle completes parking with as few operations as possible.

[0102] As one implementation of this application, in order to ensure that the vehicle can implement the target parking trajectory, before S104 above, the method may further include:

[0103] When obstacles exist within the parking-accessible area information, obtain the location and image information of the obstacles;

[0104] Based on image information, identify the obstacle type of the obstacle. The obstacle type is used to characterize whether the obstacle is a stationary obstacle or a moving obstacle.

[0105] Collision detection is performed based on the location information of obstacles, the target parking trajectory, and the expansion coefficient corresponding to the obstacle type. The detection results are used to characterize whether the vehicle will collide with obstacles when it travels along the target parking trajectory. Different obstacle types correspond to different expansion coefficients.

[0106] Specifically, S104 mentioned above may include:

[0107] If the test results indicate that the vehicle will not collide with obstacles while driving along the target parking trajectory, control the vehicle to drive along the target parking trajectory.

[0108] For example, the presence of obstacles in the aforementioned parking and driving area information can be detected by the vehicle using radar sensors.

[0109] For example, obtaining the location and image information of obstacles can be achieved by detecting the location of obstacles using the vehicle's radar and collecting image information of obstacles using an onboard camera.

[0110] The obstacle types described above can be used to characterize whether an obstacle is stationary or moving. For example, a stationary obstacle can be a parking bollard, a trash can, or a fire hydrant, while a moving obstacle can be a pedestrian or an animal.

[0111] The above detection results can be used to characterize whether the vehicle will collide with an obstacle when traveling along the target parking trajectory. In some embodiments, if the detection results indicate that the vehicle will collide with an obstacle when traveling along the target parking trajectory, the vehicle is braked.

[0112] The aforementioned expansion coefficient refers to the process of enlarging the actual size of an obstacle in collision detection by a certain proportion, creating an "expanded" obstacle area. This expanded area is used to ensure that the vehicle maintains a safe distance from the obstacle during travel. Different obstacle types correspond to different expansion coefficients. If the obstacle is stationary, a smaller vehicle expansion coefficient (f1, f2) is used for collision detection. If the obstacle is moving, the vehicle expansion coefficient is increased for collision detection.

[0113] In this embodiment, when there are obstacles in the parking drivable area information, the location information and image information of the obstacles are obtained, and the obstacle type is identified based on the image information. The obstacle type is used to characterize whether the obstacle is a stationary obstacle or a moving obstacle. Then, collision detection is performed based on the location information of the obstacles, the target parking trajectory, and the expansion coefficient corresponding to the obstacle type, and a detection result is generated. Only when the detection result indicates that the vehicle will not collide with the obstacle when driving along the target parking trajectory, the vehicle is controlled to drive along the target parking trajectory to ensure that the vehicle can implement the target parking trajectory.

[0114] As another implementation of this application, in order to accurately determine whether the vehicle has reached the target parking space, after S104 above, the method may further include:

[0115] Acquire vehicle speed and location information as the vehicle travels along the target parking trajectory;

[0116] If the vehicle speed information corresponds to a speed less than a preset speed threshold, and the position error between the location information and the second location information is less than a preset threshold range, the vehicle is determined to have reached the target parking space.

[0117] In this embodiment, by comparing the vehicle speed and location information when the vehicle is traveling along the target parking trajectory with the preset speed threshold and the preset threshold range, it is possible to accurately determine whether the vehicle has reached the target parking space.

[0118] To facilitate understanding of the vehicle parking control method in the embodiments of this application, the actual application process of this vehicle parking control method is described as follows:

[0119] 1. Determine the permitted parking area

[0120] First, the parking-accessible area is obtained through sensing units or high-precision map units, such as... Figure 2 As shown, the boundaries of the parking and driving area will serve as hard constraints to ensure that vehicles do not collide with these boundaries during parking.

[0121] 2. Determine the parking segment points (i.e., the parking trajectory segment points mentioned above).

