Parking control method and device, vehicle and storage medium

Abstract

The invention provides a parking control method and device, a vehicle and a storage medium, and the method comprises the steps: responding to the detection of the existence of a target parking space which can be parked, transmitting the multimedia data of a parking area to a cloud end, and enabling the cloud end to recognize the parking decision information of the target parking space, the parking decision information, sent by the cloud, of the target parking space is obtained, parking control is conducted on the vehicle according to the parking decision information of the target parking space, the target parking space obtained through vehicle end recognition is achieved, and the parking decision information of the target parking space is obtained through cloud recognition. The accuracy of the parking decision information of the target parking space is improved, and the parking safety is improved through the cooperation of the vehicle terminal and the cloud terminal.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a parking control method, device, vehicle and storage medium. Background Art

[0002] With the popularization of autonomous driving and assisted driving, more and more vehicles are equipped with automatic parking technology, which helps drivers find suitable parking locations and complete parking.

[0003] However, as vehicle driving scenarios become more and more complex, how to find a suitable parking location and complete parking when faced with various special scenarios and improve parking safety is a technical problem that needs to be solved. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the present application proposes a parking control method, device, vehicle and storage medium to achieve finding a suitable parking position and completing parking, thereby improving parking safety.

[0006] In one aspect, an embodiment of the present application provides a parking control method, including:

[0007] In response to detecting the presence of a target parking space that can be parked, multimedia data of the parking area is sent to the cloud; wherein the multimedia data is used by the cloud to identify and obtain parking decision information of the target parking space;

[0008] Obtaining parking decision information of the target parking space sent by the cloud;

[0009] Parking control is performed on the vehicle according to the parking decision information of the target parking space.

[0010] Another aspect of the present application provides a parking control device, including:

[0011] A sending module, configured to send multimedia data of the parking area to the cloud in response to detecting the presence of a target parking space that can be parked; wherein the multimedia data is used by the cloud to identify the parking decision information of the target parking space;

[0012] An acquisition module, used for acquiring the parking decision information of the target parking space sent by the cloud;

[0013] The control module is used to control the parking of the vehicle according to the parking decision information of the target parking space.

[0014] Another aspect of the present application provides a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the above aspect is implemented.

[0015] Another aspect of the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the aforementioned aspect is implemented.

[0016] Another aspect of the present application provides a computer program product on which a computer program is stored. When the program is executed by a processor, the method described in the above aspect is implemented.

[0017] The parking control method, device, vehicle and storage medium proposed in the present application send multimedia data of the parking area to the cloud in response to detecting the existence of a target parking space that can be parked, wherein the multimedia data is used for the cloud to identify the parking decision information of the target parking space, obtain the parking decision information of the target parking space sent by the cloud, and perform parking control on the vehicle according to the parking decision information of the target parking space, so as to realize the target parking space identified by the vehicle side, and obtain the parking decision information of the target parking space through the cloud. Since the cloud has stronger computing power and reasoning ability, the accuracy of the parking decision information of the target parking space is improved, and the parking safety is improved through the collaboration between the vehicle side and the cloud.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flow chart of a parking control method provided in an embodiment of the present application;

[0021] Figure 2 A flowchart of another parking control method provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of a parking scenario provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of multimedia data of a parking area provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the structure of a parking control device provided in an embodiment of the present application;

[0025] Figure 6 It is a structural schematic diagram of a vehicle proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0026] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0027] The parking control method, device, vehicle and storage medium according to the embodiments of the present application are described below with reference to the accompanying drawings.

[0028] Figure 1 A flowchart of a parking control method provided in an embodiment of the present application.

[0029] The embodiment of the present application takes the parking control method configured in a parking control device as an example. The parking control device can be applied to any vehicle-mounted device so that the vehicle-mounted device can perform a parking control function.

[0030] like Figure 1 As shown, the method may include the following steps:

[0031] Step 101 , in response to detecting the presence of a target parking space that can be parked, sending multimedia data of the parking area to the cloud.

[0032] The parking area refers to a designated area that provides legal parking space for motor vehicles, including at least one parking space. The multimedia data of the parking area may be at least one of image data and video data, and is used for cloud-based recognition to obtain parking decision information of a target parking space. As an implementation method, the multimedia data of the parking area may be recognized based on a visual detection model set in the cloud to obtain parking decision information of each parking space, including parking decision information of the target parking space.

