Complete vehicle stability control method for composite braking system

Abstract

The invention relates to a whole vehicle stability control method for a composite braking system, which comprises the following steps of: quickly obtaining target braking requirements of each braking execution unit by adopting an NSGA-II algorithm, solving the response speed difference between a hub motor and an EMB through an EMB response delay model and a hub motor feedforward compensation algorithm, and eliminating braking force oscillation. In a four-wheel pure EMB vehicle stability control strategy, a sliding mode control strategy and a model prediction control strategy are respectively adopted based on control demand difference and algorithm characteristic matching of a drifting risk and an understeering working condition, and meanwhile, an actuator physical constraint and a multi-stage fault monitoring mechanism are embedded, so that the basic stability of the vehicle can still be kept under an extreme working condition.

Description

Technical Field

[0001] This invention belongs to the field of vehicle braking technology, specifically relating to hub motors and electromechanical brakes (EMB), and particularly to a method for controlling the overall vehicle stability of a composite braking system. Background Technology

[0002] With the booming development of the new energy vehicle industry and the rapid advancements in artificial intelligence technology, vehicle chassis are accelerating their evolution from electric chassis to intelligent chassis. A distributed integrated drive and braking intelligent chassis architecture, employing four in-wheel motors and four EMBs, aligns with the technological demands for highly integrated and deeply intelligent vehicle chassis development. The energy regenerative braking function of the in-wheel motors can be superimposed and coupled with the friction braking effect of the EMB, resulting in higher safety levels, faster braking response, and lower system energy consumption for the vehicle braking system. However, current technology has the following limitations / defects: insufficient transient coordination precision in multi-source braking force coupling at the vehicle stability control level; conflicts in yaw moment distribution under the distributed architecture; and insufficient matching between vehicle stability requirements and control mechanisms. Summary of the Invention

[0003] To address the above technical problems, this invention proposes a vehicle stability control strategy for crisis scenarios to improve the active safety level of vehicles, specifically a vehicle stability control method for a composite braking system.

[0004] The technical solution adopted in this invention includes: a method for controlling vehicle stability in a composite braking system, comprising:

[0005] Step S210, determine the battery state (SOC). At that time, a full-domain coordination mode using four sets of hub motors and four sets of EMB actuators is adopted;

[0006] Step S220, determine the battery state (SOC). At that time, a four-wheel pure EMB vehicle stability control strategy will be adopted: determine the yaw rate error, and calculate the deviation between the actual value and the expected value. ,in, It is the deviation between the actual value and the expected value. This is the actual value. It is the expected value, if If the vehicle is deemed to be at risk of tail-swing, the following control strategy will be adopted: the inner rear wheel will be braked to generate a reverse yaw moment, which will quickly suppress the tail swing and restore the vehicle to a stable trajectory.

[0007] Beneficial effects:

[0008] This invention proposes a dynamic stability control system and method for vehicles based on a distributed chassis architecture with four in-wheel motors and four-wheel electromechanical brakes (EMB). Addressing the core issues of large differences in response delays among multiple actuators and high control dimensionality in distributed drive chassis under instability conditions, a multi-objective optimization allocation mechanism is proposed. The NSGA-II algorithm is used to quickly obtain the target braking demand of each braking actuator. The response delay model of the EMB and the feedforward compensation algorithm of the in-wheel motors are used to resolve the response speed difference between the in-wheel motors and the EMB, eliminating braking force oscillations. In the stability control strategy for four-wheel pure EMB vehicles, based on the differences in control requirements and algorithm characteristic matching for fishtail risk and understeer conditions, sliding mode control and model predictive control strategies are adopted respectively. Simultaneously, actuator physical constraints and multi-level fault monitoring mechanisms are embedded to ensure that the vehicle maintains basic stability even under extreme conditions. Attached Figure Description

[0009] Figure 1 The hardware architecture of the vehicle chassis of the present invention is shown;

[0010] Figure 2 A schematic flowchart of the vehicle stability control method of the composite braking system of the present invention is shown;

