Vehicle control method and device and readable storage medium

Abstract

The invention discloses a vehicle control method and device and a readable storage medium, and relates to the technical field of vehicles. The vehicle control method comprises the steps of collecting a road surface image of a road where a vehicle is located in the vehicle driving process; performing recognition processing on the road surface image through a road surface recognition model to determine a road surface type corresponding to the road surface image; determining a torque value of the vehicle based on the road surface type; and adjusting the motor torque of the vehicle to the torque value. The driving stability of the vehicle is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to a vehicle control method, apparatus, and readable storage medium. Background Technology

[0002] When new energy vehicles are driving on icy or snowy roads, releasing the accelerator pedal triggers energy recovery. However, due to the low traction on icy or snowy surfaces, further coasting and energy recovery can easily cause the vehicle to lock up, resulting in loss of handling stability and potentially causing an accident. This demonstrates that existing vehicle control methods suffer from technical problems such as poor driving stability. Summary of the Invention

[0003] This application provides a vehicle control method, device, and readable storage medium to solve technical problems such as poor driving stability in the prior art.

[0004] A first aspect of this application provides a vehicle control method, the method comprising: During the vehicle's journey, images of the road surface where the vehicle is located are collected. The road surface recognition model is used to identify and process road surface images in order to determine the road surface type corresponding to the road surface image. The torque value of the vehicle is determined based on the road surface type; Adjust the vehicle's motor torque to the specified value.

[0005] In some embodiments, acquiring images of the road surface where the vehicle is located includes: Obtain the first image of the road where the vehicle is located; The first image is binarized to update it into the second image; Based on a preset Gaussian function, the second image is denoised to obtain a road surface image.

[0006] In some embodiments, the method further includes: A first model is established based on convolutional layers, pooling layers, and fully connected layers. The weights of the first model are initialized so that the first model is updated to the second model; Obtain the output value of the second model and determine the error between the output value and the preset value; Based on the error between the output value and the preset value, the second model is iteratively updated to obtain the road surface recognition model.

[0007] In some embodiments, a road surface recognition model is used to perform recognition processing on a road surface image to determine the road surface type corresponding to the road surface image, including: Set the road surface image as the input data; Input data is fed into the road surface recognition model to obtain the type coefficients output by the road surface recognition model; Based on the type coefficient, the road surface type corresponding to the road surface image is determined.

[0008] In some embodiments, determining the vehicle's torque value based on road surface type includes: Obtain the vehicle's real-time speed; The torque value is determined based on the real-time speed and road surface type.

[0009] In some embodiments, determining the vehicle's torque value based on road surface type includes: When the road surface is ice, the torque value is determined to be the first torque. When the road surface is snow, the torque value is determined to be the second torque. When the road surface type is a rain-prone road surface, the torque value is determined to be the third torque. When the road surface type is normal, the torque value is determined to be the fourth torque. The fourth torque is greater than the first, second, and third torques.

[0010] In some embodiments, after adjusting the vehicle's motor torque to a torque value, the method further includes: Obtain the vehicle's initial torque; Determine the torque difference between the initial torque and the torque value; The vehicle's regenerated energy is determined based on the torque difference.

[0011] The vehicle control method in this embodiment uses a road surface recognition model to identify and process road surface images to determine the road surface type corresponding to the road surface image. Based on the road surface type, the torque value of the vehicle is determined, ensuring the applicability of the torque value and thus ensuring the driving stability of the vehicle on slippery roads.

[0012] A second aspect of this application provides a vehicle control device, the device comprising: The acquisition unit is used to collect road surface images of the road where the vehicle is located during the vehicle's movement. The first processing unit is used to perform recognition processing on the road surface image through the road surface recognition model in order to determine the road surface type corresponding to the road surface image; The second processing unit is used to determine the vehicle's torque value based on the road surface type; The control unit is used to adjust the vehicle's motor torque to the specified torque value.

[0013] In this embodiment, the vehicle control device uses a road surface recognition model to identify and process road surface images to determine the road surface type corresponding to the road surface image. Based on the road surface type, it determines the torque value of the vehicle, ensuring the applicability of the torque value and thus ensuring the driving stability of the vehicle on slippery roads.

