Vehicle control method, control device, processor and vehicle

Abstract

The present application provides a vehicle control method, control device, processor, and vehicle. The method includes acquiring real-time radar point cloud data and real-time image data of a sprinkler truck, wherein the real-time radar point cloud data is used to characterize local information of the sprinkler truck; based on the real-time radar point cloud data and real-time image data, determining the edge of the sprinkler truck close to a target vehicle, wherein the target vehicle and the sprinkler truck are located in the same driving area; and determining the real-time driving information of the target vehicle based on at least the edge of the sprinkler truck close to the target vehicle, wherein the real-time driving information includes at least the driving speed of the target vehicle at the next moment. In this solution, the edge of the sprinkler truck close to the target vehicle is determined based on the local point cloud data, thereby avoiding affecting the real-time driving information of the target vehicle and achieving precise navigation of the target vehicle.

Description

Technical Field

[0001] The present application relates to autonomous driving, and more specifically, to a vehicle control method, a control device, a computer-readable storage medium, a processor, and a vehicle. Background Art

[0002] In existing solutions, if you want to build a sprinkler truck model, you generally need to identify the four vertices of the sprinkler truck. However, in actual operation, due to occlusion and other reasons, it is relatively difficult to accurately obtain the four vertices of the sprinkler truck, which makes it difficult to accurately construct the sprinkler truck model, further affecting the driving planning of the target vehicle. Summary of the Invention

[0003] The main purpose of this application is to provide a vehicle control method, control device, computer-readable storage medium, processor and vehicle to solve the problem in the prior art that it is difficult to accurately construct a sprinkler truck model, which further affects the driving planning of the target vehicle.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a vehicle control method is provided, including: obtaining real-time radar point cloud data and real-time image data of a sprinkler truck, the real-time radar point cloud data being used to characterize local information of the sprinkler truck; determining the edge of the sprinkler truck close to a target vehicle based on the real-time radar point cloud data and the real-time image data, the target vehicle and the sprinkler truck being located in the same driving area; determining the real-time driving information of the target vehicle based at least on the edge of the sprinkler truck close to the target vehicle, the real-time driving information including at least the driving speed of the target vehicle at the next moment.

[0005] Optionally, based on the real-time radar point cloud data and the real-time image data, determining the edge of the sprinkler truck close to the target vehicle includes: constructing a neural network model, wherein the neural network model is trained using multiple sets of historical radar point cloud data, historical image data, and the edges of the sprinkler truck close to the target vehicle corresponding to the historical radar point cloud data and historical image data acquired within a historical time period; inputting the real-time radar point cloud data and the real-time image data into the neural network model for calculation to obtain the edges of the sprinkler truck close to the target vehicle.

[0006] Optionally, the method further includes: projecting the historical radar point cloud data onto a horizontal plane to obtain a historical two-dimensional bird's-eye view of the historical radar point cloud data; and converting the historical two-dimensional bird's-eye view into a historical feature map by using a bird's-eye view encoding method.

[0007] Optionally, constructing a neural network model includes: using the historical feature map corresponding to multiple groups of historical radar point cloud data acquired within a historical time period, the historical image data, and the edges of the sprinkler truck close to the target vehicle corresponding to the historical radar point cloud data and the historical image data for training to obtain the neural network model; inputting the real-time radar point cloud data and the real-time image data into the neural network model for calculation to obtain the edges of the sprinkler truck close to the target vehicle, including: obtaining a real-time feature map corresponding to the real-time radar point cloud data; inputting the real-time feature map and the real-time image data into the neural network model for calculation to obtain the edges of the sprinkler truck close to the target vehicle.

[0008] Optionally, when the watering truck is located in front of the target vehicle, the real-time radar point cloud data is used to characterize the tail information of the watering truck.

[0009] Optionally, when the sprinkler truck is located on the right side of the target vehicle, the real-time radar point cloud data is used to characterize the left side information of the sprinkler truck.

[0010] Optionally, when the sprinkler truck is located on the left side of the target vehicle, the real-time radar point cloud data is used to characterize the right side information of the sprinkler truck.

[0011] Optionally, the neural network model is a deep learning model.

[0012] Optionally, the real-time driving information of the target vehicle is determined at least based on the edge of the sprinkler truck approaching the target vehicle, including: obtaining the driving speed and driving acceleration of the target vehicle at the current moment; determining the distance between the target vehicle and the sprinkler truck at the current moment based on the edge of the sprinkler truck approaching the target vehicle; determining the driving speed and driving acceleration of the target vehicle at the next moment based on the driving speed and driving acceleration of the target vehicle at the current moment and the distance between the target vehicle and the sprinkler truck at the current moment.