[0122] By determining the current vehicle's position and orientation in the UTM coordinate system x ,location y ,location yaw (Equivalent to the first position sub-information and first heading sub-information in the first orientation information above) Transformed to the FLU coordinate system of the target parking space (parkplot_center) x ,parkplot_center y,parkplot_center yaw (Equivalent to the second position sub-information and second heading sub-information in the second orientation information mentioned above), determine the current position of the vehicle, such as Figure 3 As shown.

[0123] Calculate UTM coordinates (location) x ,location y ,location yaw Relative to the origin of the FLU coordinate system (parkplot_center) x ,parkplot_center y ,parkplot_center yaw The relative position of delta x and delta y .

[0124] delta x =location X -parkplot_center x

[0125] delta y =location y -parkplot_center y

[0126] Use the following formula to convert UTM coordinates to FLU coordinates:

[0127] flu x =cos(parkplot_center) yaw )*delta x +sin(parkplot_center yaw )

[0128] *delta y

[0129] flu y = -sin(parkplot_center) yaw )*delta x +cos(parkplot_center yaw )

[0130] *delta y

[0131] flu yaw =location yaw -parkplot_centeryaw (Equivalent to the relative direction information mentioned above)

[0132] If sin(flu yaw If ) < 0, then the current vehicle position is in the lane closest to the target parking space; otherwise, the current vehicle position is in the lane furthest from the target parking space.

[0133] When sin(flu yaw When the value is less than 0, to facilitate large vehicles parking with minimal maneuvering, the segmentation point should be selected at the upper right of the parking space. Otherwise, the segmentation point should be selected at the upper left of the parking space, such as... Figure 4 As shown.

[0134] The direction of the segment point (equivalent to the third heading sub-information mentioned above) has been determined; the specific location of the segment point still needs to be determined. Using sin(flu) yaw Taking the case where the segmentation point is selected at the upper right of the parking space (where Δx and Δy are the resolutions), the right-side range of the parking space is discretized. Starting from the center point of the target parking space, and using the discrete points within the right-side range as endpoints, an RS curve is constructed to connect these points. This determines the specific location of the segmentation point (equivalent to the third location sub-information mentioned above). Figure 5 As shown.

[0135] The RS curve considers both forward and reverse motion (such as the movement of a vehicle in reverse gear) and is used to plan paths with orientation requirements between two points. This method consists of multiple circular arcs (based on the vehicle's minimum turning radius) and a straight line segment, resulting in 48 possible connections. JA Reeds and LA Shepp proved that, in the absence of obstacles, any two points can always be connected by one or more RS curves.

[0136] Theoretically, multiple discrete points can have multiple RS curves (equivalent to the trajectory curves mentioned above). However, in actual path planning, it is necessary to calculate whether these multiple RS curves can generate a valid path between the current location and the target parking space without collision. If there is only one usable RS curve, the endpoint of that curve is selected as the segmentation point. If multiple usable RS curves exist, the different curves are evaluated using the following objective function J (equivalent to the evaluation value mentioned above) to select the optimal curve.

[0137] J = w1L + w2R + w3S + w4O

[0138] in:

[0139] (L) represents the path length, used to reduce the total distance a vehicle travels during parking.

[0140] (R) represents the minimum turning radius in the path, ensuring that the path meets the vehicle's turning capabilities.

[0141] (S) represents the curvature of the path, which can reduce the violent movement of the vehicle when turning, improving comfort and safety.

[0142] (O) indicates the degree of deviation between the path and the desired orientation. The path is planned based on the initial and final orientation of the vehicle to ensure that the vehicle conforms to the desired orientation during parking.

[0143] (w1, w2, w3, w4) are weighting coefficients used to adjust the importance of different objective items.

[0144] In practical applications, these weighting coefficients can be adjusted according to specific parking needs and vehicle characteristics to obtain the optimal path planning result. The curve of the minimum objective function J is the optimal curve, and the last trajectory point of the optimal curve is the optimal segment point. At this point, the location of the segment point has been determined.

