Parking re-planning type judgment method and device, equipment and medium

Abstract

The invention discloses a parking re-planning type judgment method and device, equipment and a medium, and relates to the technical field of intelligent driving. Acquiring obstacle data and environment data when the target vehicle is parked based on the parking path; acquiring vehicle state data and parking data of the target vehicle; the vehicle state data comprises vehicle position information, vehicle driving information, vehicle basic information and gear data; the parking data comprises a parking state and a parking type; and determining a path re-planning type based on the vehicle state data, the parking data, the obstacle data, the environment data and a path re-planning judgment rule. By adopting the technical scheme, through multi-dimensional information acquisition of the vehicle state data and logic judgment based on the path re-planning judgment rule, the obstacle type and the re-planning trigger condition in the parking blocked scene are accurately identified, and the problem of poor path adaptability caused by fuzzy judgment of the existing path re-planning type is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and in particular to a method, apparatus, device, and medium for determining parking replanning type. Background Technology

[0002] Current autonomous driving parking replanning technologies are either single-trigger type, meaning they rely on only a single piece of information to trigger replanning without distinguishing between different types of information; or simplified classification type, meaning they combine at most two types of information to make a replanning decision.

[0003] However, existing parking replanning technologies collect low-dimensional information, have low accuracy in judgment, are prone to misjudgment, and reduce parking efficiency by more than 30%. Moreover, they all adopt full-process replanning, which additionally consumes 20%-40% of the vehicle chip resources, and are prone to lag when multitasking. Overall, they have limited adaptability to various scenarios. In complex environments such as narrow parking spaces and dynamic obstacle scenarios, the parking failure rate exceeds 45%, and there is no unified industry standard, so the solutions are incompatible.

[0004] Therefore, there is an urgent need for a parking replanning type determination method to solve the problem of path planning misjudgment caused by single information. Summary of the Invention

[0005] This invention provides a parking replanning type determination method, device, equipment, and medium to solve the problem of ambiguous path replanning type determination and poor path adaptability in existing methods. By collecting multi-dimensional information from vehicle status data and performing logical judgment based on path replanning determination rules, it accurately identifies the type of obstacle and replanning triggering conditions in parking obstruction scenarios.

[0006] According to one aspect of the present invention, a method for determining parking replanning type is provided, comprising:

[0007] The system collects obstacle data and environmental data when the target vehicle parks based on a parking path; the obstacle data includes data on obstacles in front and obstacles behind.

[0008] Acquire vehicle status data and parking data of the target vehicle; the vehicle status data includes vehicle location information, vehicle driving information, vehicle basic information, and gear data; the parking data includes parking status and parking type;

[0009] Based on the vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules, the path replanning type is determined.

[0010] According to another aspect of the present invention, a parking replanning type determination device is provided, comprising:

[0011] The data acquisition module is used to collect obstacle data and environmental data when the target vehicle parks based on the parking path; the obstacle data includes obstacle data in front and obstacle data behind.

[0012] The acquisition module is used to acquire vehicle status data and parking data of the target vehicle; the vehicle status data includes vehicle driving status, vehicle location information, vehicle driving information, vehicle basic information, and gear data; the parking data includes parking status and parking type.

[0013] The type determination module is used to determine the path replanning type based on the vehicle status data, the parking data, the obstacle data, the environmental data, and the path replanning judgment rules.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the parking replanning type determination method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the parking replanning type determination method according to any embodiment of the present invention.

[0019] This invention, through multi-dimensional information collection of vehicle status data and logical judgment based on path replanning judgment rules, accurately identifies obstacle types and replanning trigger conditions in parking obstruction scenarios, thus solving the problem of ambiguous path replanning type judgment and poor path adaptability in existing methods.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a parking replanning type determination method provided by an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of a parking replanning type determination method provided by an embodiment of the present invention;

[0024] Figure 3 This is a flowchart of a method for determining a path replanning type according to an embodiment of the present invention;

[0025] Figure 4 This is a flowchart of a parking replanning type determination method provided by an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of a parking replanning type determination device according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the parking replanning type determination method according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product or device.