[0013] Optionally, the real-time image data is also used to determine the direction of the sprinkler truck.

[0014] According to another aspect of the present application, a vehicle control device is provided, including: an acquisition unit for acquiring real-time radar point cloud data and real-time image data of a sprinkler truck, wherein the real-time radar point cloud data is used to characterize local information of the sprinkler truck; a first determination unit for determining the edge of the sprinkler truck close to a target vehicle based on the real-time radar point cloud data and the real-time image data, wherein the target vehicle and the sprinkler truck are located in the same driving area; a second determination unit for determining the real-time driving information of the target vehicle based at least on the edge of the sprinkler truck close to the target vehicle, wherein the real-time driving information includes at least the driving speed of the target vehicle at the next moment.

[0015] According to another aspect of the present application, the first determination unit includes: a construction module for constructing a neural network model, wherein the neural network model is trained using multiple sets of historical radar point cloud data, historical image data, and the edges of the sprinkler truck close to the target vehicle corresponding to the historical radar point cloud data and historical image data acquired within a historical time period; and a calculation module for inputting the real-time radar point cloud data and the real-time image data into the neural network model for calculation to obtain the edges of the sprinkler truck close to the target vehicle.

[0016] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0017] According to another aspect of the present application, a processor is provided, wherein the processor is configured to run a program, wherein when the program is run, any one of the methods described is executed.

[0018] According to another aspect of the present application, a vehicle is provided, comprising a controller, wherein the controller is configured to execute any one of the methods described.

[0019] Applying the technical solution of the present application, by acquiring real-time radar point cloud data and real-time image data of a sprinkler truck, the real-time radar point cloud data is used to characterize local information of the sprinkler truck. Based on the real-time radar point cloud data and the real-time image data, the edge of the sprinkler truck close to the target vehicle is determined. At least based on the edge of the sprinkler truck close to the target vehicle, the real-time driving information of the target vehicle is determined. In this solution, the edge of the sprinkler truck close to the target vehicle is determined based on the local point cloud data, thereby avoiding affecting the real-time driving information of the target vehicle and achieving precise navigation of the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0021] Figure 1 A flow chart of a vehicle control method according to an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram of a vehicle control device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element or intervening elements may be present. Moreover, in the specification and claims, when it is described that an element is "connected to" another element, the element may be "directly connected to" the other element or "connected to" the other element through a third element.

[0027] As introduced in the background technology, it is difficult to accurately construct a sprinkler truck model in the existing technology, which further affects the driving plan of the target vehicle. In order to solve the problem of it being difficult to accurately construct a sprinkler truck model, which further affects the driving plan of the target vehicle, the embodiments of the present application provide a vehicle control method, a control device, a computer-readable storage medium, a processor and a vehicle.

[0028] According to an embodiment of the present application, a vehicle control method is provided.

[0029] Figure 1 FIG. 1 is a flow chart of a vehicle control method according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0030] Step S101, acquiring real-time radar point cloud data and real-time image data of the sprinkler truck, wherein the real-time radar point cloud data is used to represent local information of the sprinkler truck;

[0031] In the above steps, the four vertices of the sprinkler truck are no longer determined based on the global point cloud data (that is, not only the point cloud of the sprinkler truck but also the point clouds of other objects around the sprinkler truck). Only local point cloud data is obtained. The local point cloud data may only include the tail of the sprinkler truck, only the left side of the sprinkler truck, and only the right side of the sprinkler truck.

[0032] More preferably, the point cloud data of the part of the sprinkler truck far away from the target vehicle is no longer considered, thus avoiding the inability to obtain accurate point cloud data due to occlusion and the like.

[0033] Specifically, when the sprinkler truck is located in front of the target vehicle, the real-time radar point cloud data is used to characterize the tail information of the sprinkler truck.

[0034] Specifically, when the sprinkler truck is located on the right side of the target vehicle, the real-time radar point cloud data is used to characterize the left side information of the sprinkler truck.

[0035] Specifically, when the sprinkler truck is located on the left side of the target vehicle, the real-time radar point cloud data is used to represent the right side information of the sprinkler truck.

[0036] Of course, the sprinkler truck may also be located at the upper left, upper right, lower left, lower right, etc. of the target vehicle, which will not be listed here one by one.