[0014] A third aspect of this application provides another vehicle control device, including a processor and a memory. The memory stores a computer program that, when executed by the processor, implements the steps of the vehicle control method as described in any of the above embodiments. Therefore, this vehicle control device possesses all the beneficial effects of the vehicle control method in any of the above embodiments, which will not be elaborated further here.

[0015] A fourth aspect of this application provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the vehicle control method as described in any of the above embodiments. Therefore, this readable storage medium possesses all the beneficial effects of the vehicle control method in any of the above embodiments, which will not be elaborated further here. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of a vehicle control method provided in an embodiment of this application; Figure 2 This is one of the functional diagrams of the vehicle control method provided in the embodiments of this application; Figure 3 This is a second functional schematic diagram of the vehicle control method provided in the embodiments of this application; Figure 4 Functional block diagram of the vehicle control device provided in the embodiments of this application; Figure 5 This is a structural block diagram of a vehicle control device provided in an embodiment of this application. Detailed Implementation

[0018] To better understand the technical solutions provided in the embodiments of this specification, the technical solutions of the embodiments of this specification will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0019] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The term "two or more" includes two or more cases.

[0020] In some embodiments, such as Figure 1 As shown, an embodiment of this application provides a vehicle control method, including: Step S101: During the vehicle's journey, collect images of the road surface where the vehicle is located. Step S102: The road surface image is processed by the road surface recognition model to determine the road surface type corresponding to the road surface image; Step S103: Determine the vehicle's torque value based on the road surface type; Step S104: Adjust the vehicle's motor torque to the specified torque value.

[0021] In this embodiment, a vehicle control method is proposed to automatically adjust the vehicle's motor torque under different types of road conditions, ensuring both the vehicle's range on normal roads and its handling stability on icy or snowy surfaces.

[0022] For example, the vehicle may specifically be a new energy electric vehicle.

[0023] For example, the vehicle may specifically be an autonomous vehicle.

[0024] During the vehicle's operation, the system controls the acquisition of road surface images of the road where the vehicle is located.

[0025] For example, a panoramic camera is installed on the vehicle, and controlling the panoramic camera can capture images of the road surface where the vehicle is located.

[0026] For example, the road surface image can be a 360-degree panoramic image.

[0027] For example, the road surface image can be a two-dimensional image of the road surface.

[0028] For example, the road surface image can be a three-dimensional image of the road surface.

[0029] Establish a road surface recognition model, which is a model for recognizing the real-time condition of the road surface.

[0030] For example, the road surface recognition model can be specifically a deep learning model.

[0031] For example, the road surface recognition model can be stored in the vehicle's memory.

[0032] By using a road surface recognition model to process road surface images, the road surface type corresponding to the road surface image can be determined. The road surface type refers to the specific type of road surface where the vehicle is located.

[0033] For example, the road surface type can be specifically a normal road surface.

[0034] For example, the road surface type can be specifically an icy road surface.

[0035] For example, the road surface type can be specifically a snow-covered road surface.

[0036] For example, the road surface type can be specifically a rainwater road surface.

[0037] For example, by setting the road surface image as the model input value and inputting the road surface image into the road surface recognition model, the road surface type output by the road surface recognition model can be obtained.

[0038] Based on the road surface type, the torque value of the vehicle is determined, where the torque value is the target torque corresponding to the road surface type.

[0039] For example, a type torque response model can be established, and the torque value corresponding to the road surface type can be determined through the type torque response model.

[0040] For example, a mathematical model is established between road surface type and motor torque. Through the mathematical model, the torque value corresponding to the road surface type can be determined.

[0041] Adjust the torque of the vehicle's motor to a specified value.

[0042] For example, the torque value can be specifically the vehicle's coasting energy recovery torque value.

[0043] For example, take photos of the terrain where the vehicle is traveling using a camera or smartphone. When taking photos, you should consider both objective external factors and subjective factors, such as different shooting angles, different weather conditions, and different light intensities.

[0044] For example, the labeled image dataset is divided into four categories: wet and slippery road surface (label1), ice surface (label2), snow surface (label3), and normal road surface (label4).

[0045] For example, the identified road surface signal is sent to the VCU (Vehicle Control Unit) to obtain the corresponding energy recovery torque.

