4886 results about "Steering angle" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

The invention belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

An embodiment related to a system, wherein the system is operable to: detect an intersection and a target vehicle near the intersection; determine a current location of a host vehicle and the target vehicle; detect a target turn lane; estimate parameters of the target vehicle, wherein the parameters of the target vehicle comprise a vehicle type, a vehicle model, a steering angle, and a turn radius; predict a trajectory of motion of the target vehicle; establish a boundary for an impact zone by the host vehicle; and determine a collision avoidance action for the host vehicle to avoid the impact zone.

The present application relates to the technical field of electric vehicles, and discloses a fault-tolerant actuator control method and apparatus for an electric vehicle, a storage medium, and a vehicle. The method comprises: acquiring vehicle-mounted sensor information and a reference trajectory of a distributed drive electric vehicle; using an MPC algorithm to perform trajectory tracking on the basis of the vehicle-mounted sensor information and the reference trajectory to obtain an expected front wheel steering angle; when at least one steering actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a differential power moment on the basis of the expected front wheel steering angle and an actual front wheel steering angle corresponding to a previous moment by using a sliding mode control (SMC) algorithm; when at least one driving actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a torque corresponding to the current moment on the basis of the differential power moment by using a distribution optimization algorithm based on minimum tire adhesion utilization; and controlling steering of the distributed drive electric vehicle on the basis of the torque corresponding to the current moment. The present application implements reliable control of the vehicle in a case where a steering actuator and a driving actuator fail at the same time.

The invention relates to the technical field of vehicle cruise control, and discloses a neural network driven vehicle adaptive cruise control method. Real-time driving data are collected through a multi-modal sensor, spatial-temporal feature fusion is carried out through a multi-layer convolutional neural network to generate dynamic environment sensing data, and a pre-trained adaptive decision model is input to obtain driving strategy parameters. And constructing a mixed integer programming model on the basis, globally planning a cruise path by adopting an incremental branch and bound algorithm integrating a dynamic relaxation threshold and a heuristic pruning strategy, and outputting optimal cruise trajectory data. A hierarchical control framework comprising a planning layer, a coordination layer and an execution layer is constructed, the planning layer generates a global trajectory sequence, the coordination layer dynamically corrects a local trajectory, and the execution layer achieves vehicle longitudinal acceleration and transverse steering angle tracking based on a robust sliding mode control algorithm and outputs a vehicle control instruction to complete self-adaptive cruise control. And the cruise control performance, safety and stability of the vehicle in a complex environment are improved.

The invention discloses an intelligent suspension control system based on complex road condition recognition, relates to the technical field of automobile damping adjustment, and solves the technical problems that the system is difficult to adapt to complex and changeable road conditions, suspension parameters cannot be accurately adjusted in real time according to actual road conditions, and the performance of a vehicle under the complex road conditions is limited. After the road condition information acquisition module acquires data of various sensors, the road condition comprehensive identification and analysis module deeply analyzes the data and judges that a vehicle is in a curve or straight driving state, and when the vehicle is driven on the curve, the curve intelligent analysis module determines whether the vehicle is in a curve or straight driving state according to information such as the steering angle, the quality, the vehicle speed and the turning radius of the vehicle. The turning centrifugal force and the heeling moment are calculated by means of a vehicle dynamics model, the adaptive heeling rigidity and damping requirements are determined accordingly, current suspension parameters are compared, the hydraulic oil and damping hydraulic oil flow and pressure adjusting amount are calculated, and precise suspension regulation and control, vehicle heeling reduction and curve control stability strengthening are achieved.

The invention discloses a cooperative control method for multiple AGVs for cooperatively carrying heavy objects, and belongs to the technical field of automatic logistics, and the method comprises the steps: taking a high-dimensional environment description vector as a model condition, inputting the model condition into an improved conditional diffusion model which is introduced into a linkage constraint feature channel in a reverse denoising stage of a conventional conditional diffusion model, and obtaining a preliminary scheme; and then correcting the preliminary scheme by adopting a popular theory and a high-dimensional differential geometric constraint, and then ensuring cooperative control of multiple AGVs by adopting a dynamic environment perception and linkage state prediction mechanism, an online feedback and local path adjustment mechanism and a multi-heavy-load synchronous control strategy in control operation according to the corrected scheme. According to the method, the initial path is obtained based on the improved conditional diffusion model, the feasibility and the collaboration of a generation result are improved, the vehicle speed, the acceleration and the steering angle are controlled and optimized by applying a high-dimensional differential geometry dynamic control method, and the dynamic consistency of synchronous movement of multiple vehicles is ensured.

