A vehicle anti-skid control method and device, electronic equipment and storage medium

Abstract

The application provides a vehicle anti-skid control method and device, electronic equipment and a storage medium. The current road surface image, the environmental temperature parameter, the driving rated torque and the theoretical maximum torque are obtained, the target image processing strategy is matched according to the size relationship between the environmental temperature parameter and the preset environmental threshold temperature, the road surface type information and the initial road surface information are determined, if the initial road surface information exists in the preset road surface control scenario list, the current road surface information is determined, the first road condition coefficient is matched based on the current road surface information and the road surface type information, the associated second road condition coefficient is matched based on the current road surface information, and the actual torque is determined according to the first road condition coefficient, the second road condition coefficient, the driving rated torque and the theoretical maximum torque. According to the method, the current road surface information is determined through image recognition and road condition sensing data cooperation, and the actual anti-skid control torque is determined by multiple torque parameters, so that the wet and slippery type of the road surface is accurately determined, and the low anti-skid efficiency caused by the multi-class identification of the image is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and more particularly to a vehicle anti-skid control method, device, electronic device, and storage medium. Background Technology

[0002] In complex and ever-changing road environments, drivers face severe driving challenges, especially when the road surface is flooded, icy, or snowy. The vehicle's stability and safety are severely tested. Flooded, icy, snowy, and wet roads reduce tire traction, making it difficult for the tires to gain sufficient grip during acceleration. When the driver attempts to accelerate, insufficient friction between the tires and the road can easily cause the vehicle to skid or even fishtail, seriously threatening driving safety.

[0003] For vehicles capable of generating significant torque, such as new energy vehicles driven by electric motors and high-horsepower gasoline vehicles, their efficient power response and strong torque output are significant advantages. However, on slippery roads, this instantaneous high torque can become a safety hazard. Due to the reduced friction between the tires and the ground, the vehicle is prone to significant slippage during acceleration, leading to loss of vehicle control. This not only seriously affects the driver's driving experience but may also have serious safety consequences. Therefore, anti-skid treatment for vehicles is always an important part of safety measures.

[0004] Existing technologies primarily activate anti-skid modes based on whether the road surface is wet or slippery. However, this significantly reduces driving maneuverability and causes user discomfort. Furthermore, existing wet-slip detection methods rely solely on image recognition, which cannot accurately determine the type of road surface slipperiness. Additionally, the recognition response time is long, resulting in imprecise control management and low efficiency in anti-skid control management. Summary of the Invention

[0005] The purpose of this invention is to provide a vehicle anti-skid control method, device, electronic device, and storage medium to solve the above-mentioned technical problems.

[0006] This invention provides a vehicle anti-skid control method, comprising: acquiring a current road surface image and ambient temperature parameters, as well as the vehicle's rated driving torque, theoretical maximum torque, and current throttle opening; comparing the ambient temperature parameters with a preset ambient threshold temperature, and matching a target image processing strategy based on the magnitude relationship between the ambient temperature parameters and the preset ambient threshold temperature; identifying the current road surface image according to the target image processing strategy to obtain road surface type information and initial road surface information; if the initial road surface information exists in a preset road surface control scenario list, triggering a target sensing device to acquire current road condition sensing data, and determining the current road surface information based on the road condition sensing data and the initial road surface information; matching a first road condition coefficient associated with the current road surface information and the road surface type information, and matching a second road condition coefficient associated with the current road surface information and the current throttle opening; determining a first torque limit based on the first road condition coefficient and the rated driving torque, determining a second torque limit based on the second road condition coefficient and the theoretical maximum torque, and determining the actual torque based on the first torque limit and the second torque limit, so as to control the vehicle according to the actual torque.

[0007] In one embodiment of the present invention, after determining the current road surface information based on the road condition sensing data and the initial road surface information, the vehicle anti-skid control method further includes: if the current road surface information is included in the preset list of slippery roads, an anti-skid warning is issued at the control terminal, and anti-skid feedback information is received from the control terminal, the anti-skid feedback information including activating the anti-skid mode, deactivating the anti-skid mode, and no response; if the anti-skid feedback information is activating the anti-skid mode or no response, the anti-skid mode is activated, and a first road condition coefficient is matched and associated based on the current road surface information and the road surface type information, and a second road condition coefficient is matched and associated based on the current road surface information; if the anti-skid feedback information is deactivating the anti-skid mode, the current vehicle anti-skid control is terminated.

[0008] In one embodiment of the present invention, the target image processing strategy includes a first processing strategy and a second processing strategy. Matching the target image processing strategy according to the relationship between the ambient temperature parameter and the preset ambient threshold temperature includes: if the ambient temperature parameter is greater than the preset ambient threshold temperature, identifying the current road surface image based on the first processing strategy; if the ambient temperature parameter is less than or equal to the preset ambient threshold temperature, identifying the current road surface image based on the second processing strategy.