[0046] It should be noted that this embodiment uses image recognition technology to identify when the vehicle is driving on icy or snowy roads. The coasting energy recovery torque value will adaptively decrease to prevent the wheels from locking up. When the vehicle is driving on a normal road surface, the vehicle will recognize the normal road surface and the coasting energy recovery torque value will be normal. This embodiment ensures the vehicle's range on normal roads as well as its handling stability on icy or snowy roads.

[0047] The vehicle control method in this embodiment uses a road surface recognition model to identify and process road surface images to determine the road surface type corresponding to the road surface image. Based on the road surface type, the torque value of the vehicle is determined, ensuring the applicability of the torque value and thus ensuring the driving stability of the vehicle on slippery roads.

[0048] In some embodiments of this application, a vehicle control method is provided, which acquires road surface images of the road where the vehicle is located, including: Obtain the first image of the road where the vehicle is located; The first image is binarized to update it into the second image; Based on a preset Gaussian function, the second image is denoised to obtain a road surface image.

[0049] In this embodiment, a first image of the road where the vehicle is located is acquired, wherein the first image is an initial image.

[0050] For example, the first image is the raw image data captured by the vehicle.

[0051] The first image is binarized to remove some noise, so that the first image is updated to a second image, where the second image is the filtered image data.

[0052] For example, image binarization can be used to reduce noise interference in the color representation.

[0053] Based on a preset Gaussian function, the second image is denoised to obtain a road surface image.

[0054] For example, image binarization can be used to reduce noise interference in the color representation.

[0055] Gaussian noise is used to denoise images, preserving details, reducing blur, suppressing noise, and smoothing the image. The mathematical expression for Gaussian noise is as follows: ; Where (x, y) represents the coordinates of a pixel in the image, σ represents the standard deviation (this value is adjustable), and a small σ value results in a large center coefficient and small surrounding coefficients in the generated template, thus the image does not show obvious smoothing effects. Conversely, a large σ value results in more obvious image smoothing, and μ represents the expected value of the Gaussian noise. This represents the variance of the Gaussian noise.

[0056] In some embodiments of this application, a vehicle control method is provided, the method further comprising: A first model is established based on convolutional layers, pooling layers, and fully connected layers. The weights of the first model are initialized so that the first model is updated to the second model; Obtain the output value of the second model and determine the error between the output value and the preset value; Based on the error between the output value and the preset value, the second model is iteratively updated to obtain the road surface recognition model.

[0057] In this embodiment, convolutional layers, pooling layers, and fully connected layers are obtained, and a first model is established based on the convolutional layers, pooling layers, and fully connected layers, wherein the first model is an initial model.

[0058] For example, convolutional layers are the core components of convolutional neural networks, extracting local features (such as edges, textures, etc.) of an image through sliding window convolutional kernels (filters).

[0059] For example, pooling layers are placed after convolutional layers. They compress the dimension of feature maps by downsampling (such as max pooling or average pooling), reducing computation and enhancing model robustness. Max pooling preserves the maximum value in local regions, while average pooling takes the mean value. Both can filter noise and retain key features.

[0060] For example, fully connected layers integrate prior features and perform classification or regression tasks through dense connections. Each neuron in a fully connected layer is connected to all neurons in the previous layer, resulting in a large number of parameters.

[0061] The weights of the first model are initialized so that the first model is updated to the second model, where the second model is the initialized model.

[0062] Obtain the output value of the second model and determine the error between the output value and the preset value.

[0063] Based on the error between the output value and the preset value, the second model is iteratively updated to obtain the road surface recognition model.

[0064] For example, such as Figure 2 As shown, the training process of the road surface recognition model is as follows: The first step is to initialize the network weights.

[0065] The second step involves the input data propagating forward through convolutional layers, pooling layers, and fully connected layers to obtain the output value.

[0066] The third step is to calculate the error between the network's output value and the target value.

[0067] Fourth, when the error is less than the expected value, training ends directly. When the error is greater than the expected value, the error is fed back into the network, and the errors of the fully connected layer, pooling layer, and convolutional layer are calculated sequentially.

[0068] The fifth step is to update the weights based on the calculated error, then proceed to the second step and repeat the iteration until the error is less than the expected value. At this point, the training ends directly, and the weights and thresholds are fixed.