[0145] 3. Path planning

[0146] Since the locations of the segment points have been determined, the next step is to use a hybrid A-plane + RS curve method for path planning. Path planning is performed between the current position of the large vehicle and the segment points, forming the first trajectory segment (equivalent to the first trajectory mentioned above). Path planning is then performed between the segment points and the center point of the target parking space, forming the second trajectory segment (equivalent to the second trajectory mentioned above). The trajectories are then stitched together to form the complete parking trajectory (equivalent to the target parking trajectory mentioned above).

[0147] The Hybrid A* method combines the vehicle's continuous state with each cell node, considering the vehicle's minimum turning radius as a constraint, thus making the path planning more in line with actual driving needs. Cell transition costs are based on costs associated with the continuous state to ensure path smoothness. Simultaneously, heuristic costs are used to estimate the distance to the destination, guiding the search process. The total cost comprehensively considers path smoothness and efficiency in reaching the target; the smaller the value, the closer the path is to optimal driving habits. During the Hybrid A* process, RS curves are continuously used to connect the current point to the destination. If the connection is successful, trajectory planning is successful; otherwise, Hybrid A* planning continues.

[0148] 4. Trajectory splicing

[0149] Each path point in a complete parking trajectory x ,point y ,point yaw The FLU coordinate system is converted to the UTM coordinate system, and each converted path point is filled into a trajectory message.

[0150] First, it iterates through the array of path points, processing each path point one by one. For each point, it calculates the original FLU coordinates and heading angle. x ,point y ,point yaw Convert to UTM coordinate system (trajectory) x ,trajectory y ,trajectory yaw ).

[0151] trajectory x =parkplot_center x +cos(parkplot_center yaw )*point x -sin(parkplot_center yaw )*point y

[0152] trajectory y

[0153] =parkplot_center y +sin(parkplot_center yaw )*point x +cos(parkplot_center yaw )*point y

[0154] trajectory yaw =point yaw +parkplot_center yaw

[0155] After the transformation is complete, the properties of the waypoints are updated, such as acceleration, curvature, position, and heading angle. Then, the distance from each waypoint to the previous point is calculated, thus accumulating the arc length of the calculated path. Finally, each processed waypoint is added to the trajectory to obtain the complete parking trajectory.

[0156] 5. Trajectory Detection and Release

[0157] If a parking trajectory already exists, then proceed with collision detection between the vehicle (length, width) and obstacles. If the obstacle is stationary, use a smaller vehicle expansion coefficient (f1, f2) for collision detection. If the obstacle is moving, increase the vehicle expansion coefficient for collision detection.

[0158] Length = f1Length

[0159] Width = f2Width

[0160] If the collision detection passes, publish the trajectory; otherwise, publish the AEB trajectory.

[0161] 6. Parking completion judgment

[0162] If the vehicle speed v is less than the vehicle speed threshold v limit The position of the vehicle relative to the last point of the trajectory (trajectory_back) x ,trajectory_back y ) less than the threshold range (x) limit ,y limit If the vehicle reaches the parking endpoint, then parking is considered successful; otherwise, the parking trajectory continues to be published.

[0163]

[0164] In this embodiment, the parking process is divided into multiple stages, simplifying the operational requirements of each stage and making the parking of large vehicles in confined spaces more intuitive and controllable. This segmented approach reduces operational complexity and alleviates driver stress during parking. Furthermore, the segmented parking method allows the vehicle to adjust its position gradually, with each step optimized for the specific needs of the current stage. This step-by-step operation improves parking accuracy and reduces errors caused by turning radius limitations.

[0165] Based on the vehicle parking control method provided in the above embodiments, this application also provides specific implementation methods of the vehicle parking control device. Please refer to the following embodiments.

[0166] like Figure 6 As shown, the vehicle parking control device 600 provided in this application embodiment may include the following modules: a first acquisition module 601, a first determination module 602, a generation module 603, and a control module 604.

[0167] The first acquisition module 601 is used to acquire the first location information of the vehicle and the parking drivable area information, wherein the parking drivable area information includes the second location information of the target parking space.

