A vehicle tire burst detection method, electronic device and vehicle

Abstract

The application provides a vehicle tire burst detection method, an electronic device and a vehicle. The method comprises the following steps: acquiring tire information of a target vehicle; and determining whether a front vehicle has a tire burst according to the tire information of the target vehicle. The method can solve the problem that the prior art cannot detect whether a front vehicle has a tire burst.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle technology, and in particular to a method for detecting tire blowouts, electronic equipment, and a vehicle. Background Technology

[0002] Tires are crucial components for power transmission in vehicles and directly impact driving safety. A tire blowout during driving significantly reduces vehicle handling stability and may endanger the safety of passengers. Therefore, it is essential to assess tire condition while driving and promptly alert passengers to any potential blowout risk to improve driving safety.

[0003] In related technologies, tire blowout detection only applies to the vehicle itself and cannot detect whether a vehicle in front of it has experienced a tire blowout. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a vehicle tire blowout detection method, electronic equipment, and vehicle. This solution acquires the tire information of a target vehicle and determines whether the target vehicle has experienced a tire blowout based on that information. This solution can detect whether a vehicle ahead has experienced a tire blowout based on the tire information of the target vehicle, allowing the vehicle to respond promptly and take safety control measures, thereby reducing the occurrence of traffic accidents.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a method for detecting tire blowouts in vehicles, comprising: acquiring tire information of a target vehicle; and determining whether a tire blowout has occurred in the vehicle ahead based on the tire information of the target vehicle.

[0007] In some embodiments of this application, the tire information includes the first tire area and the second tire area of ​​the target vehicle.

[0008] In some embodiments of this application, obtaining the tire information of the target vehicle includes: extracting a first target region and a second target region from the bottom of the target vehicle in the acquired video image, wherein the brightness of the first target region and the second target region is greater than a set value; and calculating the first tire area enclosed by the first target region and the second tire area enclosed by the second target region.

[0009] In some embodiments of this application, determining whether the target vehicle has experienced a tire blowout based on the tire information includes: determining whether the target vehicle has experienced a tire blowout based on the first tire area and the second tire area.

[0010] In some embodiments of this application, determining whether the target vehicle has experienced a tire blowout based on the first tire area and the second tire area includes: if the difference between the first tire area and the second tire area is greater than a preset threshold, then outputting a tire blowout anomaly alert for the target vehicle in a preset manner; the preset manner includes at least one of the following: voice alert, video text alert, traffic light alert, and instrument display alert.

[0011] In some embodiments of this application, the step of extracting the first target region and the second target region of the bottom of the target vehicle in the acquired video image includes: inputting the acquired video image into a pre-trained deep learning semantic segmentation model, outputting image information containing the target vehicle; and extracting the first target region and the second target region of the bottom of the target vehicle from the image information of the target vehicle.

[0012] In some embodiments of this application, the method further includes: constructing and training a deep learning semantic segmentation model, and generating the trained deep learning semantic segmentation model.

[0013] Secondly, this application provides a vehicle tire blowout detection system, including: a processor configured to acquire tire information of a target vehicle and determine whether the target vehicle has experienced a tire blowout based on the tire information.

[0014] In some embodiments of this application, the system further includes a sensing sensor configured to acquire video images of the target vehicle.

[0015] In some embodiments of this application, the sensing sensor is located at the front of the vehicle body.

[0016] In some embodiments of this application, the sensing sensor includes any one of the following: a far-infrared camera and a visible light camera.

[0017] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the computer to perform the method described in the first aspect.

[0018] Fourthly, this application provides a computer program product that stores instructions which, when executed by a computer, cause the computer to perform the method described in the first aspect.

[0019] Fifthly, this application provides an electronic device, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the method as described in the first aspect.

[0020] In a sixth aspect, this application provides a vehicle comprising: a washing system as described in the second aspect; or an electronic device as described in the fifth aspect; or a processor configured to perform the method as described in the first aspect.

[0021] The advantages and control methods of the vehicle and electronic equipment compared to the prior art are the same, and will not be elaborated here.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0025] Figure 1 This is a schematic flowchart of a vehicle tire blowout detection method provided by an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the process for generating a semantic segmentation model based on a far-infrared camera according to an embodiment of the present invention;

[0027] Figure 3 This is a flowchart illustrating a method for detecting tire blowouts of vehicles traveling ahead based on a far-infrared camera, according to an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0030] This invention proposes a method, electronic device, and vehicle for detecting tire blowouts. The solution acquires tire information of a target vehicle and determines whether a tire blowout has occurred based on this information. This solution can detect whether a vehicle ahead has experienced a tire blowout based on the tire information of the target vehicle, allowing the vehicle to respond promptly and take safety control measures, thereby reducing the occurrence of traffic accidents.