[0037] Step S102: determining, based on the real-time radar point cloud data and the real-time image data, that the sprinkler truck is close to the target vehicle, and that the target vehicle and the sprinkler truck are located in the same driving area;

[0038] In the above steps, based on the real-time radar point cloud data and the real-time image data, a specific side of the sprinkler truck close to the target vehicle is determined. The specific side may be the side of the sprinkler truck's rear end closest to the target vehicle, the side of the sprinkler truck's left side closest to the target vehicle, or the side of the sprinkler truck's right side closest to the target vehicle, thereby accurately determining the distance between the target vehicle and the sprinkler truck.

[0039] Step S103: determining the real-time driving information of the target vehicle based at least on the edge of the water truck approaching the target vehicle, wherein the real-time driving information includes at least the driving speed of the target vehicle at the next moment.

[0040] In the above scheme, real-time radar point cloud data and real-time image data of the sprinkler truck are obtained. The real-time radar point cloud data is used to represent local information of the sprinkler truck. Based on the real-time radar point cloud data and the real-time image data, the edge of the sprinkler truck close to the target vehicle is determined. At least based on the edge of the sprinkler truck close to the target vehicle, the real-time driving information of the target vehicle is determined. In this scheme, the edge of the sprinkler truck close to the target vehicle is determined based on the local point cloud data, thereby avoiding affecting the real-time driving information of the target vehicle and achieving precise navigation of the target vehicle.

[0041] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] In some embodiments, determining the edge of the sprinkler truck that is close to the target vehicle based on the real-time radar point cloud data and the real-time image data includes: constructing a neural network model, wherein the neural network model is trained using multiple sets of historical radar point cloud data and historical image data acquired within a historical time period, as well as the edges of the sprinkler truck that are close to the target vehicle corresponding to the historical radar point cloud data and the historical image data; and inputting the real-time radar point cloud data and the real-time image data into the neural network model for calculation to obtain the edges of the sprinkler truck that are close to the target vehicle. The neural network model is used to accurately determine the edges of the sprinkler truck that are close to the target vehicle.

[0043] In some embodiments, the method further includes: projecting the historical radar point cloud data onto a horizontal plane to obtain a historical two-dimensional bird's-eye view of the historical radar point cloud data; and converting the historical two-dimensional bird's-eye view into a historical feature map using bird's-eye view encoding. Projecting the historical radar point cloud data onto a horizontal plane to obtain a historical two-dimensional bird's-eye view of the historical radar point cloud data facilitates accurate identification of edges approaching the target vehicle. Converting the historical two-dimensional bird's-eye view into a historical feature map using bird's-eye view encoding can reduce computational effort and accelerate computational speed in model training.

[0044] In some specific embodiments, the neural network model is a deep learning model. The deep learning model can adopt a typical convolutional neural network model. Of course, those skilled in the art can adjust the structure of the convolutional neural network model according to needs.

[0045] In some embodiments, constructing a neural network model includes: using the above-mentioned historical feature map corresponding to multiple groups of historical radar point cloud data obtained within a historical time period, the above-mentioned historical image data, and the edges of the sprinkler truck close to the target vehicle corresponding to the above-mentioned historical radar point cloud data and historical image data for training to obtain the above-mentioned neural network model; inputting the above-mentioned real-time radar point cloud data and the above-mentioned real-time image data into the above-mentioned neural network model for calculation to obtain the edges of the above-mentioned sprinkler truck close to the target vehicle, including: obtaining the real-time feature map corresponding to the above-mentioned real-time radar point cloud data; inputting the above-mentioned real-time feature map and the above-mentioned real-time image data into the above-mentioned neural network model for calculation to obtain the edges of the above-mentioned sprinkler truck close to the target vehicle.

[0046] In some embodiments, determining the real-time driving information of the target vehicle based at least on the edge of the water truck approaching the target vehicle includes: obtaining the current driving speed and driving acceleration of the target vehicle; determining the current distance between the target vehicle and the water truck based on the edge of the water truck approaching the target vehicle; and determining the next-moment driving speed and driving acceleration of the target vehicle based on the current driving speed and driving acceleration of the target vehicle and the current distance between the target vehicle and the water truck. That is, based on the current driving speed and driving acceleration of the target vehicle and the current distance between the target vehicle and the water truck, the next-moment driving speed and driving acceleration of the target vehicle are accurately determined.

[0047] In some specific embodiments, the real-time image data is also used to determine the direction of the sprinkler truck, which helps to plan the driving path of the target vehicle.