[0011] Figure 3 A schematic diagram of the delay prediction model and the hub motor feedforward compensation strategy is shown. Detailed Implementation

[0012] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0013] Figure 1 The hardware architecture of the vehicle chassis of the present invention is shown. For example... Figure 1 As shown, the central domain controller collects vehicle operating status information and communicates bidirectionally with four motor controllers and four EMB controllers via CAN FD or FlexRay. The four motor controllers and four EMB controllers, in turn, communicate bidirectionally with four hub motors and four EMB actuators via CAN FD or FlexRay.

[0014] Figure 2 This diagram illustrates a schematic flowchart of the vehicle stability control method for the composite braking system of the present invention. In this vehicle stability control method, the central domain controller acquires real-time vehicle speed (v) through onboard wheel speed sensors, an inertial measurement unit (IMU), and a steering angle sensor. x , vy ), yaw rate ( ), steering angle ( This includes information such as the vehicle's power battery status (SOC). When the vehicle speed exceeds 15 km / h, the central domain controller activates the vehicle stability control module to effectively control vehicle stability. See below for reference. Figure 2 Detailed description of each step:

[0015] Step S210: Determine the battery state SOC. When SOC < 95%, use the four hub motors and four EMB actuators in a global coordination mode.

[0016] The four-group hub motor and four-group EMB actuator global coordination mode includes: ideal yaw rate, multi-objective optimization allocation (solving the Pareto optimal solution through NSGA-II), optimization objectives (yaw angle tracking, minimum energy consumption), and constraints (actuator physical limitations). The aim is to use NSGA-II to synthesize these four factors and output the optimal motor / EMB command to achieve coordinated control. A detailed description follows:

[0017] Ideal yaw rate calculation: based on steering angle ( ) and vehicle speed, and estimate the ideal yaw rate ( ) through a simplified model. ): (Where L is the wheelbase,) (Vehicle longitudinal speed).

[0018] Multi-objective optimization allocation: The optimization variable is the energy feedback torque of the four hub motors ( ) and the clamping force of the four EMBs ( ).

[0019] Optimization objective: Minimize the actual yaw rate ( Deviation from target value Minimize the absolute value of the sideslip angle (in, Minimize the total energy consumption of the actuator. ,in, For wheel indexing, It is the vehicle's lateral speed.

[0020] Constraints: The motor torque shall not exceed the speed-related limits ( The EMB clamping force is within the physical limits. The combined force of the tires does not exceed the adhesion limit. ).in, It is the road surface friction coefficient for the i-th round. It is the normal force of the i-th cycle.

[0021] Command output: The Pareto optimal solution is obtained through the NSGA-II algorithm, and the torque command for each motor is output. ) and EMB clamping force command ( The control period is 20ms, and each optimization iteration is 50 times. The specific algorithm flow is as follows: population initialization, randomly generating 100 sets of torque allocation schemes; performing non-dominated sorting, calculating the 3 objective function values ​​of each scheme, and sorting them hierarchically; using a tournament, selecting the best 20 schemes to enter the next generation; simulating binary crossover (SBX) with a crossover rate of 0.9 to generate new schemes; using polynomial mutation with a mutation rate of 0.1 to avoid local optima; finally, after 50 iterations, selecting the optimal solution from the Pareto front for output.

[0022] Step S210 may include a delay prediction model and a hub motor feedforward compensation strategy, as described in detail below:

[0023] There is a conflict in response speed between in-wheel motor regenerative braking (response time ≤ 20ms) and EMB mechanical braking (response time ≥ 80ms), leading to braking force oscillations in traditional coordination strategies. To resolve the torque oscillations caused by the response speed difference between the in-wheel motor and EMB and achieve precise yaw torque control, a delay prediction model and an in-wheel motor feedforward compensation strategy are designed, such as... Figure 3 As shown, the delay prediction model quantifies the impact of three major factors on EMB action speed: mechanical transmission efficiency (positively correlated with command force), material thermal expansion (temperature effect), and voltage sensitivity, thereby achieving dynamic prediction of delay time.