[0030] Furthermore, it should be noted that the information collected in the technical solution of this invention is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data all comply with the relevant laws, regulations and standards of relevant countries and regions, necessary confidentiality measures have been taken, and public order and good morals are not violated. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0031] Figure 1 This is a flowchart of a parking replanning type determination method according to an embodiment of the present invention. This embodiment is applicable to situations where real-time path replanning is performed during vehicle parking, and is particularly suitable for determining the path replanning type when the automatic parking system triggers path replanning in parking obstruction scenarios. This method can be executed by the parking replanning type determination device provided in this embodiment, which can be implemented in hardware and / or software and can be configured in a server. Figure 1 As shown, the method includes:

[0032] S110. Collect obstacle data and environmental data when the target vehicle parks based on the parking path; obstacle data includes data on obstacles in front and obstacles behind.

[0033] The target vehicle is the vehicle performing the parking maneuver; the parking path is the autonomous driving path from the initial position of the target vehicle to the target parking space after the target parking space is determined; obstacle data can be acquired using ultrasonic sensors mounted on the target vehicle; environmental data can be acquired using a 360° holographic imaging system installed on the target vehicle; front obstacle data can include the distances to obstacles detected by ultrasonic sensors at the left front, front, and right front of the target vehicle; rear obstacle data can include the distances to obstacles detected by ultrasonic sensors at the left rear, rear, and right rear of the target vehicle.

[0034] Specifically, after determining the target parking space, the target vehicle will automatically drive according to the planned parking path, and will obtain obstacle data and surrounding environment data during the parking process through ultrasonic and holographic imaging of the target vehicle.

[0035] S120. Obtain vehicle status data and parking data of the target vehicle; vehicle status data includes vehicle location information, vehicle driving information, vehicle basic information and gear data; parking data includes parking status and parking type.

[0036] Among them, vehicle driving status includes driving status or vehicle stopped status; vehicle position information is the current position information of the target vehicle; vehicle driving information is the current angle information of the target vehicle during the parking process; vehicle basic information is the size information of the target vehicle, such as length and width; gear data is the gear status of the target vehicle, which can include forward gear or reverse gear; parking status can be parking in progress or parking completed; parking type can include parallel parking or perpendicular parking.

[0037] Specifically, it acquires real-time status data and parking data of the target vehicle during the parking process, including vehicle driving status, vehicle location information, vehicle driving information, vehicle basic information, and gear data; parking data can include parking status and parking type.

[0038] S130. Based on vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules, determine the path replanning type.

[0039] The path replanning judgment rules can include obstacle judgment rules and deviation judgment rules, and the obstacle judgment rules also include distance thresholds; the deviation judgment rules also include lateral deviation thresholds and heading deviation thresholds; the path replanning type is the strategy type that requires replanning the path when the target vehicle's movement is obstructed.

[0040] Specifically, the path replanning judgment rules are used, along with the collected vehicle status data, obstacle data, and environmental data corresponding to the target vehicle. Based on the judgment rules, the path replanning type when the target vehicle needs to perform path replanning is determined, so as to match appropriate path adjustment strategies for different types.

[0041] This invention collects obstacle data and environmental data of a target vehicle when parking based on a parking path. The obstacle data includes data on obstacles ahead and behind. The invention also acquires vehicle status data and parking data. Vehicle status data includes vehicle driving status, vehicle location information, vehicle driving information, basic vehicle information, and gear data. Parking data includes parking status and parking type. Based on the vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules, the path replanning type is determined. This technical solution, through multi-dimensional information collection of vehicle status data and logical judgment based on path replanning judgment rules, accurately identifies the obstacle type and replanning trigger conditions in parking obstruction scenarios, solving the problem of ambiguous path replanning type judgment and poor path adaptability in existing methods.

[0042] Figure 2This is a flowchart of a parking replanning type determination method according to an embodiment of the present invention. Based on the above embodiments, the obstacle determination rule in this embodiment is a distance threshold. If the vehicle's parking state is "parking in progress," the specific method for determining the path replanning type is supplemented. It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. For example... Figure 2 As shown, the method includes:

[0043] S210. Collect obstacle data and environmental data of the target vehicle when parking based on the parking path; obstacle data includes obstacle data in front and obstacle data behind.

[0044] S220. Obtain vehicle status data and parking data of the target vehicle; vehicle status data includes vehicle location information, vehicle driving information, vehicle basic information and gear data; parking data includes parking status and parking type.

[0045] S230. If the parking status is in progress, then determine the path replanning type based on the gear data, obstacle data, and obstacle judgment rules in the vehicle status data.

[0046] Among them, the obstacle judgment rule can be a distance threshold, that is, the safe environment range around the target vehicle.

[0047] Specifically, if the target vehicle is performing a parking maneuver, the path replanning type is determined based on the gear data and obstacle data in the vehicle status data and a distance threshold.

[0048] Optional, such as Figure 3 The method for determining the type of path replanning shown includes:

[0049] S231. If the gear is in forward gear, the path replanning type is determined based on the obstacle data ahead and the distance threshold in the obstacle data.

[0050] The obstacle data can include obstacle distances detected by ultrasonic sensors at the left front, front, and right front of the target vehicle, such as obstacle data at the left front, front, and right front; the obstacle data at the front can be the minimum of the four obstacle distance values ​​detected by ultrasonic sensors.

[0051] Specifically, if the target vehicle is in motion and the gear position indicates that it is moving forward, then the obstacle data for the left front, the obstacle data for the front, and the obstacle data for the right front are determined; these are compared with a distance threshold to determine the path replanning type.

[0052] Optionally, the path replanning type is determined based on the obstacle data ahead and a distance threshold in the obstacle data, including:

[0053] Compare the data of the left front obstacle in the obstacle data with the distance threshold; if it is less than the distance threshold, the path replanning type is left front obstacle type;

[0054] If the distance is greater than the distance threshold, the right front obstacle data in the obstacle data ahead is compared with the distance threshold; if the distance is less than the distance threshold, the path replanning type is the right front obstacle type.

[0055] If the distance is greater than the distance threshold, the obstacle data directly in front of the obstacle in the obstacle data is compared with the distance threshold. If the distance is less than the distance threshold, the path replanning type is the obstacle directly in front type.

[0056] Among them, the left front obstacle type is the path replanning type where an obstacle appears in the left front and needs to be replanned; the right front obstacle type is the path replanning type where an obstacle appears in the right front and needs to be replanned; and the front obstacle type is the path replanning type where an obstacle appears in the front and needs to be replanned.

[0057] Specifically, the obstacle data for the left front is compared with a distance threshold. If the obstacle data for the left front is less than the distance threshold, it is determined that an obstacle exists in the left front of the parking path, and the path replanning type is left front obstacle type. If the obstacle data for the left front is greater than the distance threshold, the obstacle data for the right front is compared with the distance threshold. If the obstacle data for the right front is less than the distance threshold, it is determined that an obstacle exists in the right front of the parking path, and the path replanning type is right front obstacle type. If the obstacle data for the right front is greater than the distance threshold, the obstacle data for the front is compared with the distance threshold. If the obstacle data for the front is less than the distance threshold, it is determined that an obstacle exists in the front of the parking path, and the path replanning type is front obstacle type.

[0058] Understandably, by analyzing complex obstruction situations such as dynamic obstacles in autonomous parking scenarios, the system can perform detailed analysis based on the current gear status and conduct targeted analysis based on the obstacle data collected under specific circumstances, reducing the amount of data to be analyzed and ensuring a 60% improvement in replanning response speed; and by combining multi-dimensional state information to calculate the optimal planning path direction.

[0059] S232. If the gear is in reverse, the path replanning type is determined based on the rear obstacle data and distance threshold in the obstacle data.

[0060] The rear obstacle data can include the distances to obstacles detected by ultrasonic sensors to the left rear, directly rear, and right rear of the target vehicle, such as the left rear obstacle data, the directly rear obstacle data, and the right rear obstacle data; the directly rear obstacle data can be the minimum value of the four obstacle distance values ​​detected by ultrasonic sensors.

[0061] Specifically, if the target vehicle is in motion and the gear position indicates that it is in reverse, then the obstacle data for the left rear, the obstacle data for the front rear, and the obstacle data for the right rear are determined; these are compared with a distance threshold to determine the path replanning type.

[0062] Optionally, the path replanning type is determined based on the rear obstacle data and distance threshold in the obstacle data, including:

[0063] Compare the data of obstacles directly behind the device with a distance threshold; if it is less than the distance threshold, then compare the data of obstacles to the left rear with a distance threshold; if it is less than the distance threshold, then the path replanning type is set to the left rear obstacle type.

[0064] If the distance is greater than the distance threshold, the right rear obstacle data in the rear obstacle data is compared with the distance threshold; if the distance is less than the distance threshold, the path replanning type is the right rear obstacle type.

[0065] If the distance exceeds the threshold, the path replanning type will be the "directly behind obstacle" type.

[0066] Specifically, the left rear obstacle type is the path replanning type where an obstacle appears to the left rear and requires replanning; the right rear obstacle type is the path replanning type where an obstacle appears to the right rear and requires replanning; and the direct rear obstacle type is the path replanning type where an obstacle appears to the direct rear and requires replanning.

[0067] Specifically, first, determine if the data for the obstacle directly behind is greater than a distance threshold. If the data for the obstacle directly behind is less than the distance threshold, then compare the data for the obstacle to the left rear with the distance threshold. If the data for the obstacle to the left rear is also less than the distance threshold, then it is determined that an obstacle exists to the left rear in the parking path, and the path replanning type is left rear obstacle type. If the data for the obstacle to the left rear is greater than the distance threshold, then compare the data for the obstacle to the right rear with the distance threshold. If the data for the obstacle to the right rear is less than the distance threshold, then it is determined that an obstacle exists to the right rear in the parking path, and the path replanning type is right rear obstacle type. In other cases where the conditions are not met, such as when the data for both the obstacle to the left rear and the obstacle to the right rear are greater than the distance threshold, then the path replanning type is determined to be the obstacle directly behind.

[0068] Understandably, in autonomous parking scenarios, when a vehicle encounters an obstacle and gets stuck, the system can determine the replanning type and quantification standard based on environmental conditions such as obstacle data and preset distance thresholds, and simultaneously calculate the optimal path direction; the adaptation logic can be dynamically adjusted based on the actual obstacle data collected.

[0069] This invention integrates the current status of the target vehicle with the surrounding environment to avoid path direction misjudgment caused by single information, clarifies the replanning type and quantification standard; and refines the data dimensions according to the vehicle status, selectively filters the required judgment data, reduces resource consumption, improves the replanning response speed, and further ensures the real-time performance and efficiency of path replanning.

[0070] Figure 4 This is a flowchart of a parking replanning type determination method according to an embodiment of the present invention. Based on the above embodiments, this embodiment supplements the specific method for determining the path replanning type if the parking status is "parking completed". It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. For example... Figure 4 As shown, the method includes:

[0071] S310. Collect obstacle data and environmental data of the target vehicle when parking based on the parking path; obstacle data includes obstacle data in front and obstacle data behind.

[0072] S320. Obtain vehicle status data and parking data of the target vehicle; vehicle status data includes vehicle driving status, vehicle location information, vehicle driving information, vehicle basic information and gear data; parking data includes parking status and parking type.

[0073] S330. If the parking status is "parking completed", then determine the path replanning type based on vehicle status data, parking type, environmental information, parking path, and deviation judgment rules.

[0074] The deviation judgment rule determines the termination state of the target vehicle after executing the parking path, judging whether there is a deviation in its stopping; it can include heading deviation judgment and centerline lateral deviation judgment; the heading deviation judgment is set with a heading deviation threshold; the centerline lateral deviation judgment is set with a lateral deviation threshold.

[0075] Specifically, if the vehicle's parking status is "parking completed," meaning the target vehicle has completed the execution of this parking path, then based on the vehicle status data, environmental information, and parking path, and using the heading deviation threshold and lateral deviation threshold set in the deviation judgment rules, the corresponding path replanning type when the target vehicle deviates is determined.

[0076] Optionally, the route replanning type is determined based on vehicle status data, parking type, environmental information, parking path, and deviation judgment rules, including:

[0077] If the target vehicle is parked in perpendicular parking, the heading deviation value is determined based on the vehicle driving information, parking path, and vehicle position information.

[0078] The lateral deviation value of the centerline is determined based on environmental information and vehicle basic information;

[0079] If the heading deviation value is greater than the heading deviation threshold or the centerline lateral deviation value is greater than the lateral deviation threshold, then the route replanning type is the vertical parking space deviation type.

[0080] The vehicle driving information may include the target vehicle's orientation, i.e., the vehicle's heading angle obtained through onboard sensors; the vehicle position information is the target vehicle's current position; the heading deviation value is the angle between the vehicle's heading angle and the tangent direction of the parking path trajectory, i.e., the degree of deviation between the vehicle's orientation and the path direction; the environmental information is the environment surrounding the target vehicle obtained through holographic imaging, used to obtain the parking space line of the target parking space; and the centerline lateral deviation value is the vertical distance from the vehicle's center point to the parking space centerline.

[0081] Specifically, the parking type in the target vehicle's parking data is determined. If the parking type is perpendicular parking, the tangent direction of the parking path at the target vehicle's current position is determined based on the vehicle's position information and parking path. The heading deviation value is determined based on the vehicle's orientation and the tangent direction of the parking path. Furthermore, the parking space centerline is obtained based on the parking space lines acquired from the holographic image, and the vertical distance from the vehicle's center point to the parking space centerline is determined based on the vehicle's size information. If either the heading deviation value or the lateral deviation value is greater than the lateral deviation threshold, it indicates that the target vehicle is not parked correctly, and its path replanning type is determined to be the perpendicular parking space deviation type.

[0082] Understandably, after parking is completed, further combining environmental information and vehicle status information from multiple dimensions to determine whether the position deviation triggers the replanning condition, the deviation type judgment and strategy are executed to cover the replanning needs of scenarios such as path deviation during parking, ensuring that the vehicle can accurately trigger the appropriate path replanning process in complex parking environments.

[0083] Optionally, the route replanning type is determined based on vehicle status data, parking type, environmental information, parking path, and deviation judgment rules, including:

[0084] If the target vehicle is parked in a parallel parking position, then determine whether the target vehicle is located in the target parking space based on the environmental information.

[0085] If the target vehicle is located within the target parking space, the heading deviation value is determined based on the vehicle driving information, parking path, and vehicle position information.

[0086] If the heading deviation value is greater than the heading deviation threshold, the route replanning type is the horizontal parking space deviation type;

[0087] If the target vehicle is located outside the target parking space, the lateral deviation value of the trajectory is determined based on the vehicle location information and the parking path.

[0088] If the lateral deviation of the trajectory is greater than the lateral deviation threshold, the path replanning type is the horizontal parking space external deviation type.

[0089] The lateral deviation of the trajectory is the vertical distance between the current vehicle position information of the target vehicle and the corresponding position information in the parking path.

[0090] Specifically, the system determines the parking type of the target vehicle in its parking data. If the parking type is horizontal parking, the system further determines the relative position of the target vehicle and the target parking space based on the holographic image information in the environmental information. If the target vehicle is located within the target parking space, it indicates that the target vehicle may be improperly parked when the current parking ends. Therefore, the system determines the tangent direction of the parking path at the current position of the target vehicle based on the vehicle's position information and the parking path. The system then determines the heading deviation value based on the vehicle's orientation and the tangent direction of the parking path. If the heading deviation value is greater than the heading deviation threshold, the path replanning type is the horizontal parking space within deviation type. If the target vehicle is located outside the target parking space, it indicates that the vehicle was not parked in the designated location when the current parking ends. Therefore, the system determines the trajectory lateral deviation value based on the vehicle's position information and the corresponding position information in the parking path. If the trajectory lateral deviation value is greater than the lateral deviation threshold, the path replanning type is the horizontal parking space outside deviation type.

[0091] Understandably, by comparing the target vehicle with the parking space at the end of the parking path using environmental information, and further combining vehicle status information to classify the complex parking environment of the vehicle into scenarios, the path replanning type is determined based on different scenarios, and the appropriate path replanning process is accurately triggered.

[0092] When determining that the target vehicle is in a completed parking state, this invention further combines multi-dimensional information such as vehicle status information and environmental information to analyze the vehicle's current scenario. Based on different scenarios, it accurately triggers the appropriate path replanning type judgment process, unifies the replanning type and direction judgment logic, promotes technical compatibility, reduces user operation intervention rate, and improves user experience.

[0093] Figure 5This is a schematic diagram of a parking replanning type determination device according to an embodiment of the present invention. This embodiment is applicable to situations where real-time path replanning is performed during vehicle parking, and is particularly suitable for determining the path replanning type when the automatic parking system triggers path replanning in parking obstruction scenarios. The parking replanning type determination device can be implemented in hardware and / or software, and can be configured in a server. The parking replanning type determination device 400 includes a data acquisition module 410, an acquisition module 420, and a type determination module 430.

[0094] The acquisition module 410 is used to acquire obstacle data and environmental data when the target vehicle parks based on the parking path; the obstacle data includes obstacle data in front and obstacle data behind.

[0095] The acquisition module 420 is used to acquire vehicle status data and parking data of the target vehicle; the vehicle status data includes vehicle driving status, vehicle location information, vehicle driving information, vehicle basic information and gear data; the parking data includes parking status and parking type.

[0096] The type determination module 430 is used to determine the path replanning type based on vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules.

[0097] This invention collects obstacle data and environmental data of a target vehicle when parking based on a parking path. The obstacle data includes data on obstacles ahead and behind. The invention also acquires vehicle status data and parking data. Vehicle status data includes vehicle driving status, vehicle location information, vehicle driving information, basic vehicle information, and gear data. Parking data includes parking status and parking type. Based on the vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules, the path replanning type is determined. This technical solution, through multi-dimensional information collection of vehicle status data and logical judgment based on path replanning judgment rules, accurately identifies the obstacle type and replanning trigger conditions in parking obstruction scenarios, solving the problem of ambiguous path replanning type judgment and poor path adaptability in existing methods.

[0098] Optionally, the path replanning judgment rules include obstacle judgment rules and deviation judgment rules; the type determination module 430 includes an obstacle determination unit and a deviation determination unit;

[0099] The obstacle determination unit is used to determine the path replanning type based on the gear data, obstacle data, and obstacle judgment rules in the vehicle status data if the parking status is in progress.

[0100] The deviation determination unit is used to determine the path replanning type based on vehicle status data, parking type, environmental information, parking path, and deviation judgment rules if the parking status is parking completed.

[0101] Optionally, the obstacle judgment rule is a distance threshold. The obstacle determination unit is also used to determine the path replanning type based on the obstacle data in the obstacle data and the distance threshold if the gear state is forward.