[0048] The present application also provides a vehicle control device. It should be noted that the vehicle control device of the present application can be used to execute the vehicle control method provided in the present application. The vehicle control device provided in the present application is introduced below.

[0049] Figure 2 Schematic diagram of a vehicle control device according to an embodiment of the present application. Figure 2 As shown, the device includes:

[0050] An acquisition unit 10 is used to acquire real-time radar point cloud data and real-time image data of the sprinkler truck, wherein the real-time radar point cloud data is used to represent local information of the sprinkler truck;

[0051] A first determining unit 20 is configured to determine, based on the real-time radar point cloud data and the real-time image data, that the sprinkler truck is close to the edge of the target vehicle, and that the target vehicle and the sprinkler truck are located in the same driving area;

[0052] The second determining unit 30 is configured to determine the real-time driving information of the target vehicle at least based on the edge of the water truck approaching the target vehicle, where the real-time driving information at least includes the driving speed of the target vehicle at a next moment.

[0053] In the above scheme, the acquisition unit acquires real-time radar point cloud data and real-time image data of the sprinkler truck. The real-time radar point cloud data is used to represent local information of the sprinkler truck. The first determination unit determines the edge of the sprinkler truck that is close to the target vehicle based on the real-time radar point cloud data and the real-time image data. The second determination unit determines the real-time driving information of the target vehicle based on at least the edge of the sprinkler truck that is close to the target vehicle. In this scheme, the edge of the sprinkler truck that is close to the target vehicle is determined based on the local point cloud data, thereby avoiding affecting the real-time driving information of the target vehicle and achieving precise navigation of the target vehicle.

[0054] In some embodiments, the first determination unit includes a construction module configured to construct a neural network model, wherein the neural network model is trained using multiple sets of historical radar point cloud data, historical image data, and edges of the sprinkler truck approaching the target vehicle corresponding to the historical radar point cloud data and the historical image data; and a calculation module configured to input the real-time radar point cloud data and the real-time image data into the neural network model for calculation to obtain the edges of the sprinkler truck approaching the target vehicle. The neural network model is used to accurately determine the edges of the sprinkler truck approaching the target vehicle.

[0055] In some embodiments, the apparatus further includes a projection unit and a conversion unit. The projection unit is configured to project the historical radar point cloud data onto a horizontal plane to obtain a historical two-dimensional bird's-eye view of the historical radar point cloud data. The conversion unit is configured to convert the historical two-dimensional bird's-eye view into a historical feature map using bird's-eye view encoding. Projecting the historical radar point cloud data onto the horizontal plane to obtain a historical two-dimensional bird's-eye view of the historical radar point cloud data facilitates accurate determination of edges approaching the target vehicle. Converting the historical two-dimensional bird's-eye view into a historical feature map using bird's-eye view encoding reduces computational effort and accelerates computation.

[0056] In some embodiments, the construction module is also used to use the above-mentioned historical feature maps corresponding to multiple groups of historical radar point cloud data obtained within a historical time period, the above-mentioned historical image data, and the edges of the sprinkler truck close to the target vehicle corresponding to the above-mentioned historical radar point cloud data and historical image data for training to obtain the above-mentioned neural network model; the operation module includes a first acquisition submodule and an operation submodule, the first acquisition submodule is used to obtain the real-time feature map corresponding to the above-mentioned real-time radar point cloud data; the operation submodule is used to input the above-mentioned real-time feature map and the above-mentioned real-time image data into the above-mentioned neural network model for operation to obtain the edges of the above-mentioned sprinkler truck close to the target vehicle.

[0057] In some embodiments, the second determination unit includes a second acquisition submodule, a first determination submodule, and a second determination submodule. The second acquisition submodule is used to acquire the current speed and acceleration of the target vehicle. The first determination submodule is used to determine the current distance between the target vehicle and the sprinkler truck based on the edge of the sprinkler truck approaching the target vehicle. The second determination submodule is used to determine the target vehicle's next speed and acceleration based on the target vehicle's current speed and acceleration and the current distance between the target vehicle and the sprinkler truck. That is, based on the target vehicle's current speed and acceleration and the current distance between the target vehicle and the sprinkler truck, the target vehicle's next speed and acceleration are accurately determined.

[0058] The control device of the above-mentioned vehicle includes a processor and a memory. The above-mentioned acquisition unit, first determination unit, second determination unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0059] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the precise sprinkler truck model can be determined by adjusting the kernel parameters.

[0060] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0061] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed, the device where the computer-readable storage medium is located is controlled to execute the vehicle control method.

