Parking scene-based path planning method and device, equipment and storage medium

Abstract

This invention relates to a path planning method, apparatus, device, and storage medium based on parking scenarios, comprising: acquiring parking space information of a parking space to be parked, vehicle information, and environmental information of the environment where the parking space is located; determining the parking scenario category of the parking space to be parked based on the parking space information; wherein the parking scenario category characterizes the ease or difficulty of the vehicle parking in the parking space to be parked; determining a parking path based on the parking scenario category, the vehicle information, and the environmental information; wherein the parking path is used to instruct the vehicle to travel from its current position to the parking space to be parked. This invention enables the targeted calculation of parking paths in different scenarios, saving computing resources and improving the efficiency of path planning.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and specifically to a path planning method, apparatus, device, and storage medium based on parking scenarios. Background Technology

[0002] With the development of autonomous driving technology, people have increasingly higher requirements for automatic parking functions. Typically, sensors such as lasers and radar are installed on vehicles to acquire information about the surrounding environment, and then calculate the parking path based on this information.

[0003] However, vehicles often need to park in different environments. How to plan parking paths in different environments, improve parking efficiency in different environments, and save computing resources are urgent problems to be solved. Summary of the Invention

[0004] This invention provides a path planning method, apparatus, device, and storage medium based on parking scenarios to improve vehicle parking efficiency and save computing resources.

[0005] In a first aspect, the present invention provides a path planning method based on parking scenarios, comprising:

[0006] Obtain parking space information, vehicle information, and environmental information of the parking space;

[0007] Based on the parking space information, the parking scenario category of the waiting parking space is determined; wherein, the parking scenario category represents the ease or difficulty of parking a vehicle in the waiting parking space;

[0008] A parking path is determined based on the parking scenario category, the vehicle information, and the environmental information; wherein, the parking path is used to instruct the vehicle to travel from its current location to the parking space to be parked.

[0009] Secondly, the present invention provides a path planning device based on a parking scenario, comprising:

[0010] The information acquisition module is used to acquire parking space information, vehicle information, and environmental information of the parking space.

[0011] The category determination module is used to determine the parking scenario category of the parking space to be parked based on the parking space information; wherein, the parking scenario category represents the ease or difficulty of a vehicle parking in the parking space to be parked.

[0012] The path determination module is used to determine a parking path based on the parking scenario category, the vehicle information, and the environmental information; wherein, the parking path is used to instruct the vehicle to travel from its current position to the parking space to be parked.

[0013] Thirdly, the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0014] The memory stores computer-executed instructions;

[0015] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect.

[0017] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0018] In a sixth aspect, the present invention provides a vehicle for performing the method described in the first aspect.

[0019] This invention provides a path planning method, apparatus, device, and storage medium based on parking scenarios. By acquiring parking space information such as the type of the parking space to be parked, the ease or difficulty of parking a vehicle in the parking space can be determined, thus obtaining the parking scenario category. For example, for the first type of parking space, such as horizontal, perpendicular, and inclined parking spaces, the shape of the parking space is relatively easy for vehicles to park. Combined with the surrounding environmental information, the parking scenario can be further judged as simple or complex. If the passable space around the parking space is large, the parking scenario can be considered simple; if the passable space is small, the parking scenario can be considered complex. This allows for different parking scenario categories to be determined for the same type of parking space in different environments, improving the accuracy of parking scenario category determination. For the second type of parking space, such as inclined parking spaces, the shape of the parking space is difficult for vehicles to park, and the parking scenario can be directly considered complex without further environmental judgment, improving the efficiency of parking scenario category determination and thus improving parking efficiency. By combining the two dimensions of parking space type and parking scenario category, the accuracy of determining the parking scenario category is improved, thereby improving parking accuracy and enhancing the user experience. Parking scenario categories can differentiate the complexity of the environment. For different parking scenario categories, parking paths are determined based on vehicle information and environmental information. This achieves coverage of different parking scenarios, enabling the use of different path planning methods in different scenarios, improving the efficiency and accuracy of path planning. It solves the problems of wasted computing resources and low path planning efficiency caused by using the same path planning method in existing technologies, effectively saving costs and improving the adaptability of automatic parking. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] Figure 1 A flowchart illustrating a path planning method based on a parking scenario provided in an embodiment of the present invention;

[0022] Figure 2 A flowchart illustrating a path planning method based on a parking scenario provided in an embodiment of the present invention;

[0023] Figure 3(a) is a schematic diagram of the forward inclined train position provided in an embodiment of the present invention;

[0024] Figure 3(b) is a schematic diagram of the reverse inclined train position provided in an embodiment of the present invention;

[0025] Figure 4(a) is a schematic diagram of the obstacle distribution corresponding to the horizontal parking space provided in the embodiment of the present invention;

[0026] Figure 4(b) is a schematic diagram of the obstacle distribution corresponding to the vertical parking space provided in the embodiment of the present invention;

[0027] Figure 5 A flowchart illustrating a path planning method based on a parking scenario provided in an embodiment of the present invention;

[0028] Figure 6 This is an architecture diagram of the parking path planning system provided in an embodiment of the present invention;

[0029] Figure 7 A structural block diagram of a path planning device based on a parking scenario provided in an embodiment of the present invention;

[0030] Figure 8 A structural block diagram of a path planning device based on a parking scenario provided in an embodiment of the present invention;

[0031] Figure 9 A structural block diagram of an electronic device provided in an embodiment of the present invention;

[0032] Figure 10 This is a structural block diagram of an electronic device provided in an embodiment of the present invention.

[0033] The accompanying drawings have illustrated specific embodiments of the invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0034] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0035] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0036] In the description of this invention, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. Furthermore, in the description of this invention, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0037] It should be noted that, due to space limitations, this specification does not exhaustively list all possible implementation methods. Those skilled in the art, after reading this specification, should be able to deduce that any combination of technical features can constitute an optional implementation method, provided that the technical features do not contradict each other. The following provides a detailed description of each embodiment.

[0038] The development prospects of autonomous driving technology are very broad. Advanced autonomous driving technology can improve driving safety, increase traffic efficiency, reduce congestion, and enhance comfort. Automated parking technology is an important research area in autonomous driving. Vehicles can be equipped with devices such as LiDAR, cameras, and domain control chips to plan parking paths.