[0168] The first determining module 602 is used to determine the third location information of the parking trajectory segment points based on the first location information and the parking drivable area information;

[0169] The generation module 603 is used to generate a target parking trajectory for the vehicle based on the preset minimum turning radius of the vehicle, first position information, second position information and third position information. The target parking trajectory is used to indicate that the vehicle travels from the position corresponding to the first position information to the position corresponding to the third position information, and then travels to the position corresponding to the second position information.

[0170] The control module 604 is used to control the vehicle to drive along the target parking trajectory.

[0171] The vehicle parking control device of this application embodiment can acquire first location information of the vehicle and parking drivable area information, the parking drivable area information including second location information of the target parking space; determine third location information of parking trajectory segment points based on the first location information and parking drivable area information; generate a target parking trajectory of the vehicle based on the preset minimum turning radius of the vehicle, the first location information, the second location information, and the third location information, the target parking trajectory is used to instruct the vehicle to travel from the position corresponding to the first location information to the position corresponding to the third location information, and then to the position corresponding to the second location information; control the vehicle to travel according to the target parking trajectory. Thus, in this application embodiment, by designing parking trajectory segment points through the first location information and parking drivable area information, the parking trajectory can be decomposed into multiple easy-to-operate trajectories, reducing the complexity of operation for large vehicles due to their large size and turning radius, thereby achieving precise control of large vehicles within a limited space to complete parking with as few operations as possible.

[0172] In some embodiments, the first determining module 602 described above may specifically include:

[0173] The first determining submodule is used to determine the relative direction information of the vehicle relative to the target parking space based on the first heading sub-information in the first orientation information and the second heading sub-information in the second orientation information.

[0174] The second determining submodule is used to determine the third heading sub-information of the parking trajectory segment points based on the relative direction information;

[0175] The third determination submodule is used to determine the third position sub-information of the parking trajectory segment points based on the parking drivable area information and the third heading sub-information. The third position sub-information includes the third heading sub-information and the third position sub-information.

[0176] In some embodiments, the third determining submodule described above may specifically include:

[0177] The processing unit is used to discretize the area associated with the target parking space in the direction corresponding to the third heading sub-information based on the parking drivable area information, and obtain the position sub-information of multiple discrete points;

[0178] The generation unit is used to generate the trajectory curve of each discrete point based on the second position sub-information in the second orientation information and the position sub-information of each discrete point;

[0179] The determining unit is used to determine the position sub-information of the discrete point corresponding to the optimal trajectory curve among multiple trajectory curves as the third position sub-information of the parking trajectory segment point. The optimal trajectory curve is the trajectory curve corresponding to the minimum evaluation value among multiple trajectory curves.

[0180] In some embodiments, the trajectory curve may include path length, minimum turning radius, path curvature, and path deviation; the determining unit may specifically include:

[0181] The first determining sub-unit is used to determine the evaluation value of each trajectory curve based on the path length, minimum turning radius, curvature, and deviation of each trajectory curve.

[0182] The second determining subunit is used to determine the trajectory curve corresponding to the minimum evaluation value among multiple trajectory curves as the optimal trajectory curve;

[0183] The third determining sub-unit is used to determine the position sub-information of the discrete points corresponding to the optimal trajectory curve as the third position sub-information of the parking trajectory segment points.

[0184] In some embodiments, the generation module 603 described above may specifically include:

[0185] The first generation submodule is used to generate the first trajectory of the vehicle based on the first orientation information, the third orientation information and the preset minimum turning radius of the vehicle. The first trajectory is the trajectory from the position corresponding to the first orientation information to the position corresponding to the third orientation information.

[0186] The second generation submodule is used to generate the second trajectory of the vehicle based on the second orientation information, the third orientation information and the minimum turning radius. The second trajectory is the trajectory from the position corresponding to the third orientation information to the position corresponding to the second orientation information.

[0187] The splicing submodule is used to splice the first trajectory with the second trajectory to obtain the target parking trajectory of the vehicle.

[0188] As one implementation of this application, in order to ensure that the vehicle can implement the target parking trajectory, the above-mentioned device 600 may further include:

[0189] The second acquisition module is used to acquire the location information and image information of obstacles when there are obstacles in the parking drivable area information;

[0190] The recognition module is used to identify the obstacle type based on image information. The obstacle type is used to characterize whether the obstacle is a stationary obstacle or a moving obstacle.