[0031] The present invention will now be described in further detail with reference to the embodiments.

[0032] like Figure 1 The diagram shown is a flowchart illustrating a vehicle tire blowout detection method provided in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0033] 101. Obtain the tire information of the target vehicle.

[0034] 102. Determine whether the target vehicle has experienced a tire blowout based on the tire information of the target vehicle.

[0035] In the above embodiments, the tire information of the target vehicle is used to determine whether a tire blowout has occurred on the vehicle ahead, enabling the vehicle to respond promptly and take safety control measures, thereby reducing the occurrence of traffic accidents. This solution directly captures changes in tire physical parameters caused by a tire blowout in the tire-ground contact area, eliminating the need for in-vehicle modifications such as tire pressure sensors, thus achieving non-contact, all-scenario tire blowout detection.

[0036] Furthermore, compared to related technologies that use tire pressure sensors to detect tire blowouts and then send the blowout signal to following vehicles, which then control their own vehicles based on the blowout signal from the preceding vehicle, this solution directly captures tire information caused by a blowout at the tire-to-ground contact area. This eliminates the need to receive a separate blowout signal, allowing for direct detection of a blowout from the preceding vehicle, thus improving detection accuracy and reducing traffic accidents.

[0037] For example, the tire information mentioned above includes the first tire area and the second tire area of ​​the target vehicle.

[0038] Further optionally, step 101 above includes the following: 101a, extracting a first target region and a second target region from the bottom of the target vehicle in the acquired video image, wherein the brightness of both the first target region and the second target region is greater than a set value; 101b, calculating the first tire area enclosed by the first target region and the second tire area enclosed by the second target region.

[0039] For example, if the brightness of the aforementioned region is greater than a set value, the region brightness can be measured by pixel values ​​during the specific data processing. Specifically, the target region is first converted from the RGB color space to the grayscale space, and the regions with pixel values ​​greater than the threshold are extracted, which are the first target region and the second target region mentioned above.

[0040] In the above embodiment, the tire temperature will rise due to friction heat generated during vehicle operation, resulting in higher brightness in the tire area of ​​the video image. This solution obtains the above-mentioned tire information by extracting a first target area and a second target area whose brightness is greater than a set value, and then calculating the area of ​​these two target areas.

[0041] As an optional implementation, the video image in step 101a above can be acquired by a sensing sensor on the vehicle. For example, the sensing device can be either a far-infrared camera or a visible light camera; preferably, a far-infrared camera can be used to acquire the video image.

[0042] In the above embodiments, by using a far-infrared camera as a sensing sensor, the thermal radiation image generated by the friction between the tires of the vehicle in front and the ground is obtained, which breaks through the detection failure bottleneck of traditional visible light systems in low visibility environments such as fog, rain, snow, and night. It realizes all-weather, non-contact, and low-cost perception of the tire blowout status of the vehicle in front, and significantly improves the availability and safety of the system in complex road scenarios.

[0043] As an optional implementation, step 102 above specifically includes the following: 102a. Determining whether the target vehicle has experienced a tire blowout based on the first tire area and the second tire area.

[0044] For example, step 102a above includes the following: 102a1. If the difference between the area of ​​the first tire and the area of ​​the second tire is greater than a preset threshold, then output a tire blowout abnormality alert for the target vehicle in a preset manner.

[0045] The aforementioned preset methods include at least one of the following: voice reminders, video and text reminders, traffic light reminders, and instrument display reminders.

[0046] In the above embodiments, the abnormally high-temperature area formed by the loss of tire pressure and intense friction between the rubber and the ground in a blown-out tire appears as a significantly brighter patch than that of a normal tire in far-infrared video images. A normal tire, due to sufficient air pressure, has a small contact area and uniform friction, resulting in symmetrical, stable thermal radiation. However, after a blowout, the tire flattens and lies close to the ground, causing a sudden increase in contact area, expanding the thermal radiation area, and creating an irregular outline. Compared to the size of the bright area in the infrared camera image of a normal tire, the bright area on the blown-out side is reduced. The system quantifies the change in the outline area of ​​this region to establish an "area difference criterion," avoiding false alarms caused by solely relying on temperature thresholds (such as residual heat from brake discs or hot spots on the road surface), thus improving the robustness and accuracy of detection.

[0047] As an optional implementation, the above method further includes: 102a1, inputting the acquired video image into a pre-trained deep learning semantic segmentation model and outputting image information containing the target vehicle; 102a2, extracting the first target region and the second target region at the bottom of the target vehicle from the image information of the target vehicle.

[0048] In some embodiments, step 102a2 specifically includes the following: performing threshold segmentation on the image information containing the target vehicle, and extracting the region of the bottom image of the target vehicle that meets the threshold condition as the first target region and the second target region.

[0049] For example, the regions in the vehicle bottom image that satisfy the threshold condition include a first target region and a second target region where the pixel value of the vehicle bottom image is greater than the threshold.

[0050] Specifically, this embodiment of the invention acquires real-time infrared video images using a far-infrared camera, and uses a pre-trained semantic segmentation model to identify vehicle targets in the video images. Then, the segmented vehicle targets are subjected to image thresholding to extract the brighter areas on both sides of the vehicle's bottom. These brighter areas refer to regions with pixel values ​​greater than a set value. Specifically, this region is first converted from the RGB color space to grayscale space, and areas with pixel values ​​≥ a set value (e.g., a set value of 200) on the bottom of the vehicle are extracted; these areas are considered the brighter areas on the bottom of the vehicle.

[0051] Further optionally, the above method also includes: 100. Constructing and training a deep learning semantic segmentation model, and generating a trained deep learning semantic segmentation model.

[0052] In the above embodiments, this invention uses a pre-trained semantic segmentation model to identify vehicle targets in video images. Semantic segmentation is a deep learning algorithm that associates labels or categories with each pixel of an image. It is used to identify sets of pixels that constitute distinguishable categories. Semantic segmentation is a classification at the pixel level; pixels belonging to the same category are grouped together. Therefore, semantic segmentation understands images at the pixel level. In this scheme, the image can be divided into two categories of image pixels: vehicles and background.

[0053] The following is a detailed description of the specific steps involved in generating the semantic segmentation model, such as... Figure 2 The diagram illustrates the process of generating a semantic segmentation model based on a far-infrared camera according to an embodiment of the present invention. As shown, the generation method includes the following:

[0054] S201. Offline acquisition of far-infrared image samples in various scenarios to construct a dataset. Examples include far-infrared images of different vehicles at different distances under different temperatures.

[0055] S202. Label the collected dataset. The image content can be divided into two categories: vehicles and background pixels. Select the pixels of different categories for each image in the dataset, assign corresponding labels, and generate a label file for each image. Finally, randomly sort the samples. This randomization helps avoid overfitting.

[0056] S203. Construct and train a deep learning semantic segmentation model. Available semantic segmentation networks include: SegNet and FCN networks (symmetric semantic segmentation models based on fully convolution); DeepLab series and RefineNet networks (semantic segmentation models based on dilated convolution); and PSPNet networks (semantic segmentation models based on residual networks). Here, we use the SegNet network (symmetric semantic segmentation model based on fully convolution). It mainly consists of an encoder network, a corresponding decoder network, and a pixel-level classification layer. SegNet is characterized by its decoder upsampling its low-resolution input feature maps. Specifically, the decoder uses the pooling index computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. Primarily designed for scene understanding applications, SegNet has significantly fewer trainable parameters than other network architectures, and it can be trained end-to-end using stochastic gradient descent. Evaluations show that SegNet exhibits superior time and memory performance during inference compared to other architectures.

[0057] The SegNet network consists of an encoder network, a corresponding decoder network, and a pixel-level classification layer. The encoder network uses a VGG network for feature extraction; the decoder network primarily performs three-fold linear upsampling; and the pixel-level classification layer uses a convolutional layer to adjust the network output to the desired value. Specifically, the encoder network uses the first 13 convolutional layers of a VGG16 network to extract features, and the feature matrix output from the fourth convolutional block of the VGG16 network is fed into the decoder. Decoding is essentially an upsampling process. The decoder uses the pooling index calculated in the max-pooling step of the corresponding encoder to perform non-linear upsampling. The pixel-level classification layer is designed for pixel-level classification and uses convolutional layers to change the number of channels in the decoder network's output tensor. The model can be trained using machine learning libraries such as TensorFlow and PyTorch; PyTorch is used here. The dataset is divided into training, validation, and test sets, and the model's loss function is the cross-entropy loss function. The training, validation, and test sets, along with the model, are loaded into PyTorch. The training set is a dataset used for model fitting and for tuning network parameters. The validation set is used to check if the model training is deteriorating; if so, training is stopped immediately, and the model structure and hyperparameters are adjusted accordingly. The test set is used to evaluate the generalization ability of the final model. Finally, the suitability of the model and its parameters is comprehensively analyzed based on the percentage of correctly classified pixels and the average prediction accuracy across all classes in the test set dataset.

[0058] S204: Generate the final model as a deep learning semantic segmentation model.

[0059] It should be noted that the above model is only for illustrative purposes and the present invention is not limited thereto. Of course, the present invention can also be implemented using other existing segmentation models, which will not be elaborated here.

[0060] The following uses an infrared camera as an example to illustrate the specific implementation process of this invention. Figure 3 The diagram shown is a flowchart illustrating a method for detecting tire blowouts of vehicles traveling ahead based on a far-infrared camera, as provided in an embodiment of the present invention. It mainly includes the following:

[0061] S101: Acquire real-time video images from the far-infrared camera.

[0062] S102: Use a pre-trained semantic segmentation model to identify vehicle targets in video images.

[0063] S103: Perform image thresholding on the segmented vehicle target to extract the brighter areas on both sides of the bottom of the vehicle.

[0064] S104: Compare the contour areas enclosed by the brighter regions extracted on both sides, and determine whether the two contour areas differ significantly. If so, proceed to S105.

[0065] S105: Remind the driver through voice and video text that the tire of the vehicle ahead has a flat tire abnormality.

[0066] In the above embodiments, due to the friction between the tires of the vehicle ahead and the ground generating heat, the temperature of the tires is relatively high. As a result, the brightness of the tires of the vehicle ahead in the video image captured by the far-infrared camera of the vehicle itself is relatively high. Perform image threshold segmentation on the segmented vehicle target, and extract two brighter regions at the bottom of the vehicle. Then, compare the contour areas enclosed by the two extracted brighter regions, and determine whether the two contour areas differ significantly. If so, remind the driver through voice, video text, etc. that the tire of the vehicle ahead has a flat tire abnormality.

[0067] The embodiment of the present application also provides a vehicle flat tire detection system, including: a processor, configured to obtain the tire information of the target vehicle and determine whether the target vehicle has a flat tire according to the tire information of the target vehicle.

[0068] In the above embodiments, the system hardware can be deployed on the domain controller, and the processor can be the Horizon J5 chip (with a computing power of 128 TOPS), which is responsible for multi-source data synchronization, feature extraction, model inference, and decision output. The detection result is sent to the instrument panel, central control screen, and voice module through the CAN bus to achieve full-vehicle-level linkage reminder.

[0069] In some embodiments, the above-mentioned perception sensors include any one of the following: far-infrared camera, visible light camera array.

[0070] In some embodiments, the above-mentioned perception sensors are located at the front of the vehicle body.

[0071] Specifically, the above-mentioned far-infrared camera takes pictures in the direction of the vehicle's forward movement, and the position of the camera is fixed through a bracket structure for collecting the original video information in front of the vehicle.

[0072] Exemplarily, the above-mentioned far-infrared camera can also be fixed in the upper middle part inside the front windshield, with a depression angle of 15°, a horizontal field of view angle of ±45°, covering 30 - 120m in front, ensuring that the tire area of the vehicle ahead can be stably captured in both high-speed cruising and urban vehicle-following scenarios. The outer shell is made of aviation aluminum alloy, with an IP67 protection level, and a PTC heating element is built-in, which can automatically start defrosting at a low temperature of -40°C, ensuring normal operation even under winter icing conditions.

[0073] Such as Figure 4The above is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The electronic device 400 includes a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, and a computer program stored in the memory 402 and executable on the processor. The processor 401 is electrically connected to the memory 402.

[0074] The processor 401 is the control center of the electronic device 400. It connects various parts of the electronic device 400 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 402, and by calling data stored in the memory 402, it executes various functions and processes data of the electronic device 400, thereby providing overall monitoring of the electronic device 400. The processor 401 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a Network Processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0075] In this embodiment of the application, the processor 401 in the electronic device 400 loads the computer program corresponding to the process of one or more applications into the memory 402 according to the method or steps of the above embodiment, and the processor 401 runs the applications stored in the memory 402 to execute the above method.

[0076] According to an embodiment of the present invention, an electronic device acquires tire information of a target vehicle by performing the above-described method; and determines whether the target vehicle has experienced a tire blowout based on the tire information. This solution can detect whether a vehicle ahead has experienced a tire blowout based on the tire information of the target vehicle, thereby enabling the vehicle to respond promptly and take safety control measures, reducing the occurrence of traffic accidents.

[0077] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, enables the computer to implement the vehicle control method described above. For example, the computer-readable storage medium may be the aforementioned memory including program instructions, which may be executed by a processor of an electronic device to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0078] This invention also provides a computer program product storing instructions that, when executed by a computer, cause the computer to implement the vehicle control method described above. For example, when executed by a computer, the instructions implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0079] Embodiments of the present invention also provide a vehicle comprising the electronic equipment described above, or a processor, the processor being used to execute the methods described above. The vehicle may be a gasoline-powered vehicle, a plug-in hybrid electric vehicle, or a new energy vehicle, etc., and this specification does not specifically limit it.

[0080] According to embodiments of the present invention, a vehicle executes the above-described method via electronic equipment, a control system, or a controller to acquire tire information of a target vehicle; and determines whether a tire blowout has occurred on the target vehicle based on the tire information. This solution can detect whether a tire blowout has occurred on the vehicle ahead based on the tire information of the target vehicle, thereby enabling the vehicle to respond promptly and take safety control measures, reducing the occurrence of traffic accidents.

[0081] The above-described embodiments are only used to illustrate the technical solutions of applying the above methods to vehicles, 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 the method can also be used in motor vehicles, trains, and ships, etc., without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0082] In one embodiment, the vehicle can be configured for fully or partially autonomous driving. For example, the vehicle can control itself while in autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human intervention, determine the possible behaviors of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of that other vehicle performing a possible behavior, and control the vehicle based on the determined information. When the vehicle is in autonomous driving mode, it can be configured to operate without human interaction.

[0083] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0084] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," "optional example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0085] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0086] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although the descriptions of each embodiment in this application have different focuses, and the parts not described in detail in a certain embodiment can be referred to the relevant embodiments of other embodiments, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A method for detecting tire blowouts in vehicles, characterized in that, include: Obtain tire information for the target vehicle; Based on the tire information, determine whether the target vehicle has experienced a tire blowout.

2. The method according to claim 1, characterized in that, The tire information includes the first tire area and the second tire area of ​​the target vehicle.

3. The method according to claim 2, characterized in that, The acquisition of tire information of the target vehicle includes: Extract a first target region and a second target region from the bottom of the target vehicle in the acquired video image, where the brightness of both the first target region and the second target region is greater than a set value; Calculate the area of ​​the first tire enclosed by the first target area and the area of ​​the second tire enclosed by the second target area.

4. The method according to claim 2, characterized in that, The step of determining whether the target vehicle has experienced a tire blowout based on the tire information includes: Whether the target vehicle has experienced a tire blowout is determined based on the first tire area and the second tire area.

5. The method according to claim 4, characterized in that, The step of determining whether the target vehicle has experienced a tire blowout based on the first tire area and the second tire area includes: If the difference between the area of ​​the first tire and the area of ​​the second tire is greater than a preset threshold, a tire blowout warning for the target vehicle is output in a preset manner; the preset manner includes at least one of the following: voice warning, video text warning, traffic light warning, and instrument display warning.

6. The method according to claim 3, characterized in that, The extraction of the first target region and the second target region from the bottom of the target vehicle in the acquired video image includes: The acquired video images are input into a pre-trained deep learning semantic segmentation model, which outputs image information containing the target vehicle. Extract the first target region and the second target region at the bottom of the target vehicle from the image information of the target vehicle.

7. The method according to claim 6, characterized in that, The method further includes: Construct and train a deep learning semantic segmentation model, and generate the trained deep learning semantic segmentation model.

8. A vehicle tire blowout detection system, characterized in that, include: The processor is configured to acquire tire information of a target vehicle and determine, based on the tire information, whether the target vehicle has experienced a tire blowout.

9. The system according to claim 8, characterized in that, The system also includes a perception sensor configured to acquire video images of the target vehicle.

10. The system according to claim 9, characterized in that, The sensing sensor is located at the front of the vehicle body.

11. The system according to claim 9 or 10, characterized in that, The sensing sensor includes any one of the following: an infrared camera and a visible light camera.

12. A computer program product, characterized in that, The computer program product stores instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7.

13. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the method of any one of claims 1 to 7.

14. A vehicle, characterized in that, include: The system according to any one of claims 8-11; Or, the electronic device according to claim 13; Alternatively, a processor, said processor being configured to perform the method of any one of claims 1-7.

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