[0024] The input parameters for the delayed prediction model include: the target clamping force command value ( The parameters include: EMB actuator temperature (Temp, range -40℃ to 150℃), and system power supply voltage (Voltage, range 18V to 32V). The reference delay is set to 80ms, and increases linearly with the command force, calculated using the following formula: ,in, This is the clamping force influence coefficient. The baseline delay is used.

[0025] The delayed prediction model is obtained through calculation:

[0026] Temperature compensation: Based on 25℃, temperature deviation increases response delay. The calculation formula is as follows: ,in, This is the temperature influence coefficient. It is the temperature of the EMB actuator. It is a temperature compensation item.

[0027] Voltage compensation term: Based on 24V, a decrease in voltage causes an increase in response delay. The calculation formula is as follows: ,in, The voltage influence coefficient is denoted as , where It is the system power supply voltage. It is a voltage compensation item.

[0028] Output parameters of the delayed prediction model:

[0029] Predicted total delay time (Unit: ms)

[0030] The following describes the feedforward compensation strategy for hub motors, namely the hub motor PI parameter adaptive tuning mechanism, which can achieve precise matching between the compensation strength and the delay degree in the motor feedforward compensation algorithm.

[0031] The input parameters for the hub motor feedforward compensation strategy include the EMB target clamping force ( ), EMB actual clamping force ( The output of the EMB delay prediction model .

[0032] The compensation torque is calculated based on the input parameters.

[0033] ;

[0034] in, This indicates that the greater the delay, the smoother the compensation response; This indicates that the greater the delay, the slower the cumulative compensation speed. The actual clamping force of the EMB is fed back to the motor compensator every 5ms, and the motor compensator updates the compensation torque in real time based on the target / actual force deviation. .

[0035] The final synthesized hub motor feedforward compensation output torque (i.e., the synthesized torque command of the hub motor): .

[0036] S220 determines the battery state of charge (SOC). When SOC ≥ 95%, a four-wheel pure EMB vehicle stability control strategy will be adopted: yaw rate error will be assessed, and the deviation between the actual and expected values ​​will be calculated. ,in, It is the deviation between the actual value and the expected value. This is the actual value. This is the expected value. If... If the vehicle is deemed to be at risk of tail-swing, the following control strategy will be adopted: the inner rear wheel will be braked to generate a reverse yaw moment, which will quickly suppress the tail swing and restore the vehicle to a stable trajectory.

[0037] Calculate the required additional reverse yaw moment based on the yaw rate error:

[0038] ,in, Let be the moment of inertia of the vehicle about the z-axis;

[0039] The specific braking force distribution and control of the stability control strategy for four-wheeled pure EMB vehicles are as follows:

[0040] Sliding surface definition: Design the sliding variable s to track the yaw rate error:

[0041] ,in This is the convergence rate parameter, used to adjust the convergence speed.

[0042] Approach law design: Control s to approach 0 as quickly as possible while avoiding chattering.

[0043] Where k is a parameter that determines the approach velocity. It is a parameter for suppressing high-frequency jitter. Represents a symbolic function. It represents the first derivative of the sliding variable with respect to time.

[0044] Braking force calculation: reverse yaw moment Mapped as the increase in braking force of the inner rear wheel :

[0045] ,

[0046] in, For the tire radius, For the normal force of the rear wheel, This is the distance from the center of mass to the rear axle.

[0047] In addition, for the stability control strategy of four-wheel pure EMB vehicles, actuator constraints and safety protection mechanisms are set up:

[0048] Braking force limit: Ensure that the braking force of a single wheel does not exceed the maximum traction of the tire.

[0049] ,in This is the road surface friction coefficient.

[0050] If the calculated value exceeds this limit, it will be forced to... Set as the upper limit value.