[0069] In some embodiments, this application provides a vehicle control method that uses a road surface recognition model to process road surface images to determine the road surface type corresponding to the road surface image, including: Set the road surface image as the input data; Input data is fed into the road surface recognition model to obtain the type coefficients output by the road surface recognition model; Based on the type coefficient, the road surface type corresponding to the road surface image is determined.

[0070] In this embodiment, the road surface image is set as the input data, wherein the input data is the model input value of the road surface recognition model.

[0071] Input data is fed into the road surface recognition model to obtain the type coefficients output by the road surface recognition model, where the type coefficients are coefficients that represent the road surface type.

[0072] For example, the type coefficient can be a coefficient between 0 and 1.

[0073] Based on the type coefficient, the road surface type corresponding to the road surface image is determined.

[0074] For example, when the type coefficient is 0.3, the road surface type can be determined to be ice.

[0075] For example, when the type coefficient is 0.6, the road surface type can be determined to be snow.

[0076] For example, when the type coefficient is 0.9, the road surface type can be determined to be a rainwater road surface.

[0077] For example, when the type coefficient is 0.1, the road surface type can be determined to be normal road surface.

[0078] For example, the 360 ​​camera captures images and sends them to the IVI training model for automatic identification of normal road surface, ice surface, snow surface, and rainy road surface. At the same time, the image signals are processed into CAN signals, with normal road surface images sending signal 0, ice surface sending signal 1, snow surface sending signal 2, and rainy road surface sending signal 3.

[0079] In some embodiments of this application, a vehicle control method is provided, which, after adjusting the vehicle's motor torque to a torque value, further includes: Obtain the vehicle's initial torque; Determine the torque difference between the initial torque and the torque value; The vehicle's regenerated energy is determined based on the torque difference.

[0080] In this embodiment, the initial torque of the vehicle is obtained, and the torque difference between the initial torque and the torque value is determined, wherein the initial torque is the torque before the motor is adjusted.

[0081] The vehicle's regenerated energy is determined based on the torque difference.

[0082] For example, when a vehicle is driving on an icy road, a 360-degree camera captures images of the road surface, which are then processed by a road surface recognition model and a signal 1 is sent. The energy recovery value of the normal road surface is multiplied by the ice surface coefficient.

[0083] For example, when a vehicle is driving on a snowy road, a 360-degree camera captures images of the road surface, which are then processed by a road surface recognition model and a signal 2 is sent. The energy recovery value of the normal road surface is multiplied by the snow surface coefficient.

[0084] For example, when a vehicle is driving on a wet road surface, a 360-degree camera captures images of the road surface, which are then processed by a road surface recognition model and a signal is sent. The energy recovery value of a normal road surface is multiplied by the wet road surface coefficient.

[0085] For example, such as Figure 3 As shown, the steps for model training are as follows: Step 1: Acquire terrain images of the vehicle's driving path and store the images in IVI (In-V Vehicle Infotainment).

[0086] Step 2: Obtain and label the images in the IVI to construct a preliminary dataset for terrain image recognition model of vehicle driving.

[0087] Step 3: Binarize the image to suppress some of the noise caused by the colors, and then use a Gaussian function to denoise the image and smooth it.

[0088] Step four: Label the terrain image data of vehicle driving obtained in step three, and divide it into training set, test set and validation set.

[0089] Step 5: Train the model based on the convolutional neural network and save the optimal trained model.

[0090] Step 6: Integrate the trained model into the IVI to achieve automatic ground image recognition.

[0091] In some embodiments, such as Figure 4 As shown, an embodiment of this application provides a vehicle control device 400, including: The acquisition unit 402 is used to acquire road surface images of the road where the vehicle is located during the vehicle's movement. The first processing unit 404 is used to perform recognition processing on the road surface image through the road surface recognition model in order to determine the road surface type corresponding to the road surface image; The second processing unit 406 is used to determine the torque value of the vehicle based on the road surface type. Control unit 408 is used to adjust the vehicle's motor torque to a torque value.

[0092] In this embodiment, a vehicle control device 400 is proposed to automatically adjust the vehicle's motor torque under different types of road conditions, ensuring both the vehicle's range on normal roads and its handling stability on icy or snowy roads.

[0093] For example, the vehicle may specifically be a new energy electric vehicle.

[0094] For example, the vehicle may specifically be an autonomous vehicle.