[0039] Parking scenarios are complex and varied, including end-point roads and narrow lanes. The computational power required to calculate parking paths may differ depending on the scenario. For instance, in simple road scenarios, geometric path planning algorithms can yield optimal parking paths; however, in complex road scenarios, pre-defined artificial intelligence algorithms are needed, consuming more computational power to calculate accurate parking paths. Currently, the same algorithm is used for path planning across different road scenarios. Using simple geometric planning algorithms results in low efficiency and accuracy in determining parking paths, impacting parking success rates; while using artificial intelligence algorithms consumes significant resources, hindering cost reduction and efficiency improvement. Current parking path planning methods cannot improve parking scenario coverage, parking success rates, or the adaptability and efficiency of parking path planning under low-resource conditions.

[0040] The present invention provides a path planning method, apparatus, device and storage medium based on parking scenarios, which aims to solve the above-mentioned technical problems of the prior art.

[0041] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0042] Figure 1 This is a flowchart illustrating a path planning method based on a parking scenario according to an embodiment of the present invention. This method can be executed by a path planning device based on a parking scenario. Figure 1 As shown, the method includes the following steps:

[0043] S101. Obtain parking space information, vehicle information, and environmental information of the parking space.

[0044] For example, a parking space to be parked is the space where the vehicle needs to park; that is, the vehicle needs to drive from its current location to the parking space to park. The vehicle can obtain real-time information about the parking space, including its shape, size, orientation, and location. The vehicle can be equipped with a camera to capture and identify images of the parking space, thus obtaining the parking space information. The vehicle can also obtain its own information in real-time, as its own vehicle information. This vehicle information can represent the vehicle's location and driving status. For example, vehicle information can be obtained by inputting CAN signals from the chassis.

[0045] After identifying a parking space, environmental information about the area surrounding that space can be obtained. This information can characterize the road layout and the distribution of obstacles around the parking space. For example, environmental information can be acquired through devices such as cameras and ultrasonic radar.

[0046] Parking space information, vehicle information, and environmental information can be collected from various sensors on the vehicle, such as vehicle positioning, obstacle detection signals, and ultrasonic radar detection signals. Real-time acquisition of decision-making information from the vehicle's state machine system and control feedback information is also possible. The state machine system's decision-making information represents the decisions or instructions made by the vehicle's state machine regarding vehicle movement, while the control feedback information represents the feedback results obtained from executing those decisions or instructions. A unified processing mechanism can be implemented for parking space information, vehicle positioning information, obstacle detection information, ultrasonic radar detection signals, CAN signals input from the chassis, state machine system decision-making information, and control feedback information. This information can be aggregated or converted into different formats to facilitate subsequent data processing and improve the efficiency and accuracy of path planning.

[0047] In this embodiment, obtaining parking space information of the parking space to be parked includes: responding to a parking space selection command issued by the user, determining the parking space to be parked; acquiring image data of the parking space to be parked based on the image acquisition device on the vehicle; and performing recognition processing on the image data of the parking space to be parked to obtain the parking space information of the parking space to be parked.

[0048] Specifically, when a user needs the vehicle to park, they can issue a parking command, and the vehicle will activate its parking function. The vehicle is equipped with cameras that capture environmental images, which may include multiple parking spaces. These images are then displayed on the vehicle's infotainment screen, allowing the user to view each parking space.

[0049] Users can interact with the vehicle's infotainment screen. For example, a user can use touch controls on the screen to select a parking space, and the selected space becomes the designated parking spot. In other words, the system responds to the user's parking space selection command on the screen and confirms the parking space. The vehicle can also display captured environmental images on a device linked to the vehicle, such as the user's personal mobile device. The user can then issue a parking space selection command from their mobile device to confirm the parking spot.

[0050] After identifying a parking space, the vehicle's camera captures images of that space, determining its image data. This image data includes the complete parking space and the surrounding area. A pre-defined image recognition algorithm processes the image data to identify the parking space and determine its information. For example, it can identify a rectangular frame in the image data and determine its size, orientation, and position as the parking space information.

[0051] The advantage of this setup is that, when the parking function is activated, the user's desired parking space is determined through interaction with the vehicle's infotainment system, thereby meeting the user's actual parking needs and improving the user experience.

[0052] S102. Based on the parking space information, determine the parking scenario category of the parking space to be parked; wherein, the parking scenario category represents the ease or difficulty of parking a vehicle in the parking space to be parked.

[0053] For example, parking space information may include the type, orientation, location, and size of the parking space. Based on this information, different parking scenario categories can be distinguished. Parking scenario categories can characterize the ease or difficulty of parking a vehicle in the designated parking space. For instance, parking scenario categories may include general scenarios and complex scenarios. General scenarios indicate relatively low parking difficulty, while complex scenarios indicate a complex environment and higher parking difficulty. For example, based on the parking space information, if it can be determined that the space around the parking space is large, the parking space is parallel to the vehicle, and the vehicle can easily drive into the parking space, then the parking scenario category can be considered a general scenario.

[0054] Different relationships between parking space information and parking scenario categories can be preset. For example, the parking space information includes the number of obstacles around the parking space, with different numbers corresponding to different parking scenario categories. If there are many obstacles, the parking scenario category is determined to be a complex scenario; if there are few obstacles, the parking scenario category is determined to be a general scenario.

[0055] S103. Determine the parking path based on the parking scenario category, vehicle information, and environmental information; wherein, the parking path is used to indicate the vehicle's movement from its current location to the parking space to be parked.

[0056] For example, different methods can be used to calculate parking paths under different parking scenario categories. For instance, the association between different parking scenario categories and path planning algorithms can be pre-defined. After determining the parking scenario category, the corresponding path planning algorithm is determined. Based on vehicle information and environmental information, the output parking path is obtained based on the path planning algorithm. In this embodiment, the pre-defined path planning algorithm is not specifically limited. After obtaining the parking path, the vehicle can be instructed to automatically drive from its current position to the parking space according to the parking path to complete the parking process. Alternatively, the parking path can be displayed on the vehicle's infotainment screen, allowing the driver to control the vehicle to park according to the path.

[0057] In this embodiment, parking scenarios can be categorized into a first scenario category and a second scenario category. The first scenario category represents easy parking scenarios, while the second scenario category represents complex parking scenarios that are more difficult to park in. Different scenario categories can correspond to different path planning algorithms. For simple, general scenarios, a low-resource-consuming geometric planning algorithm can be used, while for complex scenarios, a more resource-intensive but more accurate hybrid A* algorithm can be used. The algorithm corresponding to the first scenario category is designated as the first planning algorithm, and the algorithm corresponding to the second scenario category is designated as the second planning algorithm. Since the second planning algorithm consumes more resources, when invoking the second planning algorithm, the preset sensors on the vehicle, such as cameras, can be temporarily stopped. Once the parking path is determined, the preset sensors on the vehicle can resume operation to reduce resource waste and achieve path planning with low resource consumption.