[0191] The detection module is used to perform collision detection based on the location information of obstacles, the target parking trajectory, and the expansion coefficient corresponding to the obstacle type, and generate detection results. The detection results are used to characterize whether the vehicle will collide with obstacles when it travels along the target parking trajectory. Different obstacle types correspond to different expansion coefficients.

[0192] The aforementioned control module 604 is specifically used to control the vehicle to drive along the target parking trajectory when the detection result indicates that the vehicle will not collide with obstacles while driving along the target parking trajectory.

[0193] As another implementation of this application, in order to accurately determine whether the vehicle has reached the target parking space, the device 600 may further include:

[0194] The second acquisition module is used to acquire vehicle speed and location information when the vehicle is traveling along the target parking trajectory.

[0195] The second determining module is used to determine that the vehicle has reached the target parking space when the vehicle speed information corresponds to a vehicle speed that is less than a preset vehicle speed threshold and the position error between the position information and the second orientation information is less than a preset threshold range.

[0196] Figure 7 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0197] An electronic device may include a processor 701 and a memory 702 storing computer program instructions.

[0198] Specifically, the processor 701 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0199] Memory 702 may include mass storage for data or instructions. For example, and not limitingly, memory 702 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 702 may include removable or non-removable (or fixed) media. Where appropriate, memory 702 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 702 is non-volatile solid-state memory.

[0200] In a particular embodiment, memory 702 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0201] The processor 701 reads and executes computer program instructions stored in the memory 702 to implement any of the vehicle parking control methods in the above embodiments.

[0202] In one example, the electronic device may also include a communication interface 703 and a bus 710. For example, Figure 7 As shown, the processor 701, memory 702, and communication interface 703 are connected through bus 710 and complete communication with each other.

[0203] The communication interface 703 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0204] Bus 710 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 710 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0205] The electronic device can execute the vehicle parking control method in the embodiments of this application, thereby achieving the combination of Figure 1 and Figure 6 The vehicle parking control method and device described.

[0206] Furthermore, in conjunction with the vehicle parking control methods in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the vehicle parking control methods in the above embodiments.

[0207] In conjunction with the vehicle parking control methods in the above embodiments, this application embodiment can provide a computer program product, in which the instructions of the computer program product, when executed by the processor of an electronic device, cause the electronic device to perform any of the above vehicle parking control methods.

[0208] Based on the vehicle parking control method described in the above embodiments, this application embodiment can provide a vehicle to implement it. The vehicle includes at least one of the following: the vehicle parking control device as described above; the electronic device as described above; the computer-readable storage medium as described above; and the computer program product as described above.

[0209] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0210] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0211] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0212] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0213] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A vehicle parking control method, characterized in that, include: Obtain the first location information and parking drivable area information of the vehicle, wherein the parking drivable area information includes the second location information of the target parking space; Based on the first location information and the parking drivable area information, determine the third location information of the parking trajectory segment points; Based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information, a target parking trajectory for the vehicle is generated. The target parking trajectory is used to indicate that the vehicle travels from the position corresponding to the first orientation information to the position corresponding to the third orientation information, and then travels to the position corresponding to the second orientation information. Control the vehicle to drive along the target parking trajectory.

2. The method according to claim 1, characterized in that, The step of determining the third location information of the parking trajectory segment points based on the first location information and the parking drivable area information includes: Based on the first heading sub-information in the first orientation information and the second heading sub-information in the second orientation information, the relative direction information of the vehicle relative to the target parking space is determined; Based on the relative direction information, determine the third heading sub-information of the parking trajectory segment points; Based on the parking drivable area information and the third heading sub-information, the third position sub-information of the parking trajectory segment point is determined. The third position sub-information includes the third heading sub-information and the third position sub-information.