[0062] An embodiment of the present invention provides a processor, which is used to run a program, wherein the vehicle control method is executed when the program is run.

[0063] An embodiment of the present invention provides a vehicle, including a controller, and the controller is used to execute any one of the above methods. Specifically, the vehicle is an autonomous driving vehicle. When the autonomous driving vehicle and the sprinkler truck are located in the same driving area, the real-time radar point cloud data and real-time image data of the sprinkler truck are obtained, and the real-time radar point cloud data is used to characterize the local information of the sprinkler truck. According to the real-time radar point cloud data and the real-time image data, the edge of the sprinkler truck close to the target vehicle is determined, and at least the real-time driving information of the target vehicle is determined based on the edge of the sprinkler truck close to the target vehicle. In this solution, the edge of the sprinkler truck close to the target vehicle is determined based on the local point cloud data, thereby avoiding the impact on the real-time driving information of the target vehicle, and achieving accurate navigation of the target vehicle.

[0064] An embodiment of the present invention provides a device, comprising a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the following steps are performed:

[0065] Step S101, acquiring real-time radar point cloud data and real-time image data of the sprinkler truck, wherein the real-time radar point cloud data is used to represent local information of the sprinkler truck;

[0066] Step S102: determining, based on the real-time radar point cloud data and the real-time image data, that the sprinkler truck is close to the target vehicle, and that the target vehicle and the sprinkler truck are located in the same driving area;

[0067] Step S103: determining the real-time driving information of the target vehicle based at least on the edge of the water truck approaching the target vehicle, wherein the real-time driving information includes at least the driving speed of the target vehicle at the next moment.

[0068] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0069] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program for initializing at least the following method steps:

[0070] Step S101, acquiring real-time radar point cloud data and real-time image data of the sprinkler truck, wherein the real-time radar point cloud data is used to represent local information of the sprinkler truck;

[0071] Step S102: determining, based on the real-time radar point cloud data and the real-time image data, that the sprinkler truck is close to the target vehicle, and that the target vehicle and the sprinkler truck are located in the same driving area;

[0072] Step S103: determining the real-time driving information of the target vehicle based at least on the edge of the water truck approaching the target vehicle, wherein the real-time driving information includes at least the driving speed of the target vehicle at the next moment.

[0073] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0074] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0078] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0079] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0081] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0082] 1) The vehicle control method of the present application obtains real-time radar point cloud data and real-time image data of a sprinkler truck. The real-time radar point cloud data is used to characterize local information of the sprinkler truck. Based on the real-time radar point cloud data and the real-time image data, the edge of the sprinkler truck close to the target vehicle is determined. At least based on the edge of the sprinkler truck close to the target vehicle, the real-time driving information of the target vehicle is determined. In this solution, the edge of the sprinkler truck close to the target vehicle is determined based on the local point cloud data, thereby avoiding affecting the real-time driving information of the target vehicle and achieving precise navigation of the target vehicle.

[0083] 2) In the vehicle control device of the present application, an acquisition unit acquires real-time radar point cloud data and real-time image data of the sprinkler truck. The real-time radar point cloud data is used to characterize local information of the sprinkler truck. The first determination unit determines the edge of the sprinkler truck close to the target vehicle based on the real-time radar point cloud data and the real-time image data. The second determination unit determines the real-time driving information of the target vehicle based at least on the edge of the sprinkler truck close to the target vehicle. In this solution, the edge of the sprinkler truck close to the target vehicle is determined based on the local point cloud data, thereby avoiding affecting the real-time driving information of the target vehicle and achieving precise navigation of the target vehicle.

[0084] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A vehicle control method, characterized in that: include: Acquire real-time radar point cloud data and real-time image data of the sprinkler truck, wherein the real-time radar point cloud data is used to characterize local information of the sprinkler truck; Determining, based on the real-time radar point cloud data and the real-time image data, that the sprinkler truck is close to an edge of a target vehicle, and that the target vehicle and the sprinkler truck are located in the same driving area; Determining real-time driving information of the target vehicle based at least on the edge of the sprinkler truck approaching the target vehicle, the real-time driving information including at least the driving speed of the target vehicle at a next moment; At least based on the edge of the sprinkler truck approaching the target vehicle, the real-time driving information of the target vehicle is determined, including: obtaining the current driving speed and driving acceleration of the target vehicle; determining the distance between the target vehicle and the sprinkler truck at the current moment based on the edge of the sprinkler truck approaching the target vehicle; determining the driving speed and driving acceleration of the target vehicle at the next moment based on the current driving speed and driving acceleration of the target vehicle and the distance between the target vehicle and the sprinkler truck at the current moment.