[0058] In this embodiment, the method further includes: performing a format conversion process on the parking path to obtain a parking path in a preset format, and displaying the parking path in the preset format on the vehicle's infotainment screen.

[0059] Specifically, different path planning algorithms can be used for different parking scenario categories, and the formats of the parking paths obtained by different algorithms may vary. The parking paths obtained by the algorithms can be standardized in format to facilitate parking control. A target format can be pre-set, and the parking path can be converted into the target format and displayed on the vehicle's infotainment screen. For example, the target format could be in the form of discrete points.

[0060] The vehicle's lateral and longitudinal actuators can control the vehicle to move along a parking path based on a target parking pattern. The lateral and longitudinal actuators are the lateral and longitudinal actuators on the vehicle used to control movement; for example, they could be a steering wheel, gear shifting device, etc.

[0061] The advantage of this setup is that it enables post-processing of parking paths, achieving a unified parking path format, facilitating the control of the vehicle by its lateral and longitudinal actuators, and improving parking accuracy and efficiency.

[0062] In this embodiment of the invention, by acquiring the parking space information of the waiting parking space, the ease or difficulty of parking a vehicle in the waiting parking space can be determined, thus obtaining a parking scenario category. That is, the complexity of the environment is distinguished by the parking scenario category. For different parking scenario categories, a parking path is determined based on the vehicle's own information and environmental information. This achieves coverage of different parking scenarios, thereby enabling the use of different path planning methods in different scenarios, improving the efficiency and accuracy of path planning. It solves the problems of wasted computing resources and low path planning efficiency caused by using the same path planning method in existing technologies, effectively saving costs and improving the adaptability of automatic parking.

[0063] Figure 2 This is a flowchart illustrating a path planning method based on a parking scenario, which is an optional embodiment based on the above embodiments.

[0064] In this embodiment, the parking space information includes the parking space type of the parking space to be parked; based on the parking space information, the parking scenario category of the parking space to be parked is determined, including: if the parking space type of the parking space to be parked is a preset first type, then the parking scenario category of the parking space to be parked is determined based on the environmental information; wherein, the preset first type is a horizontal parking space, a vertical parking space, or a forward-sloping parking space, and the parking scenario category is a first scenario category or a second scenario category, wherein the parking difficulty represented by the first scenario category is lower than the parking difficulty represented by the second scenario category; if the parking space type of the parking space to be parked is a preset second type, then the parking scenario category of the parking space to be parked is determined to be the second scenario category; wherein, the preset second type is a reverse-sloping parking space.

[0065] like Figure 2 As shown, the method includes the following steps:

[0066] S201. Obtain parking space information, vehicle information, and environmental information of the parking space.

[0067] For example, this step can refer to step S101 above, and will not be repeated here.

[0068] S202. If the parking space type of the parking space to be parked is the preset first type, then the parking scenario category of the parking space to be parked is determined according to the environmental information; wherein, the preset first type is a horizontal parking space, a perpendicular parking space, or a forward-sloping parking space, and the parking scenario category is either the first scenario category or the second scenario category, and the parking difficulty represented by the first scenario category is lower than that represented by the second scenario category.

[0069] For example, the parking space information may include the type of parking space to be parked. The parking space type may include horizontal parking spaces, perpendicular parking spaces, angled parking spaces, etc. Angled parking spaces may include forward-facing angled parking spaces and reverse-facing angled parking spaces. A forward-facing angled parking space indicates that the vehicle's driving direction forms an acute angle with the parking space's orientation, while a reverse-facing angled parking space indicates that the vehicle's driving direction forms an obtuse angle with the vehicle's orientation. Figure 3(a) is a schematic diagram of a forward-facing angled parking space, and Figure 3(b) is a schematic diagram of a reverse-facing angled parking space. In Figures 3(a) and 3(b), the dashed arrows represent the vehicle's driving direction, and the solid arrows represent the parking space's orientation.

[0070] The parking space type is obtained from the parking space information. There are two pre-defined types: a first type, a second type, and two types. Horizontal, perpendicular, and diagonally aligned parking spaces belong to the first type, while diagonally aligned parking spaces belong to the second type. If the parking space to be parked is determined to be of the first type, the parking scenario category can be determined based on the known environmental information. The parking scenario category can include both the first and second scenario categories. The parking difficulty represented by the first scenario category is lower than that represented by the second scenario category; that is, the first scenario category represents a general scenario, and the second scenario category represents a complex scenario.

[0071] The environmental information can include information about the distribution of obstacles around the parking space. Based on this information, the parking scenario category can be determined. For example, if there are few obstacles, the parking scenario category is determined to be the first scenario category; if there are many obstacles, the parking scenario category is determined to be the second scenario category.

[0072] In this embodiment, the environmental information includes the location information of obstacles; determining the parking scene category of the parking space based on the environmental information includes: determining a target border from the border of the parking space based on the location information of obstacles; determining the vertical distance between the obstacle and the target border; determining the parking scene category of the parking space based on the border category of the target border and the vertical distance between the obstacle and the target border; wherein, the border category is a long border or a short border.

[0073] Specifically, the environment may include multiple obstacles, and the environmental information may include the location information of each obstacle. The parking space is a quadrilateral bounding box, meaning it has four borders. For each obstacle in the environment, based on its location information, one border from the four borders of the parking space is selected as the target border for that obstacle. The vertical distance between the obstacle and its corresponding target border is then determined.

[0074] The four borders of a parking space can be divided into two categories: long borders and short borders. For each obstacle, the border category of the corresponding target border is determined. Based on the border categories of each target border and the vertical distance between each obstacle and its corresponding target border, the parking scenario category of the parking space is determined. For example, different distance judgment conditions can be preset for different border categories to determine whether the vertical distance between the obstacle and its corresponding target border meets the preset distance judgment conditions. If the vertical distance between each obstacle and its corresponding target border meets the preset distance judgment conditions, the parking scenario category is considered a complex scenario; otherwise, it is considered a general scenario.

[0075] The advantage of this setup is that it determines the distribution of obstacles around the parking space, obtaining the passable distance (vertical distance) between the obstacle and the target boundary. Based on the passable distance and different boundary types, the parking scenario category is comprehensively determined, improving the accuracy of parking path determination and thus enhancing parking safety.