[0051] Anti-lock braking system: Real-time monitoring of the slip ratio of the inner rear wheel ( If s > 0.2 (close to the lock-up threshold), pulse braking (duty cycle modulation) is triggered, causing the wheels to quickly switch between "brake release" and "brake release" to avoid complete lock-up. It's wheel speed. It's the vehicle speed.

[0052] Step S220 further includes: calculating the deviation between the actual value and the expected value. .like This is deemed a risk of fishtailing. The control strategy involves increasing lateral force on the rear of the vehicle by braking the outer rear wheel, inducing the rear to slide outward and compensating for understeer. This process may specifically include:

[0053] S220-1, Construction of the prediction model and optimization objective, including:

[0054] Discrete-time vehicle model:

[0055] ,

[0056] in, For state vectors, For longitudinal vehicle speed, The lateral speed is the speed of the vehicle. The yaw rate is angular velocity. The input vector is the control vector, and A and B are the state transition matrices. This increases the braking force on the right rear wheel. This increases the braking force on the left rear wheel.

[0057] Optimize the objective function: Minimize the deviation between the actual state and the desired state.

[0058] ,

[0059] Where N represents the prediction time domain, and Q and R are weight matrices, measuring state bias and control input cost, respectively. For the desired state, It is the index of the current time step. It is the step index within the prediction time domain.

[0060] S220-2, Embedding actuator physical constraints in optimization problems:

[0061] Braking force limit: , (Maximum braking force of front and rear wheels). The vertical load is on the right rear wheel. This represents the vertical load on the left rear wheel.

[0062] Front and rear braking force balance: limitations (a is the distance from the center of gravity to the front axle) to avoid excessive pitching of the vehicle body due to excessive braking force on the rear wheels.

[0063] S220-3, Rolling Optimization and Execution:

[0064] Real-time updates: Sensor data is updated every 10ms to recalculate the optimal braking force distribution. And output to the EMB actuator through the underlying controller.

[0065] Power coordination control: If the outer rear wheel brakes, the torque of the motor on the same side of the front wheel is simultaneously increased. ), to enhance steering thrust, of which, This is the torque gain coefficient. That is the reference torque.

[0066] The four-wheel pure EMB vehicle stability control algorithm also includes a unified safety mechanism and real-time monitoring, specifically including:

[0067] 1. Actuator constraint integration:

[0068] In solving the stability control algorithm for four-wheeled pure EMB vehicles, the following constraints are embedded through a quadratic programming (QP) optimizer:

[0069] Normal force Limited by suspension characteristics, Ensure rear axle load, where b is the distance from the center of gravity to the rear axle, and L is the wheelbase. It is the total weight of the vehicle. It is gravitational acceleration;

[0070] Yaw rate of change is limited to prevent excessively large sudden changes in torque that could lead to drastic changes in vehicle attitude. This limit specifically applies when the vehicle speed is below 80 km / h. When the vehicle speed is higher than 80km / h, , This represents the rate of change of yaw rate.

[0071] 2. Real-time security monitoring:

[0072] Sensor verification: Kalman filtering is used to fuse wheel speed sensor data to suppress noise; if a wheel speed signal changes abruptly (e.g., ... , (Rate of wheel speed change), marked as abnormal and switched to backup sensor.

[0073] Watchdog mechanism: Check the running status of the central domain controller (such as program counter, memory usage) every 1ms. If there is no response within a timeout period (>50ms), force the safe mode to be triggered, reduce the vehicle speed and drive in a straight line.

[0074] In summary, in the stability control strategy for four-wheel pure EMB vehicles, sliding mode control (SMC) is used to suppress drift and model predictive control (MPC) is used to correct understeer. This is mainly based on the differences in control requirements, algorithm characteristics matching, and system dynamic characteristics between the two operating conditions.