The invention belongs to the technical field of intelligent driving, and particularly relates to an intelligent driving method and system with a body, and the method comprises the steps: fusing the V2X data of a vehicle-mounted laser radar, a millimeter wave radar and a road side unit, and obtaining the vehicle driving information; constructing a centimeter-level dynamic environment model by adopting a space-time alignment algorithm, and realizing multi-source data space-time synchronization and obstacle real-time prediction; designing a hierarchical reward function including security, efficiency and comfort rewards based on a user risk cognition mechanism, and generating an end-to-end reinforcement learning decision in combination with a Transform architecture; optimizing the dynamic environment model through reinforcement learning decision; inputting vehicle driving information into the optimized centimeter-level dynamic environment model, and predicting obstacles and vehicle tracks in a vehicle driving path; an MPC-Hybrid feedforward-feedback control system is constructed, a feedforward module is adopted to calculate a front wheel turning angle and acceleration parameters according to obstacles in a vehicle driving path and a vehicle track, and a feedback module is adopted to optimize transverse and longitudinal errors; controlling the driving direction of the vehicle according to the front wheel turning angle and the acceleration parameter transverse and longitudinal errors; according to the invention, through deep fusion of vehicle-road collaborative perception and reinforcement learning decision, the reliability and adaptability of the automatic driving system are significantly improved.

Methods and apparatus to adjust a steering angle of a vehicle in a self-driving mode are described herein. An example vehicle disclosed herein includes a steering controller including instructions and programmable circuitry to execute the instructions to access a path follower (PF) angle request, generate a virtual boost curve (VBC) angle request based on a torque input to a steering wheel, determine an angle blending weight based on the torque input and a speed of the vehicle, determine a final angle request based on the PF angle request, the VBC angle request, and the angle blending weight, and convert the final angle request to a torque request to be used to adjust a steering angle of the vehicle via a motor.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention discloses a server, a heat dissipation control method and device thereof and a storage medium, relates to the field of servers, and aims to solve the problems of fan redundancy and power waste caused by selection of a high-power fan for efficient heat dissipation, and the method comprises the following steps: acquiring actual temperature values of a plurality of heat dissipation control positions in the server; in response to a target temperature value meeting a reference adjustment condition in the plurality of actual temperature values, determining a target module in the plurality of fan modules based on a heat dissipation control position corresponding to the target temperature value; and adjusting the rotating speed of the target module and / or the steering angle of the rotor based on the target temperature value and the regulation and control temperature value corresponding to the heat dissipation control position. According to the invention, an area with a heat dissipation requirement can obtain more air volume, heat dissipation balance is realized, the heat dissipation efficiency is improved, a high-power fan does not need to be arranged in each area, and redundant configuration and power consumption waste of the fan are reduced.

The invention discloses a vehicle control method and device and a vehicle, and belongs to the technical field of vehicles. Comprising the steps that under the condition that a steering wheel of a target vehicle is kept in the middle and rear wheels of the target vehicle steer abnormally, a target front wheel steering angle is determined on the basis of the difference value between the current rear wheel steering angle of the target vehicle and a target rear wheel steering angle and vehicle parameters, and the target rear wheel steering angle is the angle of the rear wheels of the target vehicle in the middle-back state; the target front wheel rotation angle is the rotation angle of the front wheels of the target vehicle for compensating the abnormal steering of the rear wheels; determining a target compensation torque based on the target front wheel steering angle; and controlling a front wheel motor of the target vehicle to output the target compensation torque so as to keep the target vehicle in a straight driving state by adjusting the front wheel angle of the target vehicle. Thus, the target compensation torque is output through the front wheel motor, track deviation caused by abnormal steering of the rear wheels can be compensated, and therefore the target vehicle can be kept running in a straight line, and the driving safety is improved.

The invention relates to an automatic driving vehicle steering and suspension cooperative control method based on reinforcement learning, and the method specifically comprises the following steps: 1, building a dynamic model of a vehicle steering system and a suspension system, and obtaining transverse and vertical vehicle motion states through controlling a steering angle and a suspension acting force; 2, constructing a control action and reward function of a vehicle steering system and a suspension system, and forming a reinforcement learning exploration strategy of vehicle steering and suspension cooperative control; 3, constructing intelligent agents of a steering system and a suspension system; and 4, constructing a reinforcement learning environment, and realizing cooperative control of the steering system and the suspension system through a multi-agent reinforcement learning method. According to the method, different vertical and transverse plane environments and different learning tasks are comprehensively considered, the data input dimension is reduced, the optimal motion control strategies under different working conditions are considered, and on the premise that accurate path tracking is guaranteed, the transverse roll stability and the vertical smoothness and comfort are improved through cooperative steering system and suspension system control.

Methods and systems are disclosed for controlling a vehicle. The method comprises receiving or determining information about a current state of the vehicle, about a target state of the vehicle and about a current state of an other traffic participant. The method further comprises iteratively optimising a set of control parameters for the vehicle based on an overall cost value associated with the set of control parameter values. The optimisation comprises: determining a potential future state of the vehicle, based on the current state of the vehicle and the set of control parameter values; determining a first cost value using a first cost function, the first cost value being based on a difference between the potential future state and the target state of the vehicle; predicting a future state of the other traffic participant, based on the current state of the other traffic participant; determining a second cost value using a second cost function, the second cost value being based on a perceived risk posed by the vehicle in the potential future state of the vehicle to the other traffic participant in the future state of the other traffic participant; and determining the overall cost value associated with the set of control parameter values, based on the first cost value and the second cost value. Based on the optimised set of control parameters, control signals may be determined that configure the vehicle to adjust its speed and / or steering angle.

A steering device controls a turning angle of each wheel in a vehicle. The vehicle has wheels, which are not mechanically constrained to each other. The wheels are steered independently. The steering device includes an operation determination unit, a coordinate determination unit, a turning angle calculator, and steering actuators. The operation determination unit determines a vehicle operation mode based on the vehicle state. The coordinate determination unit determines the coordinates of the turning center of the vehicle based on the vehicle operation mode. The turning angle calculator calculates a turning angle command value for each wheel based on the coordinates of the turning center. The steering actuators are provided correspondingly to the respective wheels. These steering actuators steer the respective wheels according to the turning angle command values.

A remote computer system may receive data representative of a state of an autonomous vehicle traversing an environment. A representation of the environment and the state may be input to a first machine learned model, which may output a vector representation of the environment at a first time. The vector representation may be input to a second machine learned model, which may output a second vector representation of the environment at a second time. A third machine learned model may receive the second vector representation and may output a mean velocity and a mean steering angle of the vehicle at the first time and associated uncertainty variables. Based on the mean velocity, the mean steering angle, and the uncertainty variables, a predicted vehicle state at the first time and an associated uncertainty may be determined and the vehicle may be controlled based on the predicted vehicle state and the uncertainty.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

The invention belongs to the field of road vehicle control systems, and relates to a method for predicting the risk of collision between an electric bicycle and a vehicle based on the multi-modal behavior of the electric bicycle, which utilizes physical modeling and takes the influence of the braking distance and the transverse and longitudinal stability of the electric bicycle on the vehicle as a basic risk; automobile risks caused by the environment are further considered, and multi-dimensional environment risk assessment modeling is carried out by integrating four dimensions of dynamic distance-speed, adjacent vehicle types, traffic flow density and weather conditions (precipitation, temperature, wind speed and visibility); the risk of electric bicycle operation behaviors (acceleration change and steering angle and frequency) to the automobile is combined; the complex relation between the environment change and the whole vehicle risk is captured in real time by using the environment sensitivity coefficient, and finally a set of comprehensive, accurate and real-time multi-dimensional comprehensive risk assessment method is provided for the vehicle for the multi-mode riding behavior of the electric bicycle, so that the collision risk in the complex traffic flow environment is reduced.

Disclosed is a corner module apparatus for a vehicle. The corner module includes a driving unit that provides drive power to a wheel of the vehicle, a braking unit that interferes with rotation of the wheel to generate braking power, and a suspension unit connected to the driving unit. The suspension unit absorbs shock transferred from a road surface. The corner module also includes a steering unit connected to the suspension unit and rotated about a steering axis inclined with respect to the wheel. The steering unit adjusts a steering angle of the wheel.

The invention discloses a cooperative steering control method considering man-machine mutual reliability, and aims to solve the problem of man-machine conflict in an existing man-machine co-driving system. The invention relates to the field of intelligent driving. The system specifically comprises an expected path module, a man-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a man-machine co-driving vehicle. The expected path module generates an expected path between the driver and the controller; the man-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected path of the driver and the controller; the driver experience evaluation module outputs the driver trust degree of the controller based on the vehicle state; the weight distribution module is used for outputting a driver control weight; and the non-cooperative game module outputs an optimal turning angle in combination with the expected path of the driver and the controller and the control weight of the driver, and controls the man-machine co-driving vehicle to complete the cooperative steering action.

The invention provides a distributed steer-by-wire vehicle stability control method. The method comprises the steps that a distributed steer-by-wire vehicle dynamics model, a two-degree-of-freedom reference model and a four-wheel steering angle distribution model are established; in the design of the yaw stability controller, a hybrid sensitivity # imgabs0 # robust control method is adopted, the uncertainty of the system is fully considered, and the robustness of the system can be considered while the target tracking performance is ensured.

Methods and apparatus are provided for performing an assisted driving trailer reversing operation including a user interface configured to generate an initiation signal in response to a user input, a processor configured to generate a control signal in response to the initiation signal indicative of an inverse steering ratio, a steering angle sensor to detect a steering direction and a steering angle from a center steering position, a steering controller configured to receive the steering angle and the steering direction from the steering angle sensor, to invert the steering angle to generate an inverted steering angle such that the inverted steering angle has a same angular magnitude as the steering angle and an opposite direction than the steering direction, and a vehicle control system for controlling a movement of a vehicle in response to the inverted steering angle.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

The invention discloses a reconfigurable distributed driving multi-axis vehicle trajectory tracking method and a carrying device. The method comprises the following steps: acquiring longitudinal and transverse movement speeds and yaw velocity of a vehicle body in real time; acquiring position information and target path information of a vehicle, and matching a trajectory tracking target point; establishing a vehicle three-degree-of-freedom dynamic model for trajectory tracking; according to a longitudinal dynamic model of the vehicle, a sliding mode control method is adopted to solve longitudinal force needed by the whole vehicle, and the effect of tracking the specified speed is achieved; a tracking error model is obtained, and the rotation angle of each wheel and the yaw moment of the whole vehicle are calculated based on a nonlinear model predictive control algorithm; distributing the longitudinal tire force of each wheel according to the definition of the tire adhesion rate and the requirements of the longitudinal force and the yawing moment of the whole vehicle output by the upper-layer controller, and converting the longitudinal tire force into motor torque; and controlling a steering system and a wheel motor of the vehicle to execute the wheel steering angle and torque instruction. The control stability and trajectory tracking precision of the vehicle can be improved.

The invention relates to a method for controlling the stability of a transport vehicle. Comprising the following steps that dynamic parameters of the speed, the acceleration, the steering angle and the sideslip angle of a vehicle are collected through a vehicle sensor comprising an accelerometer, a gyroscope and a wheel speed sensor; in combination with various sensors such as a vehicle-mounted radar, a laser radar and a camera, conditions of wet, slippery, rugged and uneven road surfaces are monitored in real time, and road surface friction coefficient parameters are generated through a data fusion algorithm; according to the dynamic state of the vehicle and the real-time road surface state, the stability of the vehicle is evaluated through a mathematical model, and the potential risk of current instability is calculated; a plurality of stability control modes are preset according to different driving scenes including expressways, mountain roads and wet and slippery roads, and different control parameters and algorithms are adopted in each mode; a sensor including a driver state monitoring system inside a vehicle and external environment data including meteorological data and traffic information are utilized to predict operation behaviors including sudden acceleration and sudden braking of a driver.