[0095] During the vehicle's operation, the system controls the acquisition of road surface images of the road where the vehicle is located.

[0096] For example, a panoramic camera is installed on the vehicle, and controlling the panoramic camera can capture images of the road surface where the vehicle is located.

[0097] For example, the road surface image can be a 360-degree panoramic image.

[0098] For example, the road surface image can be a two-dimensional image of the road surface.

[0099] For example, the road surface image can be a three-dimensional image of the road surface.

[0100] Establish a road surface recognition model, which is a model for recognizing the real-time condition of the road surface.

[0101] For example, the road surface recognition model can be specifically a deep learning model.

[0102] For example, the road surface recognition model can be stored in the vehicle's memory.

[0103] By using a road surface recognition model to process road surface images, the road surface type corresponding to the road surface image can be determined. The road surface type refers to the specific type of road surface where the vehicle is located.

[0104] For example, the road surface type can be specifically a normal road surface.

[0105] For example, the road surface type can be specifically an icy road surface.

[0106] For example, the road surface type can be specifically a snow-covered road surface.

[0107] For example, the road surface type can be specifically a rainwater road surface.

[0108] For example, by setting the road surface image as the model input value and inputting the road surface image into the road surface recognition model, the road surface type output by the road surface recognition model can be obtained.

[0109] Based on the road surface type, the torque value of the vehicle is determined, where the torque value is the target torque corresponding to the road surface type.

[0110] For example, a type torque response model can be established, and the torque value corresponding to the road surface type can be determined through the type torque response model.

[0111] For example, a mathematical model is established between road surface type and motor torque. Through the mathematical model, the torque value corresponding to the road surface type can be determined.

[0112] Adjust the torque of the vehicle's motor to a specified value.

[0113] For example, the torque value can be specifically the vehicle's coasting energy recovery torque value.

[0114] For example, take photos of the terrain where the vehicle is traveling using a camera or smartphone. When taking photos, you should consider both objective external factors and subjective factors, such as different shooting angles, different weather conditions, and different light intensities.

[0115] For example, the labeled image dataset is divided into four categories: wet and slippery road surface (label1), ice surface (label2), snow surface (label3), and normal road surface (label4).

[0116] It should be noted that this embodiment uses image recognition technology to identify when the vehicle is driving on icy or snowy roads. The coasting energy recovery torque value will adaptively decrease to prevent the wheels from locking up. When the vehicle is driving on a normal road surface, the vehicle will recognize the normal road surface and the coasting energy recovery torque value will be normal. This embodiment ensures the vehicle's range on normal roads as well as its handling stability on icy or snowy roads.

[0117] In this embodiment, the vehicle control device 400 uses a road surface recognition model to identify and process road surface images to determine the road surface type corresponding to the road surface image. Based on the road surface type, it determines the torque value of the vehicle, ensuring the applicability of the torque value and thus ensuring the driving stability of the vehicle on slippery roads.

[0118] In some embodiments of this application, a vehicle control device 400 and an acquisition unit 402 are provided, which are further configured to: Obtain the first image of the road where the vehicle is located; The first image is binarized to update it into the second image; Based on a preset Gaussian function, the second image is denoised to obtain a road surface image.

[0119] In some embodiments of this application, a vehicle control device 400 and a first processing unit 404 are provided, which are further configured to: A first model is established based on convolutional layers, pooling layers, and fully connected layers. The weights of the first model are initialized so that the first model is updated to the second model; Obtain the output value of the second model and determine the error between the output value and the preset value; Based on the error between the output value and the preset value, the second model is iteratively updated to obtain the road surface recognition model.

[0120] In some embodiments of this application, a vehicle control device 400 and a first processing unit 404 are provided, which are further configured to: Set the road surface image as the input data; Input data is fed into the road surface recognition model to obtain the type coefficients output by the road surface recognition model; Based on the type coefficient, the road surface type corresponding to the road surface image is determined.

[0121] In some embodiments of this application, a vehicle control device 400 and a second processing unit 406 are provided, further configured to: Obtain the vehicle's real-time speed; The torque value is determined based on the real-time speed and road surface type.