[0033] In one implementation of the embodiment of the present application, the parking space lines, shapes, colors, etc. are identified based on the multimedia data of the parking area based on an image processing algorithm, and the existence of a target parking space that can be parked is identified. The target parking space that can be parked means that the parking space is vacant and no other vehicles are parked in the parking space.

[0034] In one implementation of the embodiment of the present application, a parking space detection model may be set on the vehicle side. The parking space detection model is a deep learning model arranged on the vehicle side, and is used to sense whether there are vacant parking spaces that can be parked in the parking scene. As an implementation method, multimedia data of the parking area of ​​the parking scene to be parked is collected by the sensor on the vehicle side, and the multimedia data of the parking area is identified based on the parking space detection model set on the vehicle side to obtain the target parking space that can be parked. The target parking space that can be parked is determined by vehicle identification, which means that the vehicle side believes that the target parking space is parkable. However, due to the limited computing power of the vehicle-side platform, it is impossible to deploy a model with a complex structure that requires a large computing power support. Therefore, for complex special scenarios, the accuracy of parking space identification is limited to a certain extent, that is, the detected target parking space is not necessarily actually parkable. Since the cloud server has strong computing power, a visual detection model with reasoning ability obtained by data training based on complex parking scenarios can be set up, so that the cloud-based visual detection model can be combined with the environmental information around the target parking space in the multimedia data of the parking area, the spatial information and the route information for parking in the target parking space, to identify and reason, so as to determine the parking decision information of the target parking space identified by the vehicle end, wherein the parking decision information of the target parking space includes the parking route of the target parking space and whether the target parking space can be parked, wherein the parking route of the target parking space refers to the driving path of the vehicle from the current position to the target parking space. In the case where the target parking space cannot be parked, prompt information is also included, and the prompt information is used to prompt that there are obstacles on the target parking space and / or the parking route. As an example, the visual detection model can be a visual large language model.

[0035] When the vehicle recognizes that there is a target parking space that can be parked, the multimedia data of the parking area is sent to the cloud.

[0036] Step 102, obtaining parking decision information of the target parking space sent by the cloud.

[0037] In the embodiment of the present application, parking decision information is sent to the vehicle side based on the communication link established between the vehicle side and the cloud.

[0038] Step 103: Parking control of the vehicle is performed according to the parking decision information of the target parking space.

[0039] In the embodiment of the present application, when the two vehicle ends receive the parking decision information of the target parking space sent from the cloud, they will determine whether the target parking space is a scenario where parking is really possible based on the parking decision information of the target parking space, and then, based on the parking decision information, perform parking control on the vehicle.

[0040] Among them, as an implementation method, according to the parking decision information of the target parking space, it is determined that the target parking space cannot be parked in, that is, the visual detection model in the cloud confirms that although the target parking space is a vacant parking space, it does not actually meet the conditions for parking. In other words, the result detected by the vehicle end is different from the result detected by the cloud. The detection result in the cloud shall prevail. According to the parking decision information of the target parking space, a parking warning message shall be generated. Among them, the parking warning message is used to prompt that there are obstacles on the parking path corresponding to the target parking space and / or in the target parking space, and the target parking space cannot be parked in, so as to inform the car owner of the reason why parking is not possible and to prompt the user of safety risks.

[0041] As an example scenario, in the multimedia data of the parking area, there is an obstacle in front of parking space A, such as a tree stump, special-shaped objects, potholes or suspended obstacles. The parking space detection model cannot recognize this long-tail situation based on the multimedia data of the parking area, and believes that the parking space is a target parking space that can be parked. However, the actual situation is that if the vehicle is parked in the parking space, the obstacle will cause damage to the vehicle. After transmitting the multimedia data of the parking area to the cloud, the visual detection model uses reasoning ability to obtain parking decision information of parking space A, where the parking decision information includes that parking space A cannot be parked, and the prompt information is the position of the obstacle in the multimedia data that must be passed when entering parking space A according to the parking route. The obstacle prevents the vehicle from driving to parking space A, that is, there is no parking route or there is a safety hazard in the parking route, and it cannot be parked.