3. The method according to claim 2, characterized in that, The step of determining the third position sub-information of the parking trajectory segment points based on the parking drivable area information and the third heading sub-information includes: Based on the parking drivable area information, the area associated with the target parking space in the direction corresponding to the third heading sub-information is discretized to obtain the location sub-information of multiple discrete points; Based on the second position sub-information in the second orientation information and the position sub-information of each discrete point, generate the trajectory curve of each discrete point; The position sub-information of the discrete point corresponding to the optimal trajectory curve among the multiple trajectory curves is determined as the third position sub-information of the parking trajectory segment point, and the optimal trajectory curve is the trajectory curve corresponding to the minimum evaluation value among the multiple trajectory curves.

4. The method according to claim 3, characterized in that, The trajectory curve includes path length, minimum turning radius, path curvature, and path deviation. Determining the position sub-information of the discrete point corresponding to the optimal trajectory curve among multiple trajectory curves as the third position sub-information of the parking trajectory segment point includes: The evaluation value of each trajectory curve is determined based on its path length, minimum turning radius, curvature, and deviation. Among the multiple trajectory curves, the trajectory curve corresponding to the minimum evaluation value is determined as the optimal trajectory curve; The position sub-information of the discrete points corresponding to the optimal trajectory curve is determined as the third position sub-information of the parking trajectory segment points.

5. The method according to claim 1, characterized in that, The step of generating the target parking trajectory of the vehicle based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information includes: Based on the first orientation information, the third orientation information, and the preset minimum turning radius of the vehicle, a first trajectory of the vehicle is generated. The first trajectory is the trajectory from the position corresponding to the first orientation information to the position corresponding to the third orientation information. Based on the second azimuth information, the third azimuth information, and the minimum turning radius, a second trajectory of the vehicle is generated. The second trajectory is the trajectory from the position corresponding to the third azimuth information to the position corresponding to the second azimuth information. By concatenating the first trajectory with the second trajectory, the target parking trajectory of the vehicle is obtained.

6. The method according to claim 1, characterized in that, Before controlling the vehicle to travel along the target parking trajectory, the method further includes: If there are obstacles in the parking drivable area information, obtain the location information and image information of the obstacles; Based on the image information, the obstacle type of the obstacle is identified, and the obstacle type is used to characterize whether the obstacle is a stationary obstacle or a moving obstacle; Collision detection is performed based on the location information of the obstacle, the target parking trajectory, and the expansion coefficient corresponding to the obstacle type, and a detection result is generated. The detection result is used to characterize whether the vehicle will collide with the obstacle when it travels along the target parking trajectory. Different obstacle types correspond to different expansion coefficients. Controlling the vehicle to travel according to the target parking trajectory includes: If the detection result indicates that the vehicle will not collide with the obstacle when driving along the target parking trajectory, the vehicle is controlled to drive along the target parking trajectory.

7. The method according to claim 1, characterized in that, After controlling the vehicle to travel along the target parking trajectory, the method further includes: Obtain the vehicle speed and location information when the vehicle travels along the target parking trajectory; If the vehicle speed corresponding to the vehicle speed information is less than a preset vehicle speed threshold, and the position error between the position information and the second orientation information is less than a preset threshold range, then the vehicle is determined to have arrived at the target parking space.

8. A vehicle parking control device, characterized in that, The device includes: The first acquisition module is used to acquire the first location information and parking drivable area information of the vehicle, wherein the parking drivable area information includes the second location information of the target parking space; The first determining module is used to determine the third location information of the parking trajectory segment points based on the first location information and the parking drivable area information; The generation module is used to generate a target parking trajectory for the vehicle based on the preset minimum turning radius of the vehicle, the first orientation information, the second orientation information, and the third orientation information. The target parking trajectory is used to instruct the vehicle to travel from the position corresponding to the first orientation information to the position corresponding to the third orientation information, and then to the position corresponding to the second orientation information. The control module is used to control the vehicle to drive according to the target parking trajectory.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the vehicle parking control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the vehicle parking control method as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the vehicle parking control method as described in any one of claims 1-7.

12. A vehicle, characterized in that, Includes at least one of the following: The vehicle parking control device as described in claim 8; The electronic device as described in claim 9; The computer-readable storage medium as claimed in claim 10; The computer program product as described in claim 11.

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