[0076] In this embodiment, there are a first obstacle and a second obstacle in the environment where the parking space is located. The target border corresponding to the first obstacle is a long border, and the target border corresponding to the second obstacle is a short border. The parking scene category of the parking space is determined based on the border category of the target border and the vertical distance between the obstacle and the target border. This includes: determining a distance threshold corresponding to the border category of the target border based on a preset first association relationship; wherein the preset first association relationship represents the association relationship between the border category and the distance threshold; if the vertical distance between the first obstacle and the corresponding long border is equal to or greater than the distance threshold corresponding to the long border, or if the vertical distance between the second obstacle and the corresponding short border is equal to or greater than the distance threshold corresponding to the short border, then the parking scene category of the parking space is determined to be the first scene category; if the vertical distance between the first obstacle and the corresponding long border is less than the distance threshold corresponding to the long border, and the vertical distance between the second obstacle and the corresponding short border is less than the distance threshold corresponding to the long border, then the parking scene category of the parking space is determined to be the second scene category.

[0077] Specifically, the environment surrounding the parking space contains multiple obstacles, and the target borders for different obstacles can be different. Obstacles with longer borders can be designated as first obstacles, and obstacles with shorter borders as second obstacles. There can be multiple first obstacles and multiple second obstacles.

[0078] A pre-defined first association relationship is established, representing the association between different border categories and distance thresholds. For example, the distance threshold corresponding to a long border is 3.5 meters, and the distance threshold corresponding to a short border is 2.5 meters. Different first association relationships can be set for different parking space types. For example, for horizontal parking spaces, the distance threshold corresponding to a long border is 3.5 meters, and the distance threshold corresponding to a short border is 2.5 meters; for perpendicular parking spaces and inclined parking spaces, the distance threshold corresponding to a long border is 3 meters, and the distance threshold corresponding to a short border is 1.5 meters.

[0079] For the first obstacle, determine the distance threshold corresponding to the long border, determine the vertical distance between the first obstacle and the corresponding long border, and determine whether the vertical distance is equal to or greater than the distance threshold. For the second obstacle, determine the distance threshold corresponding to the short border, determine the vertical distance between the second obstacle and the corresponding short border, and determine whether the vertical distance is equal to or greater than the distance threshold. If the vertical distance between the first obstacle and the corresponding long border is equal to or greater than the distance threshold corresponding to the long border, or if the vertical distance between the second obstacle and the corresponding short border is equal to or greater than the distance threshold corresponding to the short border, then the parking scene category of the parking space is determined to be the first scene category, i.e., a general scene. If the vertical distance between the first obstacle and the corresponding long border is less than the distance threshold corresponding to the long border, and the vertical distance between the second obstacle and the corresponding short border is less than the distance threshold corresponding to the short border, then the parking scene category of the parking space is determined to be the second scene category, i.e., a complex scene.

[0080] Figure 4(a) shows the obstacle distribution for a horizontal parking space, and Figure 4(b) shows the obstacle distribution for a vertical parking space. Obstacle A is the first obstacle, and obstacle B is the second obstacle. For Figure 4(a), if the parking space is horizontal, the criteria for determining the parking scenario category can include: 1. Whether there are obstacles outside the short border of the parking space; 2. Whether the passable space outside the long border of the parking space is less than 3.5m; 3. Whether the passable space outside the short border of the parking space is less than 2.5m. If conditions 1 and 2, or conditions 2 and 3, or conditions 1 and 3 are satisfied simultaneously, the parking scenario category can be determined as a complex scenario; otherwise, it is a general scenario. For Figure 4(b), if the parking space is vertical or diagonally aligned, the criteria for determining the parking scenario category can include: 1. Whether there are obstacles outside the long border of the parking space; 2. Whether the passable space outside the short border of the parking space is less than 1.5m; 3. Whether the passable space outside the long border of the parking space is less than 3m. If conditions 1 and 3 are met simultaneously, or condition 2 is met, then the parking scenario can be classified as a complex scenario; otherwise, it is classified as a general scenario.

[0081] The advantage of this design is that the long and short borders correspond to different safety distances, improving the accuracy of parking scenario classification and thus enhancing parking safety.

[0082] S203. If the parking space type of the waiting parking space is the preset second type, then the parking scenario category of the waiting parking space is determined to be the second scenario category; wherein, the preset second type is the reverse angled parking space.

[0083] For example, if the parking space type of the parking space to be parked is determined to be a reverse angled parking space, then the parking scenario category of the parking space to be parked can be directly determined to be the second scenario category, that is, a complex scenario.

[0084] S204. Determine the parking path based on the parking scenario category, vehicle information, and environmental information; wherein, the parking path is used to indicate the vehicle's movement from its current location to the parking space to be parked.

[0085] For example, this step can refer to step S103 above, and will not be repeated here.

[0086] In this embodiment of the invention, by acquiring parking space information of the parking space to be parked, the type of the parking space is determined. Based on different parking space types, the ease or difficulty of parking a vehicle in the parking space is determined, thus obtaining a parking scenario category. For example, for the first type of parking space, such as horizontal parking spaces, perpendicular parking spaces, and inclined parking spaces, the shape of the parking space is relatively easy for vehicles to park. The surrounding environmental information can be combined to further determine whether the parking scenario is simple or complex. If the passable space around the parking space is large, the parking scenario can be considered simple; if the passable space around the parking space is small, the parking scenario can be considered complex. This allows for different parking scenario categories to be determined for the same type of parking space in different environments, improving the accuracy of parking scenario category determination. For the second type of parking space, such as inclined parking spaces, the shape of the parking space is difficult for vehicles to park. The parking scenario can be directly considered complex without further environmental judgment, improving the efficiency of parking scenario category determination and thus improving parking efficiency. By combining the two dimensions of parking space type and parking scenario category, the accuracy of determining the parking scenario category is improved, thereby improving parking accuracy and enhancing the user experience. Parking scenario categories can differentiate the complexity of the environment. For different parking scenario categories, parking paths are determined based on vehicle information and environmental information. This achieves coverage of different parking scenarios, enabling the use of different path planning methods in different scenarios, improving the efficiency and accuracy of path planning. It solves the problems of wasted computing resources and low path planning efficiency caused by using the same path planning method in existing technologies, effectively saving costs and improving the adaptability of automatic parking.

[0087] Figure 5 This is a flowchart illustrating a path planning method based on a parking scenario, which is an optional embodiment based on the above embodiments.

[0088] In this embodiment, determining the parking path based on the parking scenario category, vehicle information, and environmental information includes: determining a path planning algorithm corresponding to the parking scenario category based on a preset second association relationship; wherein the preset second association relationship represents the association relationship between the parking scenario category and the path planning algorithm; and determining the parking path based on the vehicle's vehicle information and environmental information, using the path planning algorithm corresponding to the parking scenario category.

[0089] like Figure 5 As shown, the method includes the following steps:

[0090] S501. Obtain parking space information, vehicle information, and environmental information of the parking space.

[0091] For example, this step can refer to step S101 above, and will not be repeated here.

[0092] S502. Based on the parking space information, determine the parking scenario category of the parking space to be parked; wherein, the parking scenario category represents the ease or difficulty of parking a vehicle in the parking space to be parked.

[0093] For example, this step can refer to step S102 above, and will not be repeated here.

[0094] S503. Determine the path planning algorithm corresponding to the parking scenario category based on the preset second association relationship; wherein, the preset second association relationship represents the association relationship between the parking scenario category and the path planning algorithm.

[0095] For example, a second association is pre-defined, which can characterize the relationship between different parking scenario categories and path planning algorithms. For instance, the path planning algorithm corresponding to the first scenario category is a geometric path planning algorithm, and the path planning algorithm corresponding to the second scenario category is a hybrid A* algorithm.

[0096] The current parking scenario category is determined, and a path planning algorithm corresponding to the parking scenario category is determined based on the preset second association relationship. Then, the parking path is calculated based on the path planning algorithm.

[0097] S504. Based on the vehicle information and environmental information, determine the parking path using a path planning algorithm corresponding to the parking scenario category.

[0098] For example, for different path planning algorithms, vehicle information and environmental information can be input into the path planning algorithm, which then outputs a parking path. The path planning algorithm can calculate a path from the vehicle to the parking space while avoiding collisions with obstacles in the environment. In this embodiment, the calculation process of the path planning algorithm is not specifically limited.

[0099] In this embodiment, the parking scenario category is the first scenario category. The vehicle information represents the vehicle's location, and the environmental information includes the location information of obstacles. Based on the vehicle information and the environmental information, a parking path is determined using a path planning algorithm corresponding to the parking scenario category. This includes: determining the location information of the parking space to be parked; and using the vehicle information, the location information of the parking space to be parked, and the location information of obstacles in the environment where the parking space is located, calling a preset first planning algorithm to obtain the parking path.

[0100] Specifically, if the parking scenario category is the first scenario category, i.e., a general scenario, then the first planning algorithm can obtain a better parking path. The first planning algorithm can be a simple geometric path planning algorithm, which does not require a lot of resources and can achieve path planning with low resources.

[0101] Parking space information can include the location of the parking space to be parked, and vehicle information can include the vehicle's location. The location of the parking space to be parked represents the end point of the parking path, and the vehicle's location information represents the start point of the parking path. The geometric path planning algorithm is a single-step planning method. Based on the vehicle's real-time positioning, obstacle location information, and parking space location information, it uses a planning method combining circular arcs and straight lines. While avoiding obstacles, it performs parking step-by-step and shifting operations to ultimately achieve parking. For example, the geometric path planning algorithm uses a method connecting circular arcs and straight lines, where the straight lines are tangent to the circular arcs. By performing obstacle avoidance and collision detection, a geometric parking path from the start point to the end point is planned.

[0102] The advantages of this setup are that using geometric programming to determine the parking path is time-efficient, resource-efficient, and covers all simple scenarios. Furthermore, the geometric path planning algorithm updates the parking path step-by-step, allowing for real-time correction of parking tracking errors and improving parking efficiency and accuracy.

[0103] In this embodiment, the parking scenario category is the second scenario category. Based on the vehicle information and environmental information, and using the path planning algorithm corresponding to the parking scenario category, the parking path is determined, including: based on the vehicle information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset second planning algorithm is invoked to obtain an initial path; the initial path is smoothed to obtain the parking path.

[0104] Specifically, if the parking scenario falls under the second scenario category, i.e., a complex scenario, the second planning algorithm is used to obtain a better parking path. The second planning algorithm can be a hybrid A* algorithm. The hybrid A* algorithm requires more resources, but the path planning accuracy is higher and it can cover more scenarios.

[0105] The vehicle's location information, the parking space's location information, and the location information of each obstacle can be input into the hybrid A* algorithm to obtain the output path. The path output by the hybrid A* algorithm is determined as the initial path. In this embodiment, the calculation process of the hybrid A* algorithm is not specifically limited. For example, the hybrid A* algorithm can map a grid map of obstacles, expand and grow search nodes in the grid map, and consider the vehicle's dynamics information and cost function in the direction of the search nodes. After each node expansion, RS (Reeds-Shepp) curves are used to connect the nodes. Finally, given the start and end points, a hybrid A* parking path with continuous curvature that avoids obstacles is searched, which is the initial path.

[0106] The hybrid A* algorithm is used for full path planning. Based on the current location information of the vehicle and the location information of the parking space, and using a grid map containing obstacle information, it performs an expanded search of hybrid A* nodes and RS curve stitching to obtain an initial path from the starting point to the ending point. Then, collision detection and smoothing are applied to the initial path to obtain a parking path that is easy to follow. Collision detection rules and smoothing methods can be preset; in this embodiment, no specific limitations are made on the collision detection rules and smoothing methods.

[0107] The advantage of this setup is that the hybrid A* algorithm is highly flexible and can solve and cover parking problems in complex scenarios. By combining geometric path planning algorithms and the hybrid A* algorithm, the coverage of parking scenarios is improved, thereby increasing the parking success rate and enhancing the user experience.

[0108] In this embodiment, the parking scenario category is the first scenario category; the method further includes: based on the vehicle's own information, the location information of the parking space to be parked, and the location information of obstacles in the environment where the parking space is located, calling a preset first planning algorithm to generate a path planning result identifier; wherein, the path planning result identifier indicates whether a parking path has been generated; if it is determined that the path planning result identifier indicates that a parking path has not been generated, then based on the vehicle's own information, the location information of the parking space to be parked, and the location information of obstacles in the environment where the parking space is located, calling a preset second planning algorithm to obtain an initial path; and smoothing the initial path to obtain the parking path.

[0109] Specifically, if the parking scenario falls under the first scenario category, the geometric path planning algorithm is prioritized. This means that the vehicle's location information, the location of the parking space, and the location information of obstacles in the environment surrounding the parking space are input into the geometric path planning algorithm. Regardless of whether the geometric path planning algorithm calculates a parking path, it outputs a path planning result identifier. This identifier indicates whether a parking path was generated; for example, 0 indicates no parking path was generated, and 1 indicates a successful parking path generation.

[0110] If a parking path is successfully generated, automatic parking can be achieved. If the path planning results indicate that a parking path was not successfully generated, the hybrid A* algorithm can be invoked to recalculate the parking path. Specifically, the vehicle's location information, the location of the parking space, and the location information of obstacles in the environment surrounding the parking space can be input into the hybrid A* algorithm to obtain an initial path. This initial path is then smoothed to obtain the final parking path.

[0111] The advantage of this setup is that if the current path planning is for the first time and belongs to the first scene category, the geometric path planning algorithm can be invoked; if the current path planning is for the second scene category, the hybrid A* algorithm can be invoked. When invoking the geometric path planning algorithm, the path planning results can be monitored continuously. If the geometric path planning algorithm reports an error, the hybrid A* algorithm will be invoked again. That is, if the current path planning is not for the first time and the scene is classified as the first scene category, but the geometric path planning fails, the hybrid A* algorithm will be switched to cover scenes that the geometric parking path planning cannot cover, thus improving the parking success rate.

[0112] In this embodiment, the method further includes: if it is determined that a preset second planning algorithm will be invoked, then the preset sensors on the vehicle will be controlled to stop operating; if it is determined that a parking path has been obtained, then the preset sensors on the vehicle will be controlled to continue operating.

[0113] Specifically, the second planning algorithm consumes significant resources. To achieve parking with lower resource consumption, when the second planning algorithm is invoked, preset sensors on the vehicle can be temporarily shut down. For example, preset sensors could be cameras, radar, or other devices; these devices can be controlled to temporarily stop uploading image data. In other words, the parking perception and other modules in the domain controller are paused, maintaining only the scheduling processing of the current frame output, thus freeing up resource space for the second planning algorithm.

[0114] Once the second planning algorithm successfully plans a path and is in the parking tracking phase, the pause operation on the preset sensors can be lifted, allowing the preset sensors to continue sensing, detection, and updating, preparing for subsequent parking planning. If the first planning algorithm is invoked, no scheduling work is required for the preset sensors.

[0115] The advantage of this setup is that by scheduling preset sensors, resources and space can be saved, parking path calculations can be ensured smoothly, and the parking success rate can be improved.

[0116] Figure 6 This is an architecture diagram of a parking path planning system. Figure 6 In this system, the vehicle can acquire real-time perception information, chassis information, vehicle state machine information, human-machine interaction information, and parking control information. Perception information may include parking space information, vehicle positioning information, obstacle detection information, and ultrasonic radar detection information. Chassis information consists of CAN signals input from the chassis, and parking control information is feedback information regarding vehicle parking control. All these inputs are processed uniformly; for example, coordinate transformation, unit transformation, and structural transformation can be performed on this information, merging it into a unified signal structure for easier subsequent calculations.

[0117] Based on the above input information, it can be confirmed whether the parking function is activated. For example, based on human-computer interaction information, it can be determined whether the user has issued an automatic parking command; if so, the parking function is activated. If the parking function is activated, the current parking scenario category can be identified based on the input information, and different path planning algorithms can be called according to different parking scenario categories. The parking path is planned according to the called path planning algorithm. When calling the hybrid A* algorithm, preset sensors can be scheduled and controlled to save computing resources. After the algorithm completes the calculation, the parking path can be managed. For example, for geometric parking paths, the rationality of the geometric parking path can be verified, including verifying the tangency of arcs and straight lines, the slope of straight lines, the center point and radius of arcs, etc. A qualified geometric path is assigned a value of 1, and an unqualified path is assigned a value of 0. For paths using the hybrid A* algorithm, path switching and truncation can be performed to facilitate tracking and control. After obtaining the parking path, the parking path is post-processed, and the paths of the two different algorithms are processed into discrete points and output to the lateral and longitudinal actuators for parking tracking and control, and parking control information is fed back in real time.

[0118] In this embodiment of the invention, by acquiring the parking space information of the waiting parking space, the ease or difficulty of parking a vehicle in the waiting parking space can be determined, thus obtaining a parking scenario category. That is, the complexity of the environment is distinguished by the parking scenario category. For different parking scenario categories, a parking path is determined based on the vehicle's own information and environmental information. This achieves coverage of different parking scenarios, thereby enabling the use of different path planning methods in different scenarios, improving the efficiency and accuracy of path planning. It solves the problems of wasted computing resources and low path planning efficiency caused by using the same path planning method in existing technologies, effectively saving costs and improving the adaptability of automatic parking.

[0119] Figure 7 This is a structural block diagram of a path planning device based on a parking scenario provided by an embodiment of the present invention. For ease of explanation, only the parts relevant to the embodiments of this disclosure are shown. (Refer to...) Figure 7 The path planning device 700 based on parking scenarios includes: an information acquisition module 701, a category determination module 702, and a path determination module 703.

[0120] The information acquisition module 701 is used to acquire parking space information of the parking space to be parked, vehicle information of the vehicle, and environmental information of the environment where the parking space is located.

[0121] The category determination module 702 is used to determine the parking scenario category of the parking space to be parked based on the parking space information; wherein, the parking scenario category represents the ease or difficulty of a vehicle parking in the parking space to be parked.

[0122] The path determination module 703 is used to determine a parking path based on the parking scenario category, the vehicle information, and the environmental information; wherein the parking path is used to instruct the vehicle to travel from its current position to the parking space.

[0123] Figure 8 A structural block diagram of a path planning device based on a parking scenario provided in this disclosure embodiment is shown below. Figure 8 As shown, the path planning device 800 based on parking scenarios includes an information acquisition module 801, a category determination module 802, and a path determination module 803. The category determination module 802 includes a first determination unit 8021 and a second determination unit 8022.

[0124] The first determining unit 8021 is used to determine the parking scenario category of the parking space according to the environmental information if the parking space type of the parking space to be parked is a preset first type; wherein the preset first type is a horizontal parking space, a vertical parking space, or a forward-sloping parking space, and the parking scenario category is a first scenario category or a second scenario category, wherein the parking difficulty represented by the first scenario category is lower than the parking difficulty represented by the second scenario category.

[0125] The second determining unit 8022 is used to determine the parking scenario category of the parking space to be the second scenario category if the parking space type of the parking space to be parked is a preset second type; wherein the preset second type is a reverse angled parking space.