[0009] In one embodiment of the present invention, identifying the current road surface image based on the first processing strategy includes: performing feature road surface identification on the current road surface image to determine the road surface image to be processed; inputting the road surface image to be processed into a preset road surface type identification model to obtain road surface type information; performing grayscale processing on the road surface image to be processed and calculating grayscale values; performing image segmentation on the image to be processed after calculating grayscale values ​​to obtain multiple image regions to be processed with different grayscale values; determining initial road surface information based on the proportion of the area of ​​the image to be processed with grayscale values ​​greater than a preset water accumulation grayscale threshold, wherein the initial road surface information includes roads with a large amount of water accumulation, roads with a small amount of water accumulation, and roads without water accumulation.

[0010] In one embodiment of the present invention, the identification of the current road surface image based on the second processing strategy includes: performing feature road surface identification on the current road surface image to determine the road surface image to be processed; inputting the road surface image to be processed into a preset road surface type identification model to obtain road surface type information; sending the road surface image to be processed to the cloud, the cloud inputting the road surface image to be processed into a preset snow accumulation identification model to obtain initial road surface information and sending the initial road surface information back to the vehicle, wherein the initial road surface information includes snow-covered roads and snow-free roads, and the preset snow accumulation identification model is set in the cloud.

[0011] In one embodiment of the present invention, the preset road surface control scenario list includes roads with a large amount of water accumulation, roads with a small amount of water accumulation, roads without water accumulation, and roads without snow accumulation. Determining the current road surface information based on the road condition sensing data and the initial road surface information includes at least one of the following: if the initial road surface information is a road with a large amount of water accumulation, then the water depth is determined based on the road condition sensing data, and the current road surface information is determined to be either a deep water accumulation road or a shallow water accumulation road based on the water depth; if the initial road surface information is a road with a small amount of water accumulation, then the road surface wetness is determined based on the road condition sensing data, and the current road surface information is determined to be either a wet road or a dry road based on the wetness; if the initial road surface information is a road with no water accumulation, then the road surface wetness is determined based on the road condition sensing data, and the current road surface information is determined to be either a wet road or a dry road based on the wetness; if the initial road surface information is a road without snow accumulation, then the road surface icing is determined based on the road condition sensing data, and the current road surface information is determined to be either an icy road or a dry road based on the icing.

[0012] In one embodiment of the present invention, determining the actual torque based on a first torque limit and a second torque limit includes: determining the minimum value of the first torque limit and the second torque limit as the actual torque; or, determining the weighted average value of the first torque limit and the second torque limit as the actual torque.

[0013] This invention also provides a vehicle anti-skid control device, comprising: an anti-skid control parameter acquisition module for acquiring a current road surface image and ambient temperature parameters, as well as the vehicle's rated driving torque, theoretical maximum torque, and current throttle opening; an initial road surface information determination module for comparing the ambient temperature parameters with a preset ambient threshold temperature, and matching a target image processing strategy based on the relationship between the ambient temperature parameters and the preset ambient threshold temperature; identifying the current road surface image according to the target image processing strategy to obtain road surface type information and initial road surface information; and a current road surface information determination module for configuring a preset road surface control scenario list. If the initial road surface information exists, the target sensing device is triggered to acquire the current road condition sensing data, and the current road surface information is determined based on the road condition sensing data and the initial road surface information. The anti-skid control torque determination module is used to match a first road condition coefficient based on the current road surface information and the road surface type information, and to match a second road condition coefficient based on the current road surface information and the current throttle opening. A first torque limit is determined based on the first road condition coefficient and the rated driving torque, a second torque limit is determined based on the second road condition coefficient and the theoretical maximum torque, and the actual torque is determined based on the first torque limit and the second torque limit, so as to control the vehicle based on the actual torque.

[0014] This invention also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the vehicle anti-skid control method as described in any of the above embodiments.

[0015] This invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a computer's processor, cause the computer to perform the vehicle anti-skid control method as described in any of the above embodiments.

[0016] This invention provides a vehicle anti-skid control method, device, electronic device, and storage medium. It acquires a current road surface image, ambient temperature parameters, rated driving torque, and theoretical maximum torque. Based on the relationship between the ambient temperature parameters and a preset ambient threshold temperature, it matches a target image processing strategy to determine road surface type information and initial road surface information. If initial road surface information exists in a preset road surface control scenario list, it determines the current road surface information. Based on the current road surface information and road surface type information, it matches a first road condition coefficient and an associated second road condition coefficient. Finally, it determines the vehicle anti-skid control based on the first road condition coefficient, the second road condition coefficient, the rated driving torque, and the theoretical maximum torque. This method determines the actual torque by combining image recognition and road condition sensor data. After initial determination by image recognition, further refined road condition identification can be performed based on sensor data. Compared to single image recognition of road condition information, which requires multi-model recognition processing, this method firstly reduces the difficulty of identification and the investment in function development, and secondly reduces the identification response time, improving the management efficiency of anti-skid control. Furthermore, different torque control limits are determined based on the rated driving torque and the theoretical maximum torque. The actual anti-skid control torque is determined based on the above torque control limits, which can more accurately output the anti-skid torque and improve the anti-skid effect.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0019] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating a vehicle anti-skid control method in an exemplary embodiment of this application;

[0021] Figure 3 This is a schematic flowchart illustrating a specific method for determining current road surface information, as shown in an exemplary embodiment of this application.