[0075] SMC designs a "sliding surface" ( This forces the system state to reach the sliding surface and converge along it within a finite time, exhibiting strong robustness to parameter disturbances and external disturbances (such as sudden changes in road friction coefficient). When a fishtail occurs, the vehicle is in a nonlinear critical stable state, requiring rapid suppression of the tail swing. The "reaching law" of SMC (such as...) It can ensure that the braking force command reaches a steady state in the shortest possible time, avoiding control failure caused by model uncertainties (such as tire nonlinearity).

[0076] MPC is based on vehicle dynamics models (such as discrete-time state-space equations). The optimal control sequence for the next N steps is solved through rolling optimization. At the same time, actuator constraints (such as the upper limit of braking force) are embedded. Understeer is essentially a multivariate coupling problem (the front wheel steering angle and the rear wheel braking force jointly affect the yaw rate), and it is also necessary to consider long-term trajectory tracking (such as avoiding overcorrection that leads to reverse fishtailing). MPC's multi-step prediction capability can plan the braking force distribution in advance, balancing "current stability" and "future trajectory accuracy"; at the same time, its constraint handling mechanism can avoid vehicle pitch caused by excessive rear wheel braking force.

[0077] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0078] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of explaining or limiting the subject matter of the invention.

Claims

1. A method for controlling vehicle stability using a composite braking system, characterized in that, include: Step S210: Determine the battery state SOC. When SOC < 95%, use the full-domain coordination mode of four sets of hub motors and four sets of EMB actuators. Step S220: Determine the battery state of charge (SOC). When SOC ≥ 95%, a four-wheel pure EMB vehicle stability control strategy will be adopted: determine the yaw rate error and calculate the deviation between the actual value and the expected value. ,in, It is the deviation between the actual value and the expected value. This is the actual value. It is the expected value, if If the vehicle is deemed to be at risk of tail-swing, the following control strategy will be adopted: the inner rear wheel will be braked to generate a reverse yaw moment, which will quickly suppress the tail swing and restore the vehicle to a stable trajectory.

2. The vehicle stability control method for a composite braking system according to claim 1, characterized in that, The four-hub motor and four-EMB actuator global coordination mode includes: Ideal yaw rate calculation: based on steering angle And vehicle speed, the ideal yaw rate is estimated by a simplified model. : Where L is the wheelbase. The longitudinal speed of the vehicle; Multi-objective optimization allocation: The optimization variable is the energy feedback torque of the four hub motors. Clamping force of four EMBs ; Optimization objective: Minimize the actual yaw rate Deviation from target value Minimize the absolute value of the sideslip angle ,in, Minimize the total energy consumption of the actuator ,in, For wheel indexing, It is the vehicle's lateral speed; Constraints: Motor torque shall not exceed speed-related limits. The EMB clamping force is within the physical limits. The combined force of the tires does not exceed the adhesion limit. ,in, It is the road surface friction coefficient for the i-th round. It is the normal force of the i-th round; Command Output: The Pareto optimal solution is obtained through the NSGA-II algorithm, and the torque commands for each motor are output. and EMB clamping force command .

3. The vehicle stability control method for the composite braking system according to claim 2, characterized in that, The NSGA-II algorithm has a control cycle of 20ms and performs 50 optimization iterations per cycle.

4. The vehicle stability control method for a composite braking system according to claim 2, characterized in that, The NSGA-II algorithm includes: population initialization, randomly generating 100 sets of torque allocation schemes; performing non-dominated sorting, calculating the three objective function values ​​of each scheme, and sorting them hierarchically; using a tournament to select the best 20 schemes to enter the next generation; simulating binary crossover with a crossover rate of 0.9 to generate new schemes; using polynomial mutation with a mutation rate of 0.1 to avoid local optima; and finally, after 50 iterations, selecting the optimal solution from the Pareto front for output.

5. The vehicle stability control method for a composite braking system according to claim 2, characterized in that, Step S210 includes a delayed prediction model: The input parameters for the delayed prediction model include: the target clamping force command value. The EMB actuator temperature (Temp), system power supply voltage (Voltage), and the base delay calculation formula are as follows: ,in, The clamping force influence coefficient is... As a reference delay; The delayed prediction model is obtained through calculation: Temperature compensation: Based on 25℃, temperature deviation increases response delay. The calculation formula is as follows: ,in, This is the temperature influence coefficient. It is the temperature of the EMB actuator. It is a temperature compensation item; Voltage compensation term: Based on 24V, a decrease in voltage causes an increase in response delay. The calculation formula is as follows: ,in, Here, is the voltage influence coefficient, where, It is the system power supply voltage. It is a voltage compensation item; Output parameters of the delayed prediction model: Predicted total delay time , Unit: ms.

6. The vehicle stability control method for a composite braking system according to claim 5, characterized in that, Step S210 includes the hub motor feedforward compensation strategy: The input parameters for the hub motor feedforward compensation strategy include the EMB target clamping force. EMB actual clamping force The output of the EMB delay prediction model ; The compensation torque is calculated based on the input parameters. ； in, This indicates that the greater the delay, the smoother the compensation response; This indicates that the greater the delay, the slower the cumulative compensation speed; The final synthesized hub motor feedforward compensation output torque, i.e., the synthesized torque command of the hub motor: .

7. The vehicle stability control method for a composite braking system according to claim 6, characterized in that, Step S220 includes: Calculate the required additional reverse yaw moment based on the yaw rate error: ,in, Let be the moment of inertia of the vehicle about the z-axis; The specific braking force distribution and control of the stability control strategy for four-wheeled pure EMB vehicles are as follows: Sliding surface definition: Design the sliding variable s to track the yaw rate error: ,in, This is the convergence rate parameter, used to adjust the convergence speed; Approach law design: Control s to approach 0 as quickly as possible while avoiding chattering. Where k is a parameter that determines the approach velocity. It is a parameter for suppressing high-frequency jitter. Represents a symbolic function. This represents the first derivative of the sliding variable with respect to time. Braking force calculation: reverse yaw moment Mapped as the increase in braking force of the inner rear wheel : ， in, For the tire radius, For the normal force of the rear wheel, This is the distance from the center of mass to the rear axle.

8. The vehicle stability control method for a composite braking system according to claim 7, characterized in that, Step S220 includes: For the stability control strategy of four-wheeled pure EMB vehicles, actuator constraints and safety protection mechanisms are set up: Braking force limit: Ensure that the braking force of a single wheel does not exceed the maximum traction of the tire. ,in, The coefficient of friction of the road surface; If the calculated value exceeds this limit, it will be forced to... Set as the upper limit; Anti-lock braking system: Real-time monitoring of the slip ratio of the inner rear wheel. If s > 0.2, pulse braking is triggered, causing the wheels to quickly switch between "brake release" and "brake engagement" to prevent complete lock-up. It's wheel speed. It's the vehicle speed.

9. The vehicle stability control method for a composite braking system according to claim 8, characterized in that, Step S220 further includes: calculating the deviation between the actual value and the expected value. ;like The risk of a fishtailing was identified. The control strategy was to increase the lateral force on the rear of the vehicle by braking the outer rear wheel, inducing the rear of the vehicle to slide outward and compensating for understeer. This process included: S220-1, Construction of the prediction model and optimization objective, including: Discrete-time vehicle model: ， in, For state vectors, For longitudinal vehicle speed, The lateral speed is the speed of the vehicle. The yaw rate is angular velocity. To control the input vector, A and B are state transition matrices. This increases the braking force on the right rear wheel. This increases the braking force on the left rear wheel; Optimize the objective function: Minimize the deviation between the actual state and the desired state. ， Where N represents the prediction time domain, and Q and R are weight matrices, measuring state bias and control input cost, respectively. For the desired state, It is the index of the current time step. It is the step index within the prediction time domain; S220-2, Embedding actuator physical constraints in optimization problems: Braking force limit: , , The vertical load is on the right rear wheel. The vertical load is on the left rear wheel; Front and rear braking force balance: limitations 'a' is the distance from the center of mass to the front axle; S220-3, Rolