Vehicle tire burst control method, system, equipment and medium

Abstract

The invention provides a vehicle tire burst control method, system and device and a medium. The method comprises the steps that vehicle driving environment information, tire state information and vehicle driving state information are monitored; when the tire state information shows tire burst, inputting the vehicle driving environment information and the vehicle driving state information into a pre-trained decision model to obtain a current control strategy, the decision model is used for representing a mapping relationship between different vehicle driving environment information and vehicle driving state information and corresponding control strategies; and vehicle control is carried out according to a control instruction contained in the current control strategy, so that the vehicle is in a safe state. Tire burst control is automatically carried out during tire burst, the personal safety of a driver and passengers is guaranteed, and the use experience of a user is enhanced.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle control, and more particularly to a method, system, device, and medium for controlling tire blowout in vehicles. Background Technology

[0002] In the current automotive industry and traffic safety field, tire safety performance has always been a crucial issue. As the component that directly contacts the road surface, the integrity of the tire is of paramount importance in ensuring the safety of passengers and drivers. However, tire blowouts, especially those occurring at high speeds, have become a serious hidden danger threatening road safety.

[0003] According to authoritative statistics, tire blowouts account for nearly half of all traffic fatalities, over 60% of injuries, and more than 40% of direct property damage in traffic accidents. These alarming figures highlight the urgency of solving the tire blowout problem and improving driving safety.

[0004] Tires not only bear the weight of a car body during driving, but also play a vital role in cushioning road impacts, absorbing vibrations, and providing the longitudinal and tangential forces required for driving, ensuring the normal operation of functions such as acceleration, steering, and braking. Therefore, once the tire structure is damaged, the consequences are unimaginable, directly affecting the driving safety of the vehicle, and may even lead to serious consequences such as vehicle crashes and fatalities.

[0005] Under the current technological conditions, the handling of car tire blowout incidents mainly relies on the driver's personal judgment and operational ability. This approach has shortcomings such as insufficient reaction speed, low accuracy, susceptibility to psychological panic, and lack of intelligent early warning and intervention systems, which cannot effectively protect the lives of passengers. Summary of the Invention

[0006] In view of the problems existing in the prior art, this invention proposes a method, system, equipment, and medium for controlling vehicle tire blowouts. The main solution addresses the issue that handling a tire blowout relies on the driver's personal experience and reaction time, making it difficult to guarantee driving safety.

[0007] To achieve the above and other objectives, the technical solution adopted by the present invention is as follows.

[0008] This application provides a method for controlling tire blowout in vehicles, comprising: monitoring vehicle driving environment information, tire status information, and vehicle driving state information; when the tire status information indicates a tire blowout, inputting the vehicle driving environment information and the vehicle driving state information into a pre-trained decision model to obtain a current control strategy, wherein the decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving state information and the corresponding control strategy; and controlling the vehicle according to the control instructions contained in the current control strategy to ensure that the vehicle is in a safe state.

[0009] In one embodiment of this application, before monitoring vehicle driving environment information, tire status information, and vehicle driving status information, the method further includes: setting tire blowout control parameters, such that before controlling the vehicle according to the current control strategy, the tire blowout control parameters are invoked to correct the corresponding control command.

[0010] In one embodiment of this application, the method further includes: acquiring historical driving data; identifying user driving habits based on the historical driving data; generating tire blowout recommended setting information based on the user driving habits, and displaying it on the vehicle, so that the user can set tire blowout control parameters based on the recommended setting information.

[0011] In one embodiment of this application, the pre-training process of the decision model includes: constructing a training dataset, wherein the training dataset includes different tire blowout scenarios and different vehicle driving states; inputting the training dataset into a deep learning neural network, and adjusting the loss value of the loss function through network iteration to minimize the loss value, thereby obtaining the decision model, wherein the loss function is constructed based on the deviation between the output control command and the target control command of the deep learning neural network.

[0012] In one embodiment of this application, after controlling the vehicle according to the control instructions contained in the current control strategy, the method further includes: determining that the vehicle is currently in a safe driving state based on the vehicle driving environment information and the vehicle driving status information, then exiting the automatic tire blowout control, and displaying the control strategy through the vehicle-mounted display device so that the driver can control the vehicle based on the displayed information.

[0013] In one embodiment of this application, the step of determining that the current vehicle is in a safe driving state based on vehicle driving environment information and vehicle driving status information includes: if the deviation angle between the vehicle's driving direction and the current driving lane is less than a preset angle threshold, and there are no vehicles in the current driving lane and adjacent lanes, or the distance between the current vehicle and adjacent vehicles is greater than a set distance threshold, then the current vehicle is determined to be in a safe driving state.

[0014] In one embodiment of this application, the steps of controlling the vehicle according to the control instructions included in the current control strategy include: controlling the vehicle steering angle and driving direction through steering control instructions; controlling the vehicle deceleration magnitude through braking control instructions; adjusting the suspension stiffness through suspension control instructions; and adjusting the power output through power adjustment instructions.

[0015] This application also provides a vehicle tire blowout control system, comprising: a monitoring module for monitoring vehicle driving environment information, tire status information, and vehicle driving status information; a decision module for inputting the vehicle driving environment information and the vehicle driving status information into a pre-trained decision model to obtain a current control strategy when the tire status information indicates a tire blowout, wherein the decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving status information and the corresponding control strategy; and a control module for controlling the vehicle according to the control instructions contained in the current control strategy to ensure that the vehicle is in a safe state.

[0016] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the vehicle tire blowout control method.

[0017] This application also provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the vehicle tire blowout control method.

[0018] As described above, the vehicle tire blowout control method, system, device, and medium proposed in this application have the following beneficial effects.

[0019] This application automatically monitors vehicle driving environment information, tire status information, and vehicle driving status information during driving, which can promptly detect tire blowouts. Based on different driving environments and vehicle driving statuses, it makes decisions to meet the control needs of various tire blowout scenarios, obtains accurate and effective control strategies, enables the vehicle to drive safely and smoothly, protects the personal safety of the occupants, enhances the intelligent handling capabilities of tire blowout scenarios, reduces reliance on manual labor, improves the vehicle's intelligence level, and enhances the user experience. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a vehicle tire blowout control method in one embodiment of this application.

[0021] Figure 2 This is a block diagram of a vehicle tire blowout control system according to one embodiment of this application.

[0022] Figure 3This is a schematic diagram of the overall architecture of vehicle tire blowout control in one embodiment of this application.

[0023] Figure 4 This is a schematic diagram of the device in one embodiment of this application. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. 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 also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[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 drawings 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] Please see Figure 1 , Figure 1 This is a flowchart illustrating a vehicle tire blowout control method according to an embodiment of this application. The vehicle tire blowout control method provided in this embodiment includes the following steps:

[0027] Step S100: Monitor vehicle driving environment information, tire status information, and vehicle driving status information.

[0028] In one embodiment, different types of sensors installed at different locations on the vehicle body can be used to collect data to monitor various vehicle states during driving. Specifically, pressure and temperature sensors can be installed in each tire to monitor the internal state of the tire in real time. When an abnormal rate of tire pressure drop or an abnormal rate of tire temperature rise is detected, a tire blowout signal can be sent to the corresponding controller. When monitoring the vehicle's driving environment, sensors such as cameras and radar can be used to perceive traffic, road, and environmental conditions, and the perceived information can be sent to the corresponding controller. Cameras can include monocular cameras, binocular cameras, tri-lens cameras, surround-view cameras, and night vision devices, while radar sensors can include lidar, millimeter-wave radar, and ultrasonic radar. Cameras are typically installed at the front, rear, sides, and roof of the vehicle to achieve panoramic environmental perception. Front-facing cameras capture road signs, traffic signals, pedestrians, and other vehicles ahead, while rear-view or side-view cameras provide rear and side views to assist with lane changes or reversing. Surround-view cameras can be mounted around the vehicle to provide a 360-degree view and capture images of the surroundings. Night vision cameras, using infrared sensing technology, can capture clear images even at night or in low-light conditions. Millimeter-wave radar can be installed at the front, rear, or sides of the vehicle to cover ranging needs in different directions. LiDAR can be mounted on the top of the vehicle for better scanning field of view and accuracy, and can be used to build high-precision 3D environmental models based on LiDAR scanning data to collect detailed information about the vehicle's driving environment. Ultrasonic radar can be installed on the front and rear bumpers, sides, and bottom of the vehicle for close-range target detection. During vehicle driving status monitoring, onboard sensors detect the status of various vehicle subsystems, such as speed, acceleration, and steering angle. Real-time data on vehicle speed, acceleration, steering angle, and braking status are collected via the onboard CAN bus and transmitted to the corresponding controllers. Based on various sensors installed on the vehicle, information from multiple dimensions is collected during the vehicle's operation, resulting in more accurate information on the vehicle's driving environment, tire status, and overall driving status.

[0029] In one embodiment, a tire blowout control activation switch can be installed on the vehicle. This activation switch can be a physical switch or a soft switch installed on the vehicle's infotainment system. The on / off state of the activation switch can be set on the vehicle's infotainment screen. Users can choose whether to enable the tire blowout control function as needed.

[0030] In one embodiment, users can also set tire blowout control parameters on the vehicle side according to their personalized needs. Specifically, tire blowout control menu options can be set on the vehicle's interactive interface, clearly displaying whether the tire blowout control function is enabled or disabled, and the state can be switched as needed. Drivers or designated personnel can set the tire blowout warning sensitivity (such as pressure drop rate threshold, temperature rise rate threshold, etc.), the timing of tire blowout control intervention (such as taking over vehicle control 0.5 seconds after a tire blowout is detected), and the intensity level of vehicle stability control via the vehicle's touchscreen. Users can also select the type of tire blowout control strategy according to their needs. For example, control strategies may include: conservative, balanced, aggressive, etc. Users can also customize control strategies, such as setting operations to be performed at specific speeds. Specific parameter types and levels can be set and adjusted according to actual application needs, and are not limited here. The vehicle-side system stores user-defined tire blowout control parameters. When determining a tire blowout control strategy based on detected vehicle driving environment and status information, these parameters are integrated with the strategy. The corresponding parameters in the control strategy are adjusted based on the user-defined parameters to correct the errors. Vehicle control is then performed according to the corrected parameters, meeting personalized control needs. Alternatively, users can set tire blowout control parameters on external devices (such as mobile phones, tablets, or computers). These parameters are uploaded to the cloud and incrementally updated to the corresponding controller on the vehicle each time the vehicle starts. This allows users to set tire blowout control parameters from any location, meeting diverse user needs, enhancing the user experience, and reducing vehicle-side resource consumption. In another embodiment, the vehicle-side system can also provide tire blowout risk warnings based on collected vehicle driving environment, tire status, and vehicle driving status information. When a blowout risk is detected, the system accesses the controller or terminal storing configuration information to obtain the corresponding tire blowout control parameters. This reduces data transmission between the vehicle and the cloud after power-on, preventing delays caused by excessive short-term data transmission and ensuring vehicle-side functionality remains functional. Tire blowout risk warnings can also be identified based on a pre-trained risk assessment neural network model. This model collects environmental and driving data related to various vehicles before a blowout, classifies the data using a neural network, establishes a mapping relationship between the data and different levels of blowout risk, and thus obtains the risk assessment neural network model. The specific neural network can be selected and adjusted according to actual application needs, and model training can also be implemented using conventional methods, which will not be elaborated here.

[0031] In one embodiment, before executing tire blowout control, the following steps may be performed: acquiring historical driving data; identifying user driving habits based on the historical driving data; and generating recommended tire blowout settings based on the user driving habits for display on the vehicle, allowing the user to set tire blowout control parameters based on the recommended settings. Specifically, historical driving data over a period of time can be collected, which may include driving environment information, vehicle status information, and user driving operations under different driving scenarios. Based on the historical driving data, scenario operations are classified, and driving operations are associated with the driving environment and vehicle status to obtain user driving habits. Neural networks can be used for data classification to obtain driving operations corresponding to different driving scenarios, thereby forming a user driving habit dataset. When the user sets tire blowout control parameters through the vehicle's infotainment system, parameter recommendations can be made based on the user's driving habits and displayed on the system. The user can then set the parameters according to the displayed recommendations, ensuring that tire blowout control conforms to the user's daily usage habits and enhancing the user experience. The corresponding user driving habits can be updated periodically, and the corresponding recommended tire blowout settings can be regenerated based on the updated user habits. Multiple sets of tire blowout recommended settings and their mapping relationships to the actions performed by the vehicle after a blowout can be pre-stored on the vehicle or in the cloud. After acquiring the user's driving habits, the similarity between these habits and the actions performed after a blowout can be calculated. For example, the user's driving habits consist of a sequence of multiple actions, denoted as the habit sequence, and the sequence corresponding to the actions performed after a blowout is denoted as the target sequence. When calculating similarity, identical actions in the sequence are marked as 1, and different actions are marked as 0. The proportion of 1s is used as the final similarity score. If the similarity exceeds a preset threshold, the tire blowout recommended settings corresponding to the actions performed after the blowout can be retrieved based on this mapping relationship. Each tire blowout recommended setting can be verified experimentally. This method ensures the reliability of the tire blowout recommended settings while meeting the user's personalized needs.

[0032] Step S110: When a tire blowout is determined based on the tire state information, the vehicle driving environment information and the vehicle driving state information are input into a pre-trained decision model to obtain the current control strategy. The decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving state information and the corresponding control strategy.

[0033] In one embodiment, a decision model pre-training process can be performed before tire blowout control. This pre-training process may include the following steps: constructing a training dataset, wherein the training dataset includes different tire blowout scenarios and different vehicle driving states; inputting the training dataset into a deep learning neural network, and iteratively adjusting the loss value of the loss function through the network to minimize the loss value, thereby obtaining the decision model, wherein the loss function is constructed based on the deviation between the output control command and the target control command of the deep learning neural network. Specifically, vehicle driving state information under different driving environments, for different tires and at different blowout locations can be collected to form a training dataset. The driving environment can be used to construct tire blowout scenarios. The data in the training dataset serves as the input to the deep learning neural network. Initially, the weight values ​​of each layer of the deep learning neural network are randomly initialized. The training data is propagated forward through the deep learning neural network to obtain an output value, which can be the control command under the corresponding tire blowout scenario. The control command can be a sequence of commands. The target control command under the corresponding tire blowout scenario can be set as the target value, and a loss function is constructed based on the deviation between the target value and the output value. The target control command can also be a sequence of commands. The deviation between two command sequences can be obtained by calculating their similarity and normalizing it. For example, the loss function can be expressed as:

[0034] L=∑ m 0|y (i) -y *(i) |

[0035] Where y can represent the output control command, which is an observed value; y *The target control command can be represented as the true value, m is the number of control commands, and i represents the i-th control command. The specific form of the loss function can also be set and adjusted according to actual application needs, and is not restricted here. The loss value calculated based on the loss function is backpropagated to update the weights of the deep learning neural network, so that the loss value converges. Through continuous iterative updates, the loss value reaches a preset threshold, at which point the decision model is obtained. This decision model receives vehicle driving environment information and vehicle driving state information, and outputs corresponding tire blowout control commands to form a control strategy. Further, the control strategy can be integrated with the tire blowout control parameters set by the user to obtain the current control strategy. The tire blowout control commands mainly include control commands for the steering system, braking system, suspension system, and acceleration system, while the tire blowout parameter control also includes tire blowout takeover timing, tire blowout warning sensitivity, etc. Integrating the two and adjusting some control commands according to the user's personalized settings parameters, the current control strategy is obtained. Specifically, the user-personally set parameters can be set to have higher control priority. Of course, control priorities can also be set for different control commands. For example, commands related to vehicle movement have higher priorities, while commands related to user driving comfort have lower priorities. When adjusting parameters, the command set by the user is judged according to the parameter priority to determine whether it meets the priority condition. If it does not meet the condition, the adjustment is not allowed, thereby ensuring the safety of control.

[0036] In one embodiment, after controlling the vehicle according to the control instructions included in the current control strategy, the method further includes: determining that the vehicle is currently in a safe driving state based on vehicle driving environment information and vehicle driving status information; if so, disengaging the automatic tire blowout control; and displaying the control strategy through the vehicle-mounted display device, allowing the driver to control the vehicle based on the displayed information. Specifically, the vehicle status is continuously monitored. When it is determined, based on the monitored vehicle driving environment information and vehicle driving status information, that the vehicle does not pose a risk of rollover or collision, the automatic tire blowout control can be disengaged, allowing the driver to take over. Before the driver takes over, the user is prompted via voice or display interface that the automatic tire blowout control is about to be disengaged, and the relevant takeover operation is displayed on the display interface, so that the user can smoothly take over and control the vehicle to continue driving, or drive the vehicle into a specific lane for a safe stop.

[0037] In one embodiment, the step of determining whether the vehicle is in a safe driving state based on vehicle driving environment information and vehicle driving status information includes: if the deviation angle between the vehicle's driving direction and the current driving lane is less than a preset angle threshold, and there are no vehicles in the current driving lane and adjacent lanes, or the distance between the current vehicle and adjacent vehicles is greater than a set distance threshold, then the current vehicle is determined to be in a safe driving state. Specifically, if there are no other vehicles around the current vehicle, and the current driving direction does not deviate significantly after the tire blowout, or the current vehicle does not experience a sudden increase in acceleration, then the current vehicle can be determined to be in a safe driving state. Automatic tire blowout control can then be deactivated, switching back to manual operation, with the driver taking over the vehicle. Simultaneously, corresponding prompts can be displayed on the vehicle's screen. The specific conditions for determining whether the vehicle is in a safe driving state can be any combination of the aforementioned conditions, and can be adjusted according to actual needs or user settings; no restrictions are imposed here. Of course, during the automatic tire blowout control process, vehicle driving status information and driving environment information can be continuously monitored. After determining that the vehicle is in a safe driving state, automatic tire blowout control can be deactivated, with the driver taking over the vehicle. Before switching back to manual operation, a thorough safety assessment should be conducted to allow the driver sufficient response time, ensuring safety and enhancing the user experience.

[0038] Step S120: Control the vehicle according to the control instructions contained in the current control strategy to ensure the vehicle is in a safe state.

[0039] In one embodiment, the safe state here can be either the safe driving state of the vehicle or the vehicle parked in a safe area. The process for handling the safe driving state of the vehicle has been described in detail in the preceding steps and will not be repeated here. The following describes the control of the vehicle's safe parking. When the vehicle activates the automatic tire blowout control, the current control strategy can be sent to the corresponding controller, which will then send the corresponding control commands to each vehicle-end system. For example, the steps of controlling the vehicle according to the control commands contained in the current control strategy include: controlling the vehicle's steering angle and driving direction through steering control commands; controlling the vehicle's deceleration through braking control commands; adjusting the suspension stiffness through suspension control commands; and adjusting the power output through power adjustment commands. Specifically, after receiving the steering control command, the steering system can automatically adjust the steering wheel angle to maintain the stability of the vehicle's driving trajectory; after receiving the braking control command, the braking system can perform emergency braking or perform intermittent braking to reduce the vehicle speed and prevent loss of vehicle control; after receiving the suspension control command, the suspension system can adjust the suspension stiffness to improve vehicle stability and comfort; after receiving the power adjustment command, the acceleration system can reduce or cut off the power output when necessary to prevent the vehicle from losing control due to acceleration. Automatic tire blowout control guides the vehicle to a safe zone within the current road. This safe zone can be configured based on the actual scenario and determined by collecting information about the vehicle's driving environment. By automatically stopping in the safe zone, the vehicle provides an emergency response, ensuring timeliness and effectiveness, and protecting the safety of passengers. A final control strategy is formed based on multi-dimensional control commands, ensuring effective and comprehensive vehicle control while also meeting certain comfort requirements while prioritizing passenger safety.

[0040] In one embodiment, the vehicle-mounted system can collect user feedback after each automatic tire blowout control, optimize target control commands based on this feedback, and further optimize the decision-making model to improve its adaptability to the user. Specific feedback information may include aspects such as vehicle stability, comfort, and braking distance during the tire blowout control process, and can be tailored to the user's actual experience; no limitations are imposed here. Target control commands that need adjustment can be matched based on user feedback information. The mapping relationship between features in the specific feedback information and commands can be manually labeled and then obtained by training a recognition model. The recognition model can employ conventional network models such as convolutional neural networks or recurrent neural networks; the specific training process will not be detailed here.

[0041] Based on the technical solutions of the above embodiments of this application, the vehicle can be automatically taken over when a tire blowout occurs, and automatic tire blowout control can be performed according to the user's driving habits and set preference parameters, which can ensure the personal safety of the driver and passengers and enhance the user experience.

[0042] Please see Figure 2 , Figure 2 This is a block diagram of a vehicle tire blowout control system according to an embodiment of this application. The system includes: a monitoring module 20, used to monitor vehicle driving environment information, tire status information, and vehicle driving status information; a decision module 21, used to determine when a tire blowout has occurred based on the tire status information, input the vehicle driving environment information and the vehicle driving status information into a pre-trained decision model to obtain a current control strategy, wherein the decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving status information and the corresponding control strategy; and a control module 22, used to control the vehicle according to the control instructions contained in the current control strategy, so that the vehicle stops in a safe area.

[0043] Please see Figure 3 , Figure 3 This is a schematic diagram of the overall architecture of the vehicle tire blowout control in one embodiment of this application. After the vehicle is powered on, it first determines whether tire blowout control is activated. If tire blowout control is not activated, the vehicle enters human-driven mode. If tire blowout control is activated, the tire status is monitored by the tire status monitoring unit. If no blowout occurs, the vehicle remains in human-driven mode. If a blowout occurs, the tire blowout control system takes over the vehicle and makes decisions and controls through the vehicle-side AI controller. The AI ​​controller integrates a deep learning model, which receives road and environmental information perceived by the external environment perception layer and vehicle driving status information monitored by the vehicle status monitoring unit. It then determines whether the vehicle is in a safe state. If the vehicle is not in a safe state, it outputs the corresponding control commands in the current control strategy to the steering system, braking system, suspension system, and acceleration system. The system continuously senses road information, environmental information, and vehicle driving status information. When the AI ​​controller determines that the vehicle is in a safe state, it automatically exits the tire blowout control system, and the driver takes over the vehicle. The AI ​​controller can integrate a decision module 21 and a control module 22.

[0044] Specifically, after starting the vehicle, the user confirms the system is enabled via the "Tire Blowout Control" menu item in the vehicle's infotainment system. The user can set the tire blowout warning sensitivity to medium, the control system intervention timing to take over within 0.3 seconds of detecting a blowout, and select the "Balanced" control strategy, according to their driving habits. While the vehicle is driving smoothly on the highway, the tire condition detection unit monitors the pressure and temperature of each tire in real time. Simultaneously, the cameras and radar in the external environment perception layer continuously scan the surrounding environment, ensuring comprehensive perception of traffic, roads, and potential obstacles. When the vehicle approaches a construction area, the right rear tire accidentally runs over a sharp rock, causing an instant tire blowout. The tire condition detection unit immediately detects the sharp drop in pressure and sends a blowout signal to the AI ​​controller within 0.3 seconds. Upon receiving the blowout signal, the AI ​​controller quickly invokes a deep learning model for real-time calculation and decision-making. Based on the current vehicle speed (100 km / h), the road conditions ahead (no emergency obstacles, but a construction area on the right), and the vehicle's state (about to lose balance), the AI ​​controller decides to take emergency braking and slight steering maneuvers to stabilize the vehicle and prevent loss of control. The steering system automatically adjusts the steering wheel angle to maintain a stable driving trajectory. The braking system applies intermittent braking to effectively reduce speed and prevent wheel lock-up and skidding. The suspension system also adjusts its stiffness according to real-time road conditions to enhance vehicle stability. The acceleration system cuts off power output to prevent the vehicle from becoming increasingly uncontrollable due to acceleration. Under the precise control of the AI ​​controller, the vehicle smoothly decelerates and eventually comes to a safe stop on the emergency lane.

[0045] The various modules in the aforementioned vehicle tire blowout control system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the terminal's memory, or stored in software form in the terminal's memory, so that the processor can call and execute the corresponding operations of each module. This processor can be a central processing unit (CPU), microprocessor, microcontroller, etc.

[0046] like Figure 4 The diagram shown illustrates the internal structure of a computer device in one embodiment. A computer device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: monitoring vehicle driving environment information, tire status information, and vehicle driving state information; when the tire status information indicates a tire blowout, inputting the vehicle driving environment information and vehicle driving state information into a pre-trained decision model to obtain a current control strategy, wherein the decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving state information and corresponding control strategies; and controlling the vehicle according to the control instructions contained in the current control strategy to ensure the vehicle is in a safe state.

[0047] In one embodiment, before the processor implements the monitoring of vehicle driving environment information, tire status information, and vehicle driving status information, it further includes setting tire blowout control parameters, such that before controlling the vehicle according to the current control strategy, the tire blowout control parameters are called to correct the corresponding control command.

[0048] In one embodiment, when the processor is executed, it acquires historical driving data; identifies user driving habits based on the historical driving data; and generates tire blowout recommended settings information based on the user driving habits for display on the vehicle, so that the user can set tire blowout control parameters based on the recommended settings information.

[0049] In one embodiment, when the processor executes the above-mentioned process, the pre-training process of the decision model includes: constructing a training dataset, wherein the training dataset includes different tire blowout scenarios and different vehicle driving states; inputting the training dataset into a deep learning neural network, and adjusting the loss value of the loss function through network iteration to minimize the loss value, thereby obtaining the decision model, wherein the loss function is constructed based on the deviation between the output control command and the target control command of the deep learning neural network.

[0050] In one embodiment, when the processor executes the above-mentioned process, after implementing vehicle control according to the control instructions contained in the current control strategy, it further includes: determining that the current vehicle is in a safe driving state based on the vehicle driving environment information and the vehicle driving status information, then exiting the automatic tire blowout control, and displaying the control strategy through the vehicle-mounted display device, so that the driver can control the vehicle based on the displayed information.

[0051] In one embodiment, when the processor executes the above-mentioned step of determining that the current vehicle is in a safe driving state based on the vehicle driving environment information and the vehicle driving state information includes: if the deviation angle between the vehicle's driving direction and the current driving lane is less than a preset angle threshold, and there are no vehicles in the current driving lane and adjacent lanes or the distance between the current vehicle and adjacent vehicles is greater than a set distance threshold, then the current vehicle is determined to be in a safe driving state.

[0052] In one embodiment, when the processor executes the above-mentioned steps of controlling the vehicle according to the control instructions contained in the current control strategy, the steps include: controlling the vehicle steering angle and driving direction through steering control instructions; controlling the vehicle deceleration magnitude through braking control instructions; adjusting the suspension stiffness through suspension control instructions; and adjusting the power output through power adjustment instructions.

[0053] In one embodiment, the aforementioned computer device can be used as a server, including but not limited to a standalone physical server or a server cluster consisting of multiple physical servers. The computer device can also be used as a terminal, including but not limited to mobile phones, tablets, personal digital assistants, or smart devices. Figure 4 As shown, the computer device includes a processor, non-volatile storage medium, internal memory, display screen, and network interface connected via a system bus.

[0054] The processor of this computer device provides computing and control capabilities to support the operation of the entire device. The non-volatile storage medium of the computer device stores the operating system and computer programs. These programs can be executed by the processor to implement the vehicle tire blowout control method provided in the above embodiments. The internal memory of the computer device provides a cached operating environment for the operating system and computer programs stored in the non-volatile storage medium. A display interface can show data via a screen. The screen can be a touchscreen, such as a capacitive or electronic screen, and can generate corresponding instructions by receiving clicks on controls displayed on the touchscreen.

[0055] Those skilled in the art will understand that Figure 4 The structure of the computer device shown in the figure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0056] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon. When executed by a processor, the computer program performs the following steps: monitoring vehicle driving environment information, tire status information, and vehicle driving state information; when the tire status information indicates a tire blowout, inputting the vehicle driving environment information and the vehicle driving state information into a pre-trained decision model to obtain a current control strategy, wherein the decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving state information and the corresponding control strategy; and controlling the vehicle according to the control instructions contained in the current control strategy to ensure the vehicle is in a safe state.

[0057] In one embodiment, before the computer program is executed by the processor to monitor vehicle driving environment information, tire status information, and vehicle driving status information, it further includes: setting tire blowout control parameters, such that before controlling the vehicle according to the current control strategy, the tire blowout control parameters are called to correct the corresponding control command.

[0058] In one embodiment, when the computer program is executed by the processor, it acquires historical driving data; identifies user driving habits based on the historical driving data; and generates tire blowout recommended settings information based on the user driving habits for display on the vehicle, so that the user can set tire blowout control parameters based on the recommended settings information.

[0059] In one embodiment, when the computer program is executed by a processor, the pre-training process of the decision model includes: constructing a training dataset, wherein the training dataset includes different tire blowout scenarios and different vehicle driving states; inputting the training dataset into a deep learning neural network, and adjusting the loss value of the loss function through network iteration to minimize the loss value, thereby obtaining the decision model, wherein the loss function is constructed based on the deviation between the output control command and the target control command of the deep learning neural network.

[0060] In one embodiment, when the computer program is executed by the processor, after implementing vehicle control according to the control instructions contained in the current control strategy, it further includes: determining that the current vehicle is in a safe driving state based on the vehicle driving environment information and the vehicle driving status information, then exiting the automatic tire blowout control, and displaying the control strategy through the vehicle-mounted display device, so that the driver can control the vehicle according to the displayed information.

[0061] In one embodiment, when the computer program is executed by the processor, the step of determining that the current vehicle is in a safe driving state based on the vehicle driving environment information and the vehicle driving state information includes: if the deviation angle between the vehicle's driving direction and the current driving lane is less than a preset angle threshold, and there are no vehicles in the current driving lane and adjacent lanes, or the distance between the current vehicle and adjacent vehicles is greater than a set distance threshold, then the current vehicle is determined to be in a safe driving state.

[0062] In one embodiment, when the computer program is executed by the processor, the steps of controlling the vehicle according to the control instructions contained in the current control strategy include: controlling the vehicle steering angle and driving direction by steering control instructions; controlling the vehicle deceleration by braking control instructions; adjusting the suspension stiffness by suspension control instructions; and adjusting the power output by power adjustment instructions.

[0063] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), etc.

[0064] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for controlling tire blowout in vehicles, characterized in that, include: Monitor vehicle driving environment information, tire status information, and vehicle driving status information; When the tire status information indicates a tire blowout, the vehicle driving environment information and the vehicle driving status information are input into a pre-trained decision model to obtain the current control strategy. The decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving status information and the corresponding control strategy. The vehicle is controlled according to the control instructions contained in the current control strategy, so that the vehicle is in a safe state.

2. The vehicle tire blowout control method according to claim 1, characterized in that, Before monitoring vehicle driving environment information, tire condition information, and vehicle driving status information, the following is also included: The tire blowout control parameters are set so that before controlling the vehicle according to the current control strategy, the tire blowout control parameters are invoked to correct the corresponding control command.

3. The vehicle tire blowout control method according to claim 2, characterized in that, The method further includes: Obtain historical driving data; Identify user driving habits based on the historical driving data; Based on the user's driving habits, recommended tire blowout settings are generated and displayed on the vehicle, allowing the user to set tire blowout control parameters based on the recommended settings.

4. The vehicle tire blowout control method according to claim 1, characterized in that, The pre-training process of the decision model includes: Construct a training dataset, which includes different tire blowout scenarios and different vehicle driving states; The training dataset is input into a deep learning neural network, and the loss value of the loss function is adjusted through network iteration to minimize the loss value, thereby obtaining the decision model. The loss function is constructed based on the deviation between the output control command and the target control command of the deep learning neural network.

5. The vehicle tire blowout control method according to claim 1, characterized in that, After controlling the vehicle according to the control instructions contained in the current control strategy, the following steps are also included: If the vehicle is determined to be in a safe driving state based on the vehicle's driving environment information and driving status information, the automatic tire blowout control will be deactivated, and the control strategy will be displayed on the vehicle-mounted display device, allowing the driver to control the vehicle based on the displayed information.

6. The vehicle tire blowout control method according to claim 1, characterized in that, The steps for determining whether the vehicle is currently in a safe driving state based on the vehicle driving environment information and the vehicle driving status information include: If the deviation angle between the vehicle's driving direction and the current driving lane is less than a preset angle threshold, and there are no vehicles in the current driving lane or adjacent lanes, or the distance between the current vehicle and adjacent vehicles is greater than a set distance threshold, then the current vehicle is determined to be in a safe driving state.

7. The vehicle tire blowout control method according to claim 1, characterized in that, The steps for controlling the vehicle according to the control instructions contained in the current control strategy include: The vehicle's steering angle and direction of travel are controlled via steering control commands; The amount of vehicle deceleration is controlled by braking control commands; Adjust the suspension stiffness using suspension control commands; The power output is adjusted by power adjustment commands.

8. A vehicle tire blowout control system, characterized in that, include: The monitoring module is used to monitor vehicle driving environment information, tire status information, and vehicle driving status information. The decision module is used to determine when a tire blowout occurs based on the tire status information, and inputs the vehicle driving environment information and the vehicle driving state information into a pre-trained decision model to obtain the current control strategy. The decision model is used to characterize the mapping relationship between different vehicle driving environment information and vehicle driving state information and the corresponding control strategy. The control module is used to control the vehicle according to the control instructions contained in the current control strategy, so that the vehicle stops in a safe area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle tire blowout control method as described in any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle tire blowout control method as described in any one of claims 1 to 7.

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