A method for controlling a steer-by-wire steering system for a motor vehicle having steerable front and rear wheels in emergency steering operation comprises checking the steering system for the presence of a fault state and performing the emergency steering operation to maintain original driving behaviour if a fault state is detected, including: determining a target lateral acceleration on the basis of a current steering wheel position and a current vehicle speed, determining a yaw moment and, from the yaw moment, a general wheel steering angle for the original driving behaviour, where the general wheel steering angle is equal to a difference between a front wheel steering angle and a rear wheel steering angle, determining a front wheel steering angle correction and a rear wheel steering angle correction, and determining drive torques and / or braking torques for the front wheels and the rear wheels, which are distributed appropriately by the torque vectoring system.

The invention discloses a vehicle control method, electronic equipment, a collision stability control system and a vehicle. The vehicle control method comprises the steps that it is determined that the vehicle meets an unstable state; and according to the target rear wheel steering angle value, the rotation angle of the rear wheels of the vehicle is controlled, and according to the target torque value, a four-wheel motor of the vehicle is controlled to conduct driving and / or braking. According to the method, when the vehicle is in the unstable state, the rear wheel turning angle can be adjusted, the four-wheel motor of the whole vehicle can be driven and / or braked, additional yaw compensation torque is generated to maintain the stability of the vehicle, a driver is helped to correct the posture of the vehicle body in the initial stage of the unstable state, and losses caused by secondary collision after the vehicle is unstable are avoided.

The invention provides a vehicle state estimation method considering noise disturbance and quality parameter mismatch. The method comprises the steps that the longitudinal acceleration, the front wheel steering angle and the transverse acceleration of a vehicle are obtained through a vehicle-mounted sensor; based on the obtained longitudinal acceleration, the front wheel steering angle and the transverse acceleration, a nonlinear three-degree-of-freedom vehicle dynamics model of the longitudinal direction, the transverse direction and the yawing direction of the vehicle is established, discretization is conducted on the established model, and unscented transformation is adopted to process the nonlinear transmission problem of the mean value and the covariance; an unscented Kalman filter is constructed based on the model to carry out state prediction and prediction correction on a vehicle system, and a strong tracking thought and adaptive filtering are introduced to deal with a model parameter mismatch problem and noise disturbance uncertainty. According to the method, environmental interference with uncertain noise statistical characteristics and complex conditions with uncertain vehicle model parameters are considered, vehicle-mounted sensor information and measurement information can be fully utilized, and accurate estimation of vehicle state parameters under uncertain external disturbance is achieved.

The invention relates to the technical field of automobile electric control, in particular to a new energy automobile electric control steering control method and system, and the method comprises the steps: obtaining and preprocessing a four-wheel vibration data sequence, carrying out the segmentation and consistency evaluation of the vibration data sequence of each wheel, and selecting a current data sequence in an optimal segment to calculate a road condition influence coefficient. And matching the current data sequence with the historical vibration data sequence, calculating the similarity to determine the influence weight, adjusting the influence coefficient in combination with the steering angle change rate of the historical vibration data sequence, inputting the adjusted pavement condition influence coefficient into the ECU, dynamically adjusting the steering auxiliary torque, and realizing accurate steering control. According to the method, the optimal segment is selected through segment processing and consistency evaluation to accurately reflect the current road condition, the steering auxiliary torque is dynamically adjusted in combination with historical vibration data sequence matching and the steering angle change rate, and the driving safety and comfort are improved.

The invention discloses a vehicle tire burst control method and system, a vehicle, control equipment and a storage medium, and relates to the technical field of vehicle safety control. The method mainly comprises the steps of determining the position of a tire burst wheel according to a vehicle tire burst signal, and identifying a driving scene according to vehicle state information; if the linear driving scene is recognized, acquiring tire braking force pressure according to the position of the flat tire wheel, calculating a yaw torque compensation value according to the tire braking force pressure, performing rear wheel steering adjustment, transferring a driving torque based on the position of the flat tire wheel, and performing vehicle body posture adjustment based on the position of the flat tire wheel; if the scene is recognized as the curve scene, controlling and changing the steering angle of the rear wheel based on the position of the flat tire wheel so as to adjust the yawing torque, transferring the driving torque based on the position of the flat tire wheel, and adjusting the posture of the vehicle body based on the position of the flat tire wheel. According to the vehicle tire burst control scheme, safer driving experience can be provided for the user, and the vehicle using experience and satisfaction degree of the user are improved.