[0102] If the gear is reverse, the path replanning type is determined based on the obstacle data of the rear and the distance threshold.

[0103] Optionally, the obstacle determination unit is also used to compare the left front obstacle data in the front obstacle data with a distance threshold; if it is less than the distance threshold, the path replanning type is left front obstacle type;

[0104] If the distance is greater than the distance threshold, the right front obstacle data in the obstacle data ahead is compared with the distance threshold; if the distance is less than the distance threshold, the path replanning type is the right front obstacle type.

[0105] If the distance is greater than the distance threshold, the obstacle data directly in front of the obstacle in the obstacle data is compared with the distance threshold. If the distance is less than the distance threshold, the path replanning type is the obstacle directly in front type.

[0106] Optionally, the obstacle determination unit is also used to compare the data of the obstacle directly behind in the rear obstacle data with a distance threshold. If it is less than the distance threshold, the data of the obstacle to the left rear is compared with the distance threshold. If it is less than the distance threshold, the path replanning type is the left rear obstacle type.

[0107] If the distance is greater than the distance threshold, the right rear obstacle data in the rear obstacle data is compared with the distance threshold; if the distance is less than the distance threshold, the path replanning type is the right rear obstacle type.

[0108] If the distance exceeds the threshold, the path replanning type will be the "directly behind obstacle" type.

[0109] Optionally, the deviation determination unit is also used to determine the heading deviation value based on the vehicle driving information, parking path and vehicle position information if the target vehicle's parking type is perpendicular parking.

[0110] The lateral deviation value of the centerline is determined based on environmental information and vehicle basic information;

[0111] If the heading deviation value is greater than the heading deviation threshold or the centerline lateral deviation value is greater than the lateral deviation threshold, then the route replanning type is the vertical parking space deviation type.

[0112] Optionally, the deviation determination unit is also used to determine whether the target vehicle is located in the target parking space based on environmental information if the parking type of the target vehicle is parallel parking.

[0113] If the target vehicle is located within the target parking space, the heading deviation value is determined based on the vehicle driving information, parking path, and vehicle position information.

[0114] If the heading deviation value is greater than the heading deviation threshold, the route replanning type is the horizontal parking space deviation type;

[0115] If the target vehicle is located outside the target parking space, the lateral deviation value of the trajectory is determined based on the vehicle location information and the parking path.

[0116] If the lateral deviation of the trajectory is greater than the lateral deviation threshold, the path replanning type is the horizontal parking space external deviation type.

[0117] The parking replanning type determination device provided in this embodiment of the invention can execute the parking replanning type determination method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0118] According to embodiments of the present invention, the present invention also provides an electronic device and a readable storage medium.

[0119] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0122] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the parking replanning type determination method.

[0123] In some embodiments, the application configuration data detection and repair method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the parking replanning type determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the parking replanning type determination method by any other suitable means (e.g., by means of firmware).

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0128] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0129] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system. This addresses the shortcomings of traditional physical hosts and dedicated virtual services, such as high management difficulty and weak business scalability.

[0130] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0131] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining parking replanning type, characterized in that, include: Collect obstacle data and environmental data when the target vehicle parks based on the parking path; The obstacle data includes obstacle data in front and obstacle data behind; Acquire vehicle status data and parking data of the target vehicle; the vehicle status data includes vehicle location information, vehicle driving information, vehicle basic information, and gear data; the parking data includes parking status and parking type; Based on the vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules, the path replanning type is determined.

2. The method according to claim 1, wherein the path replanning judgment rule includes an obstacle judgment rule and a deviation judgment rule; characterized in that, The method of determining the path replanning type based on the vehicle status data, parking data, obstacle data, environmental data, and path replanning judgment rules includes: If the parking status is parking in progress, then the path replanning type is determined based on the gear data in the vehicle status data, the obstacle data, and the obstacle judgment rules. If the parking status is parking completed, then the path replanning type is determined based on the vehicle status data, the parking type, the environmental information, the parking path, and the deviation judgment rule.

3. The method according to claim 2, wherein the obstacle determination rule is a distance threshold, characterized in that, The path replanning type is determined based on the gear position data in the vehicle status data, the obstacle data, and the obstacle judgment rules, including: If the gear position is forward, the path replanning type is determined based on the obstacle data in the obstacle data and the distance threshold. If the gear position is reverse, the path replanning type is determined based on the rear obstacle data in the obstacle data and the distance threshold.

4. The method according to claim 3, characterized in that, The method for determining the path replanning type based on the obstacle data (including the data of obstacles ahead) and the distance threshold includes: The data of the left front obstacle in the obstacle data is compared with a distance threshold; if it is less than the distance threshold, the path replanning type is the left front obstacle type. If the distance is greater than the distance threshold, the right front obstacle data in the front obstacle data is compared with the distance threshold; if the distance is less than the distance threshold, the path replanning type is the right front obstacle type. If the distance is greater than the distance threshold, the obstacle data directly in front of the obstacle in the obstacle data is compared with the distance threshold. If the distance is less than the distance threshold, the path replanning type is the obstacle type directly in front.

5. The method according to claim 3, characterized in that, The path replanning type is determined based on the rear obstacle data in the obstacle data and the distance threshold, including: The data of obstacles directly behind the path is compared with the distance threshold. If the data is less than the distance threshold, the data of obstacles to the left rear is compared with the distance threshold. If the data is less than the distance threshold, the path replanning type is the left rear obstacle type. If the distance is greater than the distance threshold, the right rear obstacle data in the rear obstacle data is compared with the distance threshold; if the distance is less than the distance threshold, the path replanning type is the right rear obstacle type. If the distance is greater than the distance threshold, the path replanning type is the "direct rear obstacle" type.

6. The method according to claim 2, characterized in that, The method then determines the path replanning type based on the vehicle status data, the parking type, the environmental information, the parking path, and the deviation judgment rule, including: If the target vehicle is parked in perpendicular parking, the heading deviation value is determined based on the vehicle driving information, the parking path, and the vehicle position information. The lateral deviation value of the centerline is determined based on the environmental information and the vehicle's basic information. If the heading deviation value is greater than the heading deviation threshold or the centerline lateral deviation value is greater than the lateral deviation threshold, then the path replanning type is the vertical parking space deviation type.

7. The method according to claim 2, characterized in that, The method then determines the path replanning type based on the vehicle status data, the parking type, the environmental information, the parking path, and the deviation judgment rule, including: If the target vehicle is parked in a parallel parking position, then the environmental information is used to determine whether the target vehicle is located in the target parking space. If the target vehicle is located within the target parking space, the heading deviation value is determined based on the vehicle driving information, the parking path, and the vehicle position information. If the heading deviation value is greater than the heading deviation threshold, then the route replanning type is the horizontal parking space deviation type; If the target vehicle is located outside the target parking space, the lateral deviation value of the trajectory is determined based on the vehicle location information and the parking path; If the lateral deviation value of the trajectory is greater than the lateral deviation threshold, then the path replanning type is the horizontal parking space external deviation type.

8. A parking replanning type determination device, characterized in that, include: The data acquisition module is used to collect obstacle data and environmental data when the target vehicle parks based on the parking path; The obstacle data includes obstacle data in front and obstacle data behind; The acquisition module is used to acquire vehicle status data and parking data of the target vehicle; the vehicle status data includes vehicle driving status, vehicle location information, vehicle driving information, vehicle basic information, and gear data; the parking data includes parking status and parking type. The type determination module is used to determine the path replanning type based on the vehicle status data, the parking data, the obstacle data, the environmental data, and the path replanning judgment rules.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the parking replanning type determination method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the parking replanning type determination method according to any one of claims 1-7.

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