[0042] As another implementation method, the target parking space is determined to be parkable based on the parking decision information, that is, the recognition results of the cloud and the vehicle are consistent, and the parking route corresponding to the target parking space is determined, and the vehicle is controlled to park in the target parking space based on the parking route. Among them, as a first implementation method, the three-dimensional spatial information of the parking area is obtained, and the three-dimensional spatial information of the parking area can be obtained by collecting the three-dimensional spatial information of the parking area by the radar installed on the vehicle side. According to the three-dimensional spatial information, the parking route corresponding to the target parking space is determined, that is, the parking route that can be driven to the target parking space is determined through the distribution of parking spaces included in the three-dimensional spatial information and the position and posture information of the parked vehicles, and the vehicle is controlled to automatically park in the target parking space based on the parking route. As a second implementation method, the parking route corresponding to the target parking space is determined according to the parking decision information sent from the cloud. That is, since the cloud has strong computing power, it can also calculate the parking route according to the location information of the target parking space and the current location information of the vehicle during the process of recognizing multimedia data through a visual detection model, and infer that the target parking space is parkable, that is, there are no obstacles on the parking path and / or in the target parking space. Based on the parking route, the vehicle is controlled to automatically drive to the target parking space.

[0043] As an implementation manner, when it is determined that the target parking space is available for parking, the driver of the vehicle may be reminded that the target parking space is available for parking, so that the driver manually controls the vehicle to park in the target parking space.

[0044] In the parking control method of the embodiment of the present application, in response to detecting the existence of a target parking space that can be parked, multimedia data of the parking area is sent to the cloud, wherein the multimedia data is used for the cloud to identify the parking decision information of the target parking space, obtain the parking decision information of the target parking space sent by the cloud, and perform parking control on the vehicle according to the parking decision information of the target parking space, so as to realize the target parking space identified by the vehicle side, and obtain the parking decision information of the target parking space through the cloud. Since the cloud has stronger computing power and reasoning ability, the accuracy of the parking decision information of the target parking space is improved, and the parking safety is improved through the collaboration between the vehicle side and the cloud.

[0045] Based on the above embodiments, Figure 2 The flowchart of another parking control method provided in the embodiment of the present application illustrates that when the cloud detection result is different from the vehicle-side detection result, it means that the parking space detection model cannot recognize the parking scene, and the parking scene belongs to the long-tail data of parking. Therefore, the data of the parking scene can be used as a training sample to synchronously update the parking space detection model to improve the detection effect of the parking space detection model and improve parking safety. Figure 2 As shown, the method comprises the following steps:

[0046] Step 201 , in response to detecting the presence of a target parking space that can be parked, sending multimedia data of the parking area to the cloud.

[0047] Among them, the multimedia data of the parking area is used for cloud-based identification to obtain parking decision information for the target parking space.

[0048] In one implementation of the embodiment of the present application, the cloud identifies multimedia data based on a visual detection model to obtain parking decision information of a target parking space, wherein the visual detection model is pre-trained with a large amount of data and can accurately identify obstacle information in various scenes to accurately determine whether a parking space can be parked. As an implementation method, multimedia data of a parking area sample is obtained, wherein the parking decision information corresponding to the parking space is annotated in the sample multimedia data of the parking area, the sample multimedia data of the parking area is input into the visual detection model to obtain predicted parking decision information of the parking space, and a loss function is determined based on the difference between the predicted parking decision information and the annotated parking decision information, and the parameters of the visual detection model are adjusted based on the loss function to implement the training of the visual detection model and obtain a trained visual detection model. Among them, the training process of the visual detection model can be repeated multiple times, and sample multimedia data of parking areas in different scenes are used for training. When the loss function is less than a set threshold or the set number of training times, the visual detection model training is completed, and a trained visual detection model is obtained. The trained visual detection model can identify parking spaces in complex scenes based on the multimedia data of the parking area, and calculate the parking route based on the location of the parking space. It can not only determine whether the identified parking space can be parked, but also identify the dangers in the parking route, that is, whether there are potential dangerous factors in the parking route.