[0022] Figure 4 This is a schematic diagram of a vehicle anti-skid control device shown in an exemplary embodiment of this application;

[0023] Figure 5This is a schematic diagram of the structure of a computer system for an electronic device, as illustrated in an exemplary embodiment of this application. Detailed Implementation

[0024] The embodiments of the present invention will be described below with reference to the accompanying drawings and specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the illustrations only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0026] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0027] The term "and / or" used in this application describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship.

[0028] First, it should be noted that vehicle-mounted road condition sensors are used to monitor and analyze various conditions on the road surface in real time to improve road safety and traffic management efficiency. They mainly utilize technologies such as laser remote sensing, infrared spectroscopy, and multispectral measurement. By analyzing the spectrum or energy of the emitted light and receiving the feedback, they can determine the road surface condition and the thickness of the covering. This allows for quick and real-time determination of whether the road is wet or icy while the vehicle is in motion, as well as the detection of water depth. By collecting data non-contactly, they can monitor and accurately measure different road surface conditions in real time.

[0029] The beneficial effects that this application can also provide include: the addition of onboard road condition sensors for auxiliary judgment can effectively compensate for the low accuracy of image recognition in recognizing wet and icy roads; it can expand the scope of application by judging different road conditions such as wetness, ice, snow, water accumulation and water depth; and it can increase the intelligent control and interactivity of the cockpit, and enhance the user's intelligent experience.

[0030] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application.

[0031] Reference Figure 1 As shown, the system architecture may include a vehicle 110 and a computer device 120. The computer device 120 acquires the current road surface image and ambient temperature parameters from the vehicle 110, and obtains the vehicle's rated driving torque and theoretical maximum torque. It compares the ambient temperature parameters with a preset ambient temperature threshold, and matches a target image processing strategy based on the relationship between the ambient temperature parameters and the preset ambient temperature threshold. It then identifies the current road surface image according to the target image processing strategy to obtain road surface type information and initial road surface information. If initial road surface information exists in the preset road surface control scenario list, the target sensing device of the vehicle 110 is triggered to acquire current road condition sensing data. Based on the road condition sensing data and the initial road surface information, the current road surface information is determined. A first road condition coefficient is matched based on the current road surface information and the road surface type information, and a second road condition coefficient is matched based on the current road surface information and the current throttle opening. A first torque limit is determined based on the first road condition coefficient and the rated driving torque, and a second torque limit is determined based on the second road condition coefficient and the theoretical maximum torque. Finally, the actual torque is determined based on the first and second torque limits, so that the vehicle 110 can control the vehicle according to the actual torque. The aforementioned computer equipment 120 may be at least one of a microcomputer, an embedded computer, a network computer, a single-chip microcomputer, etc.; the aforementioned vehicle 110 includes at least an image acquisition device, an ambient temperature sensor, an on-board road condition sensor, and a vehicle controller.

[0032] In a schematic manner, computer device 120 acquires the current road surface image, ambient temperature parameters, rated driving torque, and theoretical maximum torque through vehicle 110. Based on the relationship between the ambient temperature parameters and the preset ambient threshold temperature, it matches the target image processing strategy to determine the road surface type information and initial road surface information. If the initial road surface information exists in the preset road surface control scenario list, it determines the current road surface information. Based on the current road surface information and road surface type information, it matches the first road condition coefficient and the associated second road condition coefficient. Based on the current road surface information, it matches the second road condition coefficient, the rated driving torque, and the theoretical maximum torque, it determines the actual torque. This method uses image recognition and road condition sensing data to collaboratively determine the current road surface information. After the initial determination by image recognition, further refined road condition identification can be performed based on sensing data. Compared to road condition information obtained from a single image, which requires multi-model recognition processing, this method firstly reduces the difficulty of recognition and the investment in function development, and secondly reduces the recognition response time, improving the management efficiency of anti-skid control. Furthermore, different torque control limits are determined based on the rated driving torque and the theoretical maximum torque. The actual anti-skid control torque is then determined based on these torque control limits, allowing for more accurate output of the anti-skid torque and improving the anti-skid effect.

[0033] Figure 2 This is a flowchart illustrating an exemplary embodiment of the present application of a vehicle anti-skid control method, which can be implemented in... Figure 1 It can be executed in the implementation environment described above, but it can also be implemented in other implementation environments. No specific limitations are imposed on the aforementioned implementation environments here. (See also...) Figure 2 As shown, the flowchart of the vehicle anti-skid control method includes at least steps S210 to S260, which are described in detail below:

[0034] In step S210, the current road surface image and ambient temperature parameters, as well as the vehicle's rated driving torque, theoretical maximum torque, and current throttle opening are obtained.

[0035] In one embodiment of this application, the aforementioned current road surface image is acquired based on an image acquisition device installed on the vehicle, and the aforementioned ambient temperature parameter is acquired based on an ambient temperature sensor installed on the vehicle.

[0036] In one embodiment of this application, the above-mentioned rated driving torque is the normal driving torque after the actual vehicle power calibration, that is, the torque value output by the vehicle during actual driving; while the theoretical maximum torque is the maximum torque value output by the vehicle's hardware facilities under ideal experimental conditions during the vehicle's design process.