[0126] In one example, the environmental information includes the location information of obstacles; the first determining unit 8021 includes:

[0127] The border determination subunit is used to determine the target border from the border of the parking space based on the position information of the obstacle;

[0128] The distance determination subunit is used to determine the vertical distance between the obstacle and the target border.

[0129] The category determination subunit is used to determine the parking scene category of the parking space based on the border category of the target border and the vertical distance between the obstacle and the target border; wherein the border category is a long border or a short border.

[0130] In one example, the environment surrounding the parking space contains a first obstacle and a second obstacle. The target bounding box corresponding to the first obstacle is a long bounding box, and the target bounding box corresponding to the second obstacle is a short bounding box. The category determination subunit is specifically used for:

[0131] Based on a preset first association relationship, a distance threshold corresponding to the border category of the target border is determined; wherein, the preset first association relationship represents the association relationship between the border category and the distance threshold.

[0132] If the vertical distance between the first obstacle and the corresponding long border is equal to or greater than the distance threshold corresponding to the long border, or if the vertical distance between the second obstacle and the corresponding short border is equal to or greater than the distance threshold corresponding to the short border, then the parking scene category of the parking space to be parked is determined to be the first scene category.

[0133] If the vertical distance between the first obstacle and its corresponding long border is less than the distance threshold corresponding to the long border, and the vertical distance between the second obstacle and its corresponding short border is less than the distance threshold corresponding to the long border, then the parking scene category of the parking space to be parked is determined to be the second scene category.

[0134] In one example, the path determination module 803 includes:

[0135] The algorithm determination unit is used to determine the path planning algorithm corresponding to the parking scenario category based on a preset second association relationship; wherein, the preset second association relationship represents the association relationship between the parking scenario category and the path planning algorithm;

[0136] The path determination unit is used to determine the parking path based on the vehicle information and the environmental information, using a path planning algorithm corresponding to the parking scenario category.

[0137] In one example, the parking scenario category is the first scenario category, the vehicle information represents the vehicle's location, and the environmental information includes the location information of obstacles; the path determination unit is specifically used for:

[0138] Determine the location information of the parking space to be parked;

[0139] Based on the vehicle information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset first planning algorithm is invoked to obtain the parking path.

[0140] In one example, the parking scenario category is the second scenario category; the path determination unit is specifically used for:

[0141] Based on the vehicle's own information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset second planning algorithm is invoked to obtain an initial path;

[0142] The initial path is smoothed to obtain the parking path.

[0143] In one example, the parking scenario is classified as the first scenario category; the device also includes:

[0144] The identifier determination module is used to call a preset first planning algorithm to generate a path planning result identifier based on the vehicle information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located; wherein, the path planning result identifier indicates whether a parking path has been generated.

[0145] The algorithm switching module is used to, if it is determined that the path planning result identifier indicates that no parking path has been generated, execute the following steps: calling a preset second planning algorithm based on the vehicle information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located to obtain an initial path; and smoothing the initial path to obtain the parking path.

[0146] One example also includes:

[0147] The sensor scheduling module is used to control the preset sensors on the vehicle to stop operating if it is determined that the preset second planning algorithm will be invoked; and to control the preset sensors on the vehicle to continue operating if the parking path is determined.

[0148] In one example, the information acquisition module 801 is specifically used for:

[0149] In response to the user's parking space selection command, determine the parking space to be used;

[0150] Image data of the parking space to be parked is acquired using the image acquisition device on the vehicle.

[0151] The image data of the parking space to be parked is processed to obtain the parking space information.

[0152] One example also includes:

[0153] The format conversion module is used to perform format conversion processing on the parking path to obtain a parking path in a preset format, and to display the parking path in the preset format on the vehicle's infotainment screen.

[0154] Figure 9 A structural block diagram of an electronic device provided in an embodiment of this application, such as... Figure 9 As shown, the electronic device includes: a memory 91 and a processor 92; the memory 91 is a memory used to store instructions executable by the processor 92.

[0155] The processor 92 is configured to perform the methods provided in the embodiments described above.

[0156] The electronic device also includes a receiver 93 and a transmitter 94. The receiver 93 is used to receive instructions and data sent by other devices, and the transmitter 94 is used to send instructions and data to external devices.

[0157] Figure 10 This is a block diagram illustrating a terminal device according to an exemplary embodiment. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0158] The device 1000 may include one or more of the following components: a processing component 1002, a memory 1004, a power supply component 1006, a multimedia component 1008, an audio component 1010, an input / output (I / O) interface 1012, a sensor component 1014, and a communication component 1016.

[0159] Processing component 1002 typically controls the overall operation of device 1000, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 1002 may include one or more processors 1020 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 1002 may include one or more modules to facilitate interaction between processing component 1002 and other components. For example, processing component 1002 may include a multimedia module to facilitate interaction between multimedia component 1008 and processing component 1002.

[0160] The device 1000 may include one or more of the following components: a processing component 1002, a memory 1004, a power supply component 1006, a multimedia component 1008, an audio component 1010, an input / output (I / O) interface 1012, a sensor component 1014, and a communication component 1016.

[0161] Memory 1004 is configured to store various types of data to support the operation of device 1000. Examples of such data include instructions for any application or method operating on device 1000, contact data, phonebook data, messages, pictures, videos, etc. Memory 1004 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0162] Power supply component 1006 provides power to various components of device 1000. Power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 1000.

[0163] The multimedia component 1008 includes a screen that provides an output interface between the device 1000 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 1008 includes a front-facing camera and / or a rear-facing camera. When the device 1000 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0164] Audio component 1010 is configured to output and / or input audio signals. For example, audio component 1010 includes a microphone (MIC) configured to receive external audio signals when device 1000 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1004 or transmitted via communication component 1016. In some embodiments, audio component 1010 also includes a speaker for outputting audio signals.

[0165] I / O interface 1012 provides an interface between processing component 1002 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0166] Sensor assembly 1014 includes one or more sensors for providing state assessments of various aspects of device 1000. For example, sensor assembly 1014 may detect the on / off state of device 1000, the relative positioning of components such as the display and keypad of device 1000, changes in the position of device 1000 or a component of device 1000, the presence or absence of user contact with device 1000, the orientation or acceleration / deceleration of device 1000, and temperature changes of device 1000. Sensor assembly 1014 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1014 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 1014 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0167] Communication component 1016 is configured to facilitate wired or wireless communication between device 1000 and other devices. Device 1000 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 1016 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1016 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0168] In an exemplary embodiment, the apparatus 1000 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0169] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1004 including instructions, which can be executed by a processor 1020 of the device 1000 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0170] A non-transitory computer-readable storage medium, wherein when the instructions in the storage medium are executed by the processor of a terminal device, the terminal device is able to execute the aforementioned path planning method based on a parking scenario.