[0122] In some embodiments of this application, a vehicle control device 400 and a second processing unit 406 are provided, further configured to: When the road surface is ice, the torque value is determined to be the first torque. When the road surface is snow, the torque value is determined to be the second torque. When the road surface type is a rain-prone road surface, the torque value is determined to be the third torque. When the road surface type is normal, the torque value is determined to be the fourth torque. The fourth torque is greater than the first, second, and third torques.

[0123] In some embodiments of this application, a vehicle control device 400 is provided, which further includes a third processing unit; The third processing unit is used for: Obtain the vehicle's initial torque; Determine the torque difference between the initial torque and the torque value; The vehicle's regenerated energy is determined based on the torque difference.

[0124] In some embodiments, such as Figure 5 As shown, a vehicle control device 500 is proposed. The vehicle control device 500 includes a processor 502 and a memory 504. The memory 504 stores a computer program, which, when executed by the processor 502, implements the steps of the vehicle control method as described in any of the above embodiments. Therefore, the vehicle control device 500 possesses all the beneficial effects of the vehicle control method in any of the above embodiments, which will not be elaborated further here.

[0125] In some embodiments, a readable storage medium is provided having a program stored thereon, which, when executed by a processor, implements the steps of the vehicle control method as described in any of the above embodiments, and thus has all the beneficial technical effects of the vehicle control method described in any of the above embodiments.

[0126] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0127] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0128] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to execute a process of a vehicle control method.

[0132] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0139] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0140] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. A method for controlling a vehicle, characterized in that, The method includes: During the vehicle's journey, images of the road surface where the vehicle is located are captured. The road surface image is processed by a road surface recognition model to determine the road surface type corresponding to the road surface image; Based on the road surface type, determine the torque value of the vehicle; The motor torque of the vehicle is adjusted to the specified torque value.

2. The method according to claim 1, characterized in that, The acquisition of road surface images of the road where the vehicle is located includes: Acquire a first image of the road where the vehicle is located; The first image is binarized to update it into the second image; The second image is denoised based on a preset Gaussian function to obtain the road surface image.

3. The method according to claim 1, characterized in that, The method further includes: A first model is established based on the convolutional layer, the pooling layer, and the fully connected layer. The weights of the first model are initialized so that the first model is updated to the second model; Obtain the output value of the second model and determine the error between the output value and the preset value; Based on the error between the output value and the preset value, the second model is iteratively updated to obtain the road surface recognition model.

4. The method according to claim 1, characterized in that, The step of using a road surface recognition model to identify and process the road surface image to determine the road surface type corresponding to the road surface image includes: Set the road surface image as input data; The input data is fed into the road surface recognition model to obtain the type coefficients output by the road surface recognition model; Based on the type coefficient, the road surface type corresponding to the road surface image is determined.

5. The method according to claim 1, characterized in that, Determining the vehicle's torque value based on the road surface type includes: Obtain the real-time speed of the vehicle; The torque value is determined based on the real-time speed and the road surface type.

6. The method according to claim 1, characterized in that, Determining the vehicle's torque value based on the road surface type includes: When the road surface type is ice, the torque value is determined to be the first torque; When the road surface type is snow, the torque value is determined to be the second torque; When the road surface type is a rain-prone road surface, the torque value is determined to be the third torque; When the road surface type is normal, the torque value is determined to be the fourth torque. The fourth torque is greater than the first torque, the second torque, and the third torque.

7. The method according to any one of claims 1 to 6, characterized in that, After adjusting the motor torque of the vehicle to the specified torque value, the method further includes: Obtain the initial torque of the vehicle; Determine the torque difference between the initial torque and the torque value; The recovered energy of the vehicle is determined based on the torque difference.

8. A vehicle control device, characterized in that, The device includes: The acquisition unit is used to acquire road surface images of the road where the vehicle is located during the vehicle's movement. The first processing unit is used to perform recognition processing on the road surface image through a road surface recognition model to determine the road surface type corresponding to the road surface image; The second processing unit is used to determine the torque value of the vehicle based on the road surface type. The control unit is used to adjust the motor torque of the vehicle to the specified torque value.

9. A vehicle control device, characterized in that, include: processor; A memory, which stores programs or instructions, wherein a processor, when executing the programs or instructions in the memory, implements the steps of the vehicle control method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, A program or instructions are stored on a readable storage medium, which, when executed by a processor, implement the steps of the vehicle control method as described in any one of claims 1 to 7.

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