[0049] In the embodiment of the present application, since the visual detection model set in the cloud is slow to execute, it is impossible to obtain an immediate response. However, since the parking speed is usually slow, the vehicle can send multimedia data of the parking area to the cloud before parking is actually carried out, and obtain the detection results sent by the cloud. As an example, Figure 3 A schematic diagram of a parking scene provided in an embodiment of the present application, taking the visual detection model as a visual large language model as an example, such as Figure 3 As shown, in the parking area, the vehicle is the vehicle to be parked. The vehicle collects multimedia data of the parking area at point A, and uses the parking space detection model to identify that the target parking space is a parking space that can be parked. The multimedia data of the parking area is sent to the cloud. The cloud performs recognition based on the multimedia data of the parking area. During the cloud recognition process, the vehicle will prepare for parking based on the results of the vehicle detection. The vehicle will travel from point A to point B. During the period from point A to point B, the cloud is performing recognition. Usually at point B, the vehicle will receive the parking decision information of the parking space returned by the cloud, and perform parking control based on the parking decision information. Through the collaboration between the cloud and the vehicle, when the large visual language model cannot be deployed on the vehicle side, it is possible to accurately determine whether the parking space can be parked, obtain the parking path and the reason why parking cannot be parked, and improve the safety of parking.

[0050] Step 202, obtaining parking decision information of the target parking space sent by the cloud.

[0051] Step 203: According to the parking decision information, it is determined that the target parking space cannot be parked into, and parking warning information is generated.

[0052] Among them, step 202 and step 203 can refer to the relevant explanations in the aforementioned embodiments, and the principles are the same, so they will not be repeated here.

[0053] It should be noted that the subsequent steps 204 and 205 may be executed before step 203, or after step 203, or simultaneously with step 203, which is not limited in the embodiments of the present application.

[0054] Step 204 : In response to the parking decision information indicating that the target parking space cannot be parked into, generating a training sample of a parking space detection model according to the multimedia data of the parking area and the parking decision information.

[0055] In the embodiment of the present application, the parking decision information indicates that the target parking space cannot be parked into, which means that the result of the detection by the visual detection model on the cloud is different from the result of the detection by the parking space detection model set on the vehicle side. Since the visual detection model on the cloud has stronger computing power and scene recognition capabilities, it is considered that the detection by the parking space detection model on the vehicle side is inaccurate. Then, the multimedia data of the parking area and the parking decision information are used to generate training samples for the parking space detection model. As an implementation method, the annotation information of the multimedia data of the parking area is determined according to the parking decision information, wherein the annotation information is that the target parking space cannot be parked into. The training samples of the parking space detection model are generated according to the multimedia data of the annotated parking area, wherein the training samples are negative samples. The parking space detection model is trained using negative samples so that the parking space detection model can identify which parking spaces in the scene are vacant, but in fact the parking spaces do not have the conditions for parking. For example, there are obstacles on the parking path to the parking space, such as low tree stumps. Figure 4 A schematic diagram of multimedia data of a parking area provided in an embodiment of the present application, such as Figure 4 As shown, the parking area image is captured by a camera installed on the vehicle, such as a fisheye camera. There is a tree stump B on the path leading to parking space A. The vehicle must pass through tree stump B to park in parking space A. Tree stump B may cause damage to the vehicle tires or chassis. Therefore, although parking space A is a vacant parking space, parking space A does not meet the parking conditions, that is, parking in parking space A is not possible.

[0056] Step 205: Send the training samples to the cloud.

[0057] The training samples are used to update and train the parking space detection model in the cloud to obtain an updated parking space detection model.

[0058] In the embodiment of the present application, the cloud updates and trains the parking space detection model based on the acquired training samples, so that the trained parking space detection model can recognize more special scenarios. Then, the updated parking space detection model is redeployed on the vehicle side, thereby increasing the recognition capability of long-tail data and improving the safety of parking.

[0059] In the parking control method of the embodiment of the present application, in response to the use of a parking space detection model to detect the existence of a target parking space that can be parked in the multimedia data of the parking area, the multimedia data of the parking area is sent to the cloud, wherein the multimedia data of the parking area is used for the cloud to obtain parking decision information of the target parking space based on the visual detection model recognition, obtain the parking decision information of the target parking space sent by the cloud, and perform parking control on the vehicle according to the parking decision information of the target parking space, so as to achieve the parking decision information of the target parking space obtained by the vehicle side through the visual detection model of the cloud. Since the visual detection model of the cloud has stronger computing power and reasoning ability, the accuracy of the parking decision information of the target parking space is improved, and the parking safety is improved through the collaboration between the vehicle side and the cloud.

[0060] In order to implement the above embodiment, the embodiment of the present application further proposes a parking control device.

[0061] Figure 5 A schematic diagram of the structure of a parking control device provided in an embodiment of the present application.

[0062] like Figure 5 As shown, the device may include:

[0063] The sending module 51 is used to send multimedia data of the parking area to the cloud in response to detecting the presence of a target parking space that can be parked; wherein the multimedia data is used by the cloud to identify the parking decision information of the target parking space.

[0064] The acquisition module 52 is used to acquire the parking decision information of the target parking space sent by the cloud.

[0065] The control module 53 is used to control the parking of the vehicle according to the parking decision information of the target parking space.

[0066] Furthermore, in an implementation of the embodiment of the present application, the target parking space is obtained based on the parking space detection model identification of the vehicle side, and the device also includes a generation module.

[0067] a generating module, configured to generate, in response to the parking decision information indicating that the target parking space is not parkable, a training sample of the parking space detection model according to the multimedia data of the parking area and the parking decision information;

[0068] The sending module 51 is further used to send the training samples to the cloud; wherein the training samples are used in the cloud to update and train the parking space detection model to obtain an updated parking space detection model.

[0069] In one implementation of the embodiment of the present application, the generating module is further used to:

[0070] Determining, according to the parking decision information, the annotation information of the multimedia data of the parking area; wherein the annotation information indicates that the target parking space cannot be parked in;

[0071] According to the multimedia data of the marked parking area, a training sample of the parking space detection model is generated; wherein the training sample is a negative sample.

[0072] In one implementation of the embodiment of the present application, the control module 53 is further configured to:

[0073] Determining, based on the parking decision information, that the target parking space is not accessible;

[0074] Generate parking warning information; wherein the parking warning information is used to prompt that there are obstacles on the parking path corresponding to the target parking space and / or in the target parking space, and the target parking space cannot be parked.

[0075] In one implementation of the embodiment of the present application, the control module 53 is further configured to:

[0076] Determining, based on the parking decision information, that the target parking space is available for parking;

[0077] Determining a parking route corresponding to the target parking space;

[0078] According to the parking route, the vehicle is controlled to park in the target parking space.

[0079] In one implementation of the embodiment of the present application, the control module 53 is further configured to:

[0080] Acquiring three-dimensional spatial information of the parking area;

[0081] A parking route corresponding to the target parking space is determined according to the three-dimensional space information.

[0082] In one implementation of the embodiment of the present application, the control module 53 is further configured to:

[0083] A parking route corresponding to the target parking space is determined according to the parking decision information.

[0084] In one implementation of the embodiment of the present application, a method for training a visual detection model includes:

[0085] Acquire sample multimedia data of the parking area; wherein the sample multimedia data of the parking area is marked with parking decision information corresponding to the parking space;

[0086] Inputting the sample multimedia data of the parking area into the visual detection model to obtain predicted parking decision information of the parking space;

[0087] determining a loss function according to a difference between the predicted parking decision information and the marked parking decision information;

[0088] The visual detection model is trained according to the loss function to obtain a trained visual detection model.

[0089] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and will not be repeated here.

[0090] In the parking control device of the embodiment of the present application, in response to detecting the existence of a target parking space that can be parked, multimedia data of the parking area is sent to the cloud, wherein the multimedia data is used for the cloud to identify the parking decision information of the target parking space, obtain the parking decision information of the target parking space sent by the cloud, and perform parking control on the vehicle according to the parking decision information of the target parking space, so as to realize the target parking space identified by the vehicle side, and obtain the parking decision information of the target parking space through the cloud. Since the cloud has stronger computing power and reasoning ability, the accuracy of the parking decision information of the target parking space is improved, and the parking safety is improved through the collaboration between the vehicle side and the cloud.

[0091] In order to implement the above embodiments, the present application also proposes a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the above method embodiments is implemented.

[0092] In order to implement the above embodiments, the present application also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the above method embodiments is implemented.

[0093] In order to implement the above embodiments, the present application also proposes a computer program product on which a computer program is stored. When the computer program is executed by a processor, the method described in the above method embodiments is implemented.

[0094] Figure 6 600 is a schematic diagram of a vehicle structure proposed in an embodiment of the present application. For example, vehicle 600 may be a hybrid vehicle, or a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 600 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0095] Reference Figure 6 , the vehicle 600 may include various subsystems, for example, an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 600 may be interconnected by wire or wireless means.

[0096] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, among others.

[0097] The perception system 620 may include several sensors for sensing information about the environment around the vehicle 600. For example, the perception system 620 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0098] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0099] The drive system 640 may include components that provide powered motion for the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0100] Some or all functions of the vehicle 600 are controlled by a computing platform 650. The computing platform 650 may include at least one processor 651 and a memory 652, and the processor 651 may execute instructions 653 stored in the memory 652.

[0101] The processor 651 may be any conventional processor, such as a commercially available CPU. The processor may also include a multimedia data processor such as a Graphic Process Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof.

[0102] The memory 652 may be implemented by any type of volatile or nonvolatile 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 memory, flash memory, magnetic disk or optical disk.

[0103] In addition to the instructions 653 , the memory 652 may also store data, such as road maps, route information, and data such as the location, direction, and speed of the vehicle. The data stored in the memory 652 may be used by the computing platform 650 .

[0104] In the embodiment of the present disclosure, the processor 651 may execute instruction 653 to complete all or part of the steps of the above method embodiment.

[0105] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0106] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0107] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0110] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A parking control method, characterized in that: include: In response to detecting the presence of a target parking space that can be parked, multimedia data of the parking area is sent to the cloud; wherein the multimedia data is used by the cloud to identify and obtain parking decision information of the target parking space; Obtaining parking decision information of the target parking space sent by the cloud; Parking control is performed on the vehicle according to the parking decision information of the target parking space.

2. The method according to claim 1, characterized in that The target parking space is obtained based on the parking space detection model of the vehicle end. After obtaining the parking decision information of the target parking space sent by the cloud, the method further includes: In response to the parking decision information indicating that the target parking space is not parkable, generating a training sample for the parking space detection model according to the multimedia data of the parking area and the parking decision information; The training samples are sent to the cloud; wherein the training samples are used in the cloud to update and train the parking space detection model to obtain an updated parking space detection model.

3. The method according to claim 2, characterized in that The step of generating a training sample for the parking space detection model according to the multimedia data of the parking area and the parking decision information comprises: Determining, according to the parking decision information, the annotation information of the multimedia data of the parking area; wherein the annotation information indicates that the target parking space cannot be parked in; According to the multimedia data of the marked parking area, a training sample of the parking space detection model is generated; wherein the training sample is a negative sample.

4. The method according to claim 1, characterized in that The parking control of the vehicle according to the parking decision information of the target parking space includes: Determining, based on the parking decision information, that the target parking space is not accessible; Generate parking warning information; wherein the parking warning information is used to prompt that there are obstacles on the parking path corresponding to the target parking space and / or in the target parking space, and the target parking space cannot be parked.

5. The method according to claim 1, characterized in that The parking control of the vehicle according to the parking decision information of the target parking space includes: Determining, based on the parking decision information, that the target parking space is available for parking; Determining a parking route corresponding to the target parking space; According to the parking route, the vehicle is controlled to park in the target parking space.

6. The method according to claim 5, characterized in that The determining of the parking route corresponding to the target parking space includes: Acquiring three-dimensional spatial information of the parking area; A parking route corresponding to the target parking space is determined according to the three-dimensional space information.

7. The method according to claim 5, characterized in that The determining of the parking route corresponding to the target parking space includes: A parking route corresponding to the target parking space is determined according to the parking decision information.

8. The method according to any one of claims 1 to 7, characterized in that: The training method of the visual detection model comprises: Acquire sample multimedia data of a parking area; wherein the sample multimedia data is marked with parking decision information corresponding to the parking space; Inputting the sample multimedia data into the visual detection model to obtain predicted parking decision information of the parking space; determining a loss function according to a difference between the predicted parking decision information and the marked parking decision information; The visual detection model is trained according to the loss function to obtain a trained visual detection model.

9. A parking control device, characterized in that: include: A sending module, configured to send multimedia data of a parking area to a cloud in response to detecting the presence of a target parking space that can be parked; wherein the multimedia data of the parking area is used by the cloud to obtain parking decision information of the target parking space based on the visual detection model; An acquisition module, used for acquiring the parking decision information of the target parking space sent by the cloud; The control module is used to control the parking of the vehicle according to the parking decision information of the target parking space.

10. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Implement the steps of the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to perform the steps of the method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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