[0171] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0172] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A path planning method based on parking scenarios, characterized in that, include: Obtain parking space information, vehicle information, and environmental information of the parking space; Based on the parking space information, the parking scenario category of the waiting parking space is determined; wherein, the parking scenario category represents the ease or difficulty of parking a vehicle in the waiting parking space; A parking path is determined based on the parking scenario category, the vehicle information, and the environmental information; wherein, the parking path is used to instruct the vehicle to travel from its current location to the parking space to be parked. The parking space information includes the parking space type of the waiting parking space; based on the parking space information, the parking scenario category of the waiting parking space is determined, including: If the parking space type of the parking space to be parked is a preset first type, then the parking scenario category of the parking space to be parked is determined according to the environmental information; wherein, the preset first type is a horizontal parking space, a vertical parking space, or a forward-sloping parking space, and the parking scenario category is a first scenario category or a second scenario category, wherein the parking difficulty represented by the first scenario category is lower than the parking difficulty represented by the second scenario category. If the parking space type of the waiting parking space is a preset second type, then the parking scenario category of the waiting parking space is determined to be the second scenario category; wherein, the preset second type is a reverse angled parking space.

2. The method according to claim 1, characterized in that, The environmental information includes the location information of obstacles; Based on the environmental information, the parking scenario category of the parking space to be parked is determined, including: Based on the location information of the obstacle, a target border is determined from the border of the parking space; Determine the vertical distance between the obstacle and the target border; The parking scene category of the parking space is determined based on the border category of the target border and the vertical distance between the obstacle and the target border; wherein the border category is a long border or a short border.

3. The method according to claim 2, characterized in that, The environment surrounding the parking space contains a first obstacle and a second obstacle. The target border corresponding to the first obstacle is a long border, and the target border corresponding to the second obstacle is a short border. Based on the border type of the target borders and the vertical distance between the obstacle and the target borders, the parking scenario category of the parking space is determined, including: Based on a preset first association relationship, a distance threshold corresponding to the border category of the target border is determined; wherein, the preset first association relationship represents the association relationship between the border category and the distance threshold. If the vertical distance between the first obstacle and the corresponding long border is equal to or greater than the distance threshold corresponding to the long border, or if the vertical distance between the second obstacle and the corresponding short border is equal to or greater than the distance threshold corresponding to the short border, then the parking scene category of the parking space to be parked is determined to be the first scene category. If the vertical distance between the first obstacle and its corresponding long border is less than the distance threshold corresponding to the long border, and the vertical distance between the second obstacle and its corresponding short border is less than the distance threshold corresponding to the long border, then the parking scene category of the parking space to be parked is determined to be the second scene category.

4. The method according to claim 1, characterized in that, Based on the parking scenario category, the vehicle information, and the environmental information, a parking path is determined, including: Based on a preset second association relationship, a path planning algorithm corresponding to the parking scenario category is determined; wherein, the preset second association relationship represents the association relationship between the parking scenario category and the path planning algorithm; Based on the vehicle information and the environmental information, the parking path is determined using a path planning algorithm corresponding to the parking scenario category.

5. The method according to claim 4, characterized in that, The parking scenario category is the first scenario category, the vehicle information represents the vehicle's location, and the environmental information includes the location information of obstacles; Based on the vehicle information and the environmental information, and using a path planning algorithm corresponding to the parking scenario category, the parking path is determined, including: Determine the location information of the parking space to be parked; Based on the vehicle information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset first planning algorithm is invoked to obtain the parking path.

6. The method according to claim 4, characterized in that, The parking scenario category is the second scenario category; based on the vehicle information and the environmental information, and using a path planning algorithm corresponding to the parking scenario category, the parking path is determined, including: Based on the vehicle's own information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset second planning algorithm is invoked to obtain an initial path; The initial path is smoothed to obtain the parking path.

7. The method according to claim 6, characterized in that, The parking scenario category is the first scenario category; the method further includes: Based on the vehicle's own information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset first planning algorithm is invoked to generate a path planning result identifier; wherein, the path planning result identifier indicates whether a parking path has been generated. If it is determined that the path planning result indicates that no parking path has been generated, then the following steps are performed: based on the vehicle information, the location information of the parking space, and the location information of obstacles in the environment where the parking space is located, a preset second planning algorithm is invoked to obtain an initial path; the initial path is then smoothed to obtain the parking path.

8. The method according to claim 6 or 7, characterized in that, Also includes: If it is determined that the preset second planning algorithm will be invoked, then the preset sensors on the vehicle will be controlled to stop operating; If the parking path is determined, the preset sensors on the vehicle are controlled to continue operating.

9. The method according to any one of claims 1-7, characterized in that, Retrieve parking space information for available parking spaces, including: In response to the user's parking space selection command, determine the parking space to be used; Image data of the parking space to be parked is acquired using the image acquisition device on the vehicle. The image data of the parking space to be parked is processed to obtain the parking space information.

10. The method according to any one of claims 1-7, characterized in that, Also includes: The parking path is converted to a preset format to obtain a parking path, and the preset format parking path is displayed on the vehicle's infotainment screen.

11. A path planning device based on parking scenarios, characterized in that, include: The information acquisition module is used to acquire parking space information, vehicle information, and environmental information of the parking space. The category determination module is used to determine the parking scenario category of the parking space to be parked based on the parking space information; wherein, the parking scenario category represents the ease or difficulty of a vehicle parking in the parking space to be parked. The path determination module is used to determine a parking path based on the parking scenario category, the vehicle information, and the environmental information; wherein, the parking path is used to instruct the vehicle to travel from its current position to the parking space to be parked. The parking space information includes the type of parking space available for occupancy; The category determination module is specifically used to determine the parking scenario category of the parking space based on the environmental information if the parking space type of the parking space to be parked is a preset first type; wherein, the preset first type is a horizontal parking space, a vertical parking space, or a forward-sloping parking space, and the parking scenario category is a first scenario category or a second scenario category, wherein the parking difficulty represented by the first scenario category is lower than the parking difficulty represented by the second scenario category; If the parking space type of the waiting parking space is a preset second type, then the parking scenario category of the waiting parking space is determined to be the second scenario category; wherein, the preset second type is a reverse angled parking space.

12. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

14. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-10.

15. A vehicle, characterized in that, The vehicle is used to perform the method as described in any one of claims 1-10.

Images