[0037] In step S220, the ambient temperature parameter and the preset ambient threshold temperature are compared, and the target image processing strategy is matched according to the relationship between the ambient temperature parameter and the preset ambient threshold temperature.

[0038] In one embodiment of this application, the aforementioned preset ambient temperature threshold is used to characterize the distinction threshold of the driving environment. For example, when the ambient temperature is above 1 degree Celsius, only wet and slippery road conditions such as water accumulation and dampness will occur on the road, while when the temperature is below 1 degree Celsius, wet and slippery road conditions such as snow accumulation and ice will occur. In this embodiment of the application, the aforementioned preset ambient temperature threshold is set to 1 degree Celsius. It should be noted that the aforementioned preset ambient temperature threshold can be further set according to the regional conditions and control accuracy requirements. The specific value of the aforementioned preset ambient temperature threshold is not limited here.

[0039] In one embodiment of this application, the relationship between the above-mentioned ambient temperature parameter and the preset ambient threshold temperature includes the ambient temperature parameter being greater than the preset ambient threshold temperature, and the ambient temperature parameter being less than or equal to the preset ambient threshold temperature.

[0040] In one embodiment of this application, the target image processing strategy includes a first processing strategy and a second processing strategy. The first and second processing strategies differ. In this embodiment, if the ambient temperature parameter is greater than a preset ambient threshold temperature, the current road surface image is identified based on the first processing strategy; if the ambient temperature parameter is less than or equal to the preset ambient threshold temperature, the current road surface image is identified based on the second processing strategy.

[0041] In step S230, the current road surface image is identified according to the target image processing strategy to obtain road surface type information and initial road surface information.

[0042] In one embodiment of this application, if the ambient temperature parameter is greater than a preset ambient threshold temperature, the current road surface image is identified based on a first processing strategy.

[0043] In one embodiment of this application, feature road surface recognition is performed on the current road surface image to determine the road surface image to be processed. The road surface image to be processed is input into a preset road surface type recognition model to obtain road surface type information. Grayscale processing is performed on the road surface image to be processed and grayscale values ​​are calculated. The image to be processed after calculating grayscale values ​​is segmented based on the grayscale values ​​to obtain multiple image regions to be processed with different grayscale values. The initial road surface information is determined according to the proportion of the area of ​​the image region to be processed with grayscale values ​​greater than a preset water accumulation grayscale threshold to the area of ​​the road surface image to be processed. The initial road surface information includes roads with a lot of water accumulation, roads with a little water accumulation, and roads without water accumulation.

[0044] In one embodiment of this application, feature road surface recognition is performed on the current road surface image to determine the road surface image to be processed. This includes the vehicle controller selecting the ROI of the captured road surface image and selecting the road surface area based on road surface features to obtain the road surface image to be processed.

[0045] In one embodiment of this application, the aforementioned preset road surface type recognition model can be set in the vehicle controller or in the cloud. If the preset road surface type recognition model is set in the vehicle controller, the road surface image to be processed is directly input into the preset road surface type recognition model to obtain road surface type information. If the preset road surface type recognition model is set in the cloud, the road surface image to be processed is transmitted to the cloud, and the cloud inputs the road surface image to be processed into the preset road surface type recognition model to obtain road surface type information, and then sends the road surface type information back to the vehicle. Specifically, deep learning is used to extract features from the road surface image to be processed, separating the target road surface from the surrounding area, separating the target road surface from the surrounding complex background, and enhancing only the target road surface portion.

[0046] In one embodiment of this application, a grayscale image of the road surface to be processed is processed and a grayscale value is calculated. The image to be processed after the grayscale value is calculated is segmented based on the grayscale value to obtain multiple image regions to be processed with different grayscale values. This includes grayscale processing of the relative image to obtain a grayscale image to be processed. The grayscale image to be processed is binarized and image noise is eliminated. Finally, numerical normalization is performed to obtain an initial grayscale image to be processed. Image segmentation is performed based on the grayscale values ​​in the initial grayscale image to be processed to obtain multiple image regions to be processed with different grayscale values.

[0047] In one embodiment of this application, initial road surface information is determined based on the proportion of the area of ​​the image to be processed with a gray value greater than a preset water accumulation gray value threshold to the area of ​​the road surface to be processed. The initial road surface information includes roads with a large amount of water accumulation, roads with a small amount of water accumulation, and roads without water accumulation. If the proportion of the area is greater than or equal to the water accumulation area proportion threshold DO, it is determined to be a road with a large amount of water accumulation. If the proportion of the area is less than the water accumulation area proportion threshold DO but greater than 0, it is determined to be a road with a small amount of water accumulation. If the proportion of the area is equal to 0, it is determined to be a road without water accumulation.

[0048] In one embodiment of this application, if the ambient temperature parameter is less than or equal to a preset ambient threshold temperature, the current road surface image is identified based on the second processing strategy.

[0049] In one embodiment of this application, feature road surface recognition is performed on the current road surface image to determine the road surface image to be processed. The road surface image to be processed is input into a preset road surface type recognition model to obtain road surface type information. The road surface image to be processed is sent to the cloud. The cloud inputs the road surface image to be processed into a preset snow accumulation recognition model to obtain initial road surface information and sends the initial road surface information back to the vehicle. The initial road surface information includes snow-covered roads and snow-free roads. The preset snow accumulation recognition model is set in the cloud.