2. The method according to claim 1, characterized in that Determining, based on the real-time radar point cloud data and the real-time image data, an edge of the sprinkler truck close to the target vehicle, including: Constructing a neural network model, wherein the neural network model is trained using multiple sets of historical radar point cloud data, historical image data, and edges of the sprinkler truck approaching the target vehicle corresponding to the historical radar point cloud data and the historical image data; The real-time radar point cloud data and the real-time image data are input into the neural network model for calculation to obtain the edge of the sprinkler truck close to the target vehicle.

3. The method according to claim 2, characterized in that The method further comprises: Projecting the historical radar point cloud data onto a horizontal plane to obtain a historical two-dimensional bird's-eye view of the historical radar point cloud data; The historical two-dimensional bird's-eye view map is converted into a historical feature map by adopting a bird's-eye view coding method.

4. The method according to claim 3, characterized in that Build a neural network model, including: The neural network model is obtained by training the historical feature graph corresponding to multiple groups of historical radar point cloud data acquired within a historical time period, the historical image data, and the edge of the sprinkler truck close to the target vehicle corresponding to the historical radar point cloud data and the historical image data; The real-time radar point cloud data and the real-time image data are input into the neural network model for operation to obtain the edge of the sprinkler truck close to the target vehicle, including: Obtaining a real-time feature map corresponding to the real-time radar point cloud data; The real-time feature map and the real-time image data are input into the neural network model for calculation to obtain the edge of the sprinkler truck close to the target vehicle.

5. The method according to any one of claims 1 to 4, characterized in that When the watering truck is located in front of the target vehicle, the real-time radar point cloud data is used to characterize the tail information of the watering truck.

6. The method according to any one of claims 1 to 4, characterized in that In the case where the watering truck is located on the right side of the target vehicle, the real-time radar point cloud data is used to characterize the left side information of the watering truck.

7. The method according to any one of claims 1 to 4, characterized in that In the case that the watering truck is located on the left side of the target vehicle, the real-time radar point cloud data is used to characterize the right side information of the watering truck.

8. The method according to any one of claims 2 to 4, characterized in that The neural network model is a deep learning model.

9. The method according to any one of claims 1 to 4, characterized in that The real-time image data is also used to determine the direction of the sprinkler truck.

10. A vehicle control device, characterized in that: include: An acquisition unit, configured to acquire real-time radar point cloud data and real-time image data of the sprinkler truck, wherein the real-time radar point cloud data is used to characterize local information of the sprinkler truck; A first determining unit is configured to determine, based on the real-time radar point cloud data and the real-time image data, that the sprinkler truck is close to an edge of a target vehicle, and that the target vehicle and the sprinkler truck are located in the same driving area; A second determining unit is configured to determine real-time driving information of the target vehicle based at least on the edge of the sprinkler truck approaching the target vehicle, wherein the real-time driving information includes at least a driving speed of the target vehicle at a next moment; The second determining unit includes a second obtaining submodule, a first determining submodule and a second determining submodule, wherein the second obtaining submodule is used to obtain the current driving speed and driving acceleration of the target vehicle; The first determining submodule is used to determine the distance between the target vehicle and the sprinkler truck at the current moment according to the edge of the sprinkler truck close to the target vehicle; The second determination submodule is used to determine the driving speed and driving acceleration of the target vehicle at the next moment based on the current driving speed, driving acceleration of the target vehicle and the distance between the target vehicle and the sprinkler truck at the current moment; that is, based on the current driving speed, driving acceleration of the target vehicle and the distance between the target vehicle and the sprinkler truck at the current moment, the driving speed and driving acceleration of the target vehicle at the next moment are accurately determined.

11. The device according to claim 10, characterized in that The first determining unit includes: A construction module for constructing a neural network model, wherein the neural network model is trained using multiple sets of historical radar point cloud data, historical image data, and edges of the sprinkler truck approaching the target vehicle corresponding to the historical radar point cloud data and historical image data acquired within a historical time period; The computing module is used to input the real-time radar point cloud data and the real-time image data into the neural network model for computing to obtain the edge of the sprinkler truck close to the target vehicle.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 9.

13. A processor, characterized in that: The processor is configured to run a program, wherein the program executes the method according to any one of claims 1 to 9 when running.

14. A vehicle, characterized in that: The method comprises a controller configured to execute the method according to any one of claims 1 to 9.

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