[0050] In one embodiment of this application, the aforementioned preset road surface type recognition model can be set in the vehicle controller or in the cloud. If the preset road surface type recognition model is set in the vehicle controller, the road surface image to be processed is directly input into the preset road surface type recognition model to obtain road surface type information. If the preset road surface type recognition model is set in the cloud, the road surface image to be processed is transmitted to the cloud, and the cloud inputs the road surface image to be processed into the preset road surface type recognition model to obtain road surface type information, and then sends the road surface type information back to the vehicle. Specifically, deep learning is used to extract features from the road surface image to be processed, separating the target road surface from the surrounding area, separating the target road surface from the surrounding complex background, and enhancing only the target road surface portion.

[0051] In one embodiment of this application, a snow-covered road surface recognition model is constructed based on deep learning. The road surface image to be processed is input into the preset snow-covered road surface recognition model to determine whether there is snow. The judgment result is fed back to the vehicle by the cloud. The vehicle determines the initial road surface information as a snow-covered road surface or a snow-free road surface based on the judgment result.

[0052] In step S240, if initial road information exists in the preset road control scenario list, the target sensing device is triggered to acquire current road condition sensing data, and the current road information is determined based on the road condition sensing data and the initial road information.

[0053] In one embodiment of this application, the target sensing device can be a vehicle-mounted road condition sensor, or a combination of multiple sensors capable of providing the same sensing data as the vehicle-mounted road condition sensor. In this embodiment, a vehicle-mounted road condition sensor is selected as the target sensing device. The vehicle-mounted road condition sensor is used to monitor and analyze various conditions of the road surface in real time to improve road safety and traffic management efficiency. It mainly utilizes technologies such as laser remote sensing, infrared spectroscopy, and multispectral measurement to determine the road surface condition and the thickness of the covering by analyzing the spectrum or energy of the emitted light. This allows for rapid, real-time determination of whether the road is wet or icy while the vehicle is in motion, as well as detection of water depth. It collects data non-contactly and can monitor and accurately measure different road conditions in real time. Adding a vehicle-mounted road condition sensor for auxiliary judgment can effectively compensate for the low accuracy of image recognition in identifying wet and icy roads.

[0054] In one embodiment of this application, the preset road control scenario list includes roads with a large amount of water accumulation, roads with a small amount of water accumulation, roads without water accumulation, and roads without snow accumulation.

[0055] In one embodiment of this application, if the initial road surface information is a road surface with a large amount of water accumulation, the water depth is determined based on road condition sensor data, and the current road surface information is determined to be either a deep water accumulation road surface or a shallow water accumulation road surface based on the water depth. The water depth h is determined based on the road condition sensor data. If the water depth h is greater than or equal to a preset hydroplaning threshold h0, the current road surface information is determined to be a deep water accumulation road surface; if the water depth h is less than the preset hydroplaning threshold h0, the current road surface information is determined to be a shallow water accumulation road surface.

[0056] In one embodiment of this application, if the initial road surface information is a road surface with a small amount of water accumulation, the road surface wetness is determined based on the road condition sensor data, and the current road surface information is determined to be a wet road surface or a dry road surface based on the road surface wetness.

[0057] In one embodiment of this application, if the initial road surface information is a road surface without water accumulation, the road surface wetness is determined based on the road condition sensor data, and the current road surface information is determined to be a wet road surface or a dry road surface based on the road surface wetness.

[0058] In one embodiment of this application, if the initial road surface information is a road surface without snow accumulation, the road surface icing state is determined based on road condition sensor data, and the current road surface information is determined to be an icy road surface or a dry road surface based on the road surface icing state.

[0059] In one embodiment of this application, after determining the current road surface information based on road condition sensor data and initial road surface information, if the current road surface information is included in the preset list of slippery roads, an anti-skid warning is issued at the control terminal, and anti-skid feedback information is received from the control terminal. The anti-skid feedback information includes activating the anti-skid mode, deactivating the anti-skid mode, and no response. If the anti-skid feedback information is activating the anti-skid mode or no response, the anti-skid mode is activated, and a first road condition coefficient associated with the current road surface information and road surface type information is matched, and a second road condition coefficient associated with the current road surface information is matched. If the anti-skid feedback information is deactivating the anti-skid mode, the current vehicle anti-skid control is terminated.

[0060] In one embodiment of this application, an anti-slip warning on the control terminal includes providing customers with corresponding road condition prompts based on different roads and road conditions, and prompting users whether to enable the "intelligent anti-slip" setting.

[0061] In step S250, a first road condition coefficient is matched and associated based on the current road surface information and road surface type information, and a second road condition coefficient is matched and associated based on the current road surface information and current throttle opening.

[0062] In one embodiment of this application, a first road condition coefficient is matched and associated based on the current road surface information and road surface type information according to the contents of Table 1. Table 1 is an exemplary illustration of the first road condition coefficient correspondence determination table in an embodiment of this application, as shown in Table 1:

[0063] Table 1. Schematic diagram of the correspondence between the first road condition coefficients.

[0064]

[0065] In one embodiment of this application, the first road condition coefficient is matched based on current road surface information and road surface type information. Examples of road surface type information in the table include asphalt pavement, concrete pavement, and gravel pavement, but it can also include various other types of road surface information, without specific limitations. The current road surface information in the table includes deep waterlogged pavement (h≥h0), shallow waterlogged pavement (h<h0), wet pavement, dry pavement, icy pavement, and snow-covered pavement. The value range of the first road condition coefficient can be determined based on the vehicle model, current driving conditions, and driving habits. In some other embodiments, the first road condition coefficient in Table 1 can be set to a fixed value.

[0066] In one embodiment of this application, a second road condition coefficient is determined based on the current road surface information and the current throttle opening, according to the contents of Table 2. Table 2 is an exemplary diagram of the second road condition coefficient correspondence determination in an embodiment of this application, as shown in Table 2:

[0067] Table 2. Schematic diagram of the correspondence between the second road condition coefficients.

[0068]

[0069] In one embodiment of this application, the second road condition coefficient is matched based on the current road surface information and the current throttle opening. The throttle opening range in the table includes 0-100, and is not specifically limited here. The current road surface information in the table includes wet road surface, dry road surface, icy road surface, and snow-covered road surface. Among them, deep water-covered road surface (h≥h0) and shallow water-covered road surface (h<h0) are both classified as wet road surface in Table 2.

[0070] It should be noted that the exemplary examples shown in Tables 1 and 2 above are only one embodiment of the present application. The specific values ​​can also be determined based on specific experiments. The specific values ​​of the road condition coefficients are not specifically limited here.

[0071] In step S260, a first torque limit is determined based on a first road condition coefficient and a rated driving torque, a second torque limit is determined based on a second road condition coefficient and a theoretical maximum torque, and an actual torque is determined based on the first torque limit and the second torque limit, so as to control the vehicle according to the actual torque.

[0072] In one embodiment of this application, an exemplary method for determining a first torque limit based on a first road condition coefficient and a rated driving torque, and a second torque limit based on a second road condition coefficient and a theoretical maximum torque, includes determining the first torque limit by multiplying the first road condition coefficient and the rated driving torque; and determining the second torque limit by multiplying the second road condition coefficient and the theoretical maximum torque. In some other embodiments of this application, a correction coefficient is introduced to further numerically correct the first and second torque limits, wherein some feasible methods for determining the correction coefficient include determining it based on vehicle driving time, tire condition, and control requirements.

[0073] In one embodiment of this application, determining the actual torque based on the first torque limit and the second torque limit includes determining the minimum value of the first torque limit and the second torque limit as the actual torque, or determining the weighted average value of the first torque limit and the second torque limit as the actual torque.

[0074] In the embodiments of this application, the actual torque of the vehicle is Tout = Min(T1,T2), that is, the smaller value between T1 and T2 is taken as the actual driving torque, where T1 is the first torque limit, T2 is the second torque limit, and Tout is the actual torque.

[0075] Please see Figure 3 , Figure 3 This is a schematic flowchart illustrating a specific current road surface information determination method according to an exemplary embodiment of this application. Figure 3 The method shown can be used in Figure 1 The implementation environment shown can be executed in other implementation environments as well, and no specific limitation is made here.

[0076] like Figure 3 As shown, in a specific embodiment of this application, the vehicle's external camera captures the road surface and selects the road surface. If the ambient temperature is less than or equal to 1°C, the system compares the road surface type with the database and identifies whether there is snow accumulation on the road surface. If there is no snow accumulation on the road surface, the vehicle's on-board road condition sensor is triggered to detect the actual road surface condition in order to determine whether the road surface is icy or not.

[0077] In one specific embodiment of this application, if the ambient temperature is greater than 1°C, the gray values ​​of each region in the image are preprocessed and then the gray values ​​of each region are determined to be greater than the restaurant threshold value G0. If the determination result is G < G0, it is determined that there is no water accumulation in the area and the road surface is dry.

[0078] In one specific embodiment of this application, if the determination result is G < G0, it is determined that there is water accumulation in the area. The proportion of areas with G ≥ G0 is counted. If the proportion is greater than or equal to DO, it is determined that there is a large area of ​​water accumulation on the road surface, triggering the vehicle-mounted road condition sensor to detect the actual road surface condition in order to detect the water depth h.

[0079] If the percentage is less than DO, it is determined that there is no water accumulation on the road surface or the water accumulation area is small, triggering the vehicle's road condition sensor to detect the actual road surface condition in order to determine whether the road surface is wet or dry.

[0080] This invention provides a vehicle anti-skid control method, device, electronic device, and storage medium. It acquires current road surface images, ambient temperature parameters, rated driving torque, and theoretical maximum torque. Based on the relationship between the ambient temperature parameters and a preset ambient threshold temperature, it matches a target image processing strategy to determine road surface type information and initial road surface information. If initial road surface information exists in a preset road surface control scenario list, the current road surface information is determined. A first road condition coefficient is matched based on the current road surface information and road surface type information, and a second road condition coefficient is matched based on the current road surface information. The actual torque is determined based on the first road condition coefficient, the second road condition coefficient, the rated driving torque, and the theoretical maximum torque. This method uses image recognition and road condition sensing data to collaboratively determine the current road surface information, allowing for further determination based on sensing data after initial determination via image recognition. The refined road condition recognition method, compared to single-image road condition recognition which requires multi-model recognition processing, firstly reduces the difficulty of recognition and the investment in function development; secondly, it reduces recognition response time and improves the management efficiency of anti-skid control. Furthermore, by determining different torque control limits based on the rated driving torque and the theoretical maximum torque, and then determining the actual anti-skid control torque based on these torque control limits, the anti-skid torque can be output more accurately, improving the anti-skid effect. Other beneficial effects include: the addition of onboard road condition sensors for auxiliary judgment can effectively compensate for the low accuracy of image recognition on wet and icy roads; it can identify different road conditions such as wetness, ice, snow, water accumulation, and water depth, expanding its applicability; and it increases intelligent cockpit control and interactivity, enhancing the user's intelligent experience.

[0081] The following describes an embodiment of the apparatus described in this application, which can be used to execute the vehicle anti-skid control method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the vehicle anti-skid control method described in the above embodiments of this application.

[0082] Figure 4 This is a schematic diagram illustrating a vehicle anti-skid control device according to an exemplary embodiment of this application. The device can be applied to… Figure 2 The method implementation process shown can be based on the device Figure 1The implementation environment shown can be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0083] like Figure 4 As shown, the exemplary vehicle anti-skid control device includes: an anti-skid control parameter acquisition module 401, an initial road surface information determination module 402, a current road surface information determination module 403, and an anti-skid control torque determination module 404.

[0084] The system includes: an anti-skid control parameter acquisition module 401, which acquires the current road surface image and ambient temperature parameters, as well as the vehicle's rated driving torque, theoretical maximum torque, and current throttle opening; an initial road surface information determination module 402, which compares the ambient temperature parameters with a preset ambient threshold temperature and matches a target image processing strategy based on the relationship between the ambient temperature parameters and the preset ambient threshold temperature; and identifies the current road surface image according to the target image processing strategy to obtain road surface type information and initial road surface information; a current road surface information determination module 403, which, if initial road surface information exists in the preset road surface control scenario list, triggers the target sensing device to acquire current road condition sensing data and determines the current road surface information based on the road condition sensing data and the initial road surface information; and an anti-skid control torque determination module 404, which matches a first road condition coefficient associated with the current road surface information and road surface type information, and matches a second road condition coefficient associated with the current road surface information and current throttle opening; determines a first torque limit based on the first road condition coefficient and the rated driving torque, determines a second torque limit based on the second road condition coefficient and the theoretical maximum torque, and determines the actual torque based on the first torque limit and the second torque limit, so as to control the vehicle based on the actual torque.

[0085] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the vehicle anti-skid control method provided in the above embodiments.

[0086] Figure 5 This is a schematic diagram illustrating the structure of a computer system for an electronic device, as shown in an exemplary embodiment of this application. It should be noted that... Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0087] like Figure 5As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 502 or a program loaded from storage into Random Access Memory (RAM) 503, such as performing the methods described in the above embodiments. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0088] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0089] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0090] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0092] In the corresponding figures of the above embodiments, connecting lines can represent the connection relationship between various components, indicating more constitutive signal paths and / or one or more ends of some lines having arrows to indicate the main information flow direction. Connecting lines serve as an identifier and are not a limitation on the scheme itself, but rather, using these lines in conjunction with one or more exemplary embodiments helps to more easily connect circuits or logic units. Any signal represented (determined by design requirements or preferences) can actually include one or more signals that can be transmitted in any direction and can be implemented in any suitable type of signal scheme.

[0093] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0094] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0095] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0096] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0097] It should be noted that this application can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0098] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0099] It should be understood that the above content of this application is only a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be the scope of protection claimed in the claims.

Claims

1. A vehicle anti-skid control method, characterized in that, The vehicle anti-skid control method includes: Acquire current road surface image and ambient temperature parameters, as well as the vehicle's rated driving torque, theoretical maximum torque, and current throttle opening; The ambient temperature parameter is compared with the preset ambient threshold temperature, and the target image processing strategy is matched according to the relationship between the ambient temperature parameter and the preset ambient threshold temperature. The current road surface image is identified according to the target image processing strategy to obtain road surface type information and initial road surface information; If the initial road information exists in the preset road control scenario list, the target sensing device is triggered to acquire the current road condition sensing data, and the current road information is determined based on the road condition sensing data and the initial road information. A first road condition coefficient is matched and associated based on the current road surface information and the road surface type information, and a second road condition coefficient is matched and associated based on the current road surface information and the current throttle opening. A first torque limit is determined based on a first road condition coefficient and a rated driving torque, a second torque limit is determined based on a second road condition coefficient and a theoretical maximum torque, and an actual torque is determined based on the first torque limit and the second torque limit, so as to perform vehicle control according to the actual torque.

2. The vehicle anti-skid control method according to claim 1, characterized in that, After determining the current road surface information based on the road condition sensor data and initial road surface information, the vehicle anti-skid control method further includes: If the current road surface information is included in the preset list of slippery road surfaces, an anti-slip warning will be issued on the control terminal, and anti-slip feedback information will be received from the control terminal. The anti-slip feedback information includes activating the anti-slip mode, deactivating the anti-slip mode, and no response. If the anti-skid feedback information is to activate the anti-skid mode or there is no response, then the anti-skid mode is activated, and a first road condition coefficient is matched and associated based on the current road surface information and the road surface type information, and a second road condition coefficient is matched and associated based on the current road surface information. If the anti-skid feedback information indicates that the anti-skid mode is off, then the current vehicle anti-skid control will end.

3. The vehicle anti-skid control method according to claim 1, characterized in that, The target image processing strategy includes a first processing strategy and a second processing strategy. Matching the target image processing strategy according to the relationship between the ambient temperature parameter and the preset ambient threshold temperature includes: If the ambient temperature parameter is greater than the preset ambient threshold temperature, the current road surface image is identified based on the first processing strategy; If the ambient temperature parameter is less than or equal to a preset ambient threshold temperature, the current road surface image is identified based on the second processing strategy.

4. The vehicle anti-skid control method according to claim 3, characterized in that, Identifying the current road surface image based on the first processing strategy includes: Perform feature road surface recognition on the current road surface image to determine the road surface image to be processed; The road surface image to be processed is input into a preset road surface type recognition model to obtain road surface type information; The road surface image to be processed is subjected to grayscale processing and grayscale values ​​are calculated. The image to be processed after grayscale value calculation is segmented based on the grayscale value to obtain multiple image regions to be processed with different grayscale values. The initial road surface information is determined based on the proportion of the area of ​​the image to be processed with a gray value greater than a preset water accumulation gray value threshold to the area of ​​the road surface to be processed. The initial road surface information includes roads with a large amount of water accumulation, roads with a small amount of water accumulation, and roads without water accumulation.

5. The vehicle anti-skid control method according to claim 3, characterized in that, Based on the second processing strategy, the current road surface image is identified as follows: Perform feature road surface recognition on the current road surface image to determine the road surface image to be processed; The road surface image to be processed is input into a preset road surface type recognition model to obtain road surface type information; The road surface image to be processed is sent to the cloud. The cloud inputs the road surface image to be processed into a preset snow accumulation recognition model to obtain initial road surface information and sends the initial road surface information back to the vehicle. The initial road surface information includes snow-covered roads and snow-free roads. The preset snow accumulation recognition model is set in the cloud.

6. The vehicle anti-skid control method according to any one of claims 1-5, characterized in that, The preset road control scenario list includes roads with a large amount of water accumulation, roads with a small amount of water accumulation, roads without water accumulation, and roads without snow accumulation. The determination of the current road information based on the road condition sensor data and the initial road information includes at least one of the following: If the initial road surface information is a road surface with a large amount of water accumulation, then the water depth is determined based on the road condition sensor data, and the current road surface information is determined to be a road surface with deep water accumulation or a road surface with shallow water accumulation based on the water depth. If the initial road surface information is a road surface with a small amount of water accumulation, then the road surface wetness is determined based on the road condition sensor data, and the current road surface information is determined to be a wet road surface or a dry road surface based on the road surface wetness. If the initial road surface information is a road surface without water accumulation, then the road surface wetness status is determined based on the road condition sensor data, and the current road surface information is determined to be a wet road surface or a dry road surface based on the road surface wetness status. If the initial road surface information is a snow-free road surface, then the road surface icing status is determined based on the road condition sensor data, and the current road surface information is determined to be an icy road surface or a dry road surface based on the road surface icing status.

7. The vehicle anti-skid control method according to any one of claims 1-5, characterized in that, Determining the actual torque based on the first torque limit and the second torque limit includes: The minimum value between the first torque limit and the second torque limit is determined as the actual torque; or, The weighted average of the first torque limit and the second torque limit is determined as the actual torque.

8. A vehicle anti-skid control device, characterized in that, The vehicle anti-skid control device includes: The anti-skid control parameter acquisition module is used to acquire the current road surface image and ambient temperature parameters, as well as the vehicle's rated driving torque, theoretical maximum torque, and current throttle opening. The initial road surface information determination module compares the ambient temperature parameter with a preset ambient temperature threshold, and matches a target image processing strategy based on the relationship between the ambient temperature parameter and the preset ambient temperature threshold; it then identifies the current road surface image according to the target image processing strategy to obtain road surface type information and initial road surface information. The current road surface information determination module is used to trigger the target sensing device to acquire the current road condition sensing data if the initial road surface information exists in the preset road surface control scenario list, and to determine the current road surface information based on the road condition sensing data and the initial road surface information. The anti-skid control torque determination module is used to match a first road condition coefficient based on the current road surface information and the road surface type information, and to match a second road condition coefficient based on the current road surface information and the current throttle opening; to determine a first torque limit based on the first road condition coefficient and the rated driving torque, to determine a second torque limit based on the second road condition coefficient and the theoretical maximum torque, and to determine the actual torque based on the first torque limit and the second torque limit, so as to control the vehicle according to the actual torque.

9. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the vehicle anti-skid control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that, when executed by the processor of a computer, cause the computer to perform the vehicle anti-skid control method as described in any one of claims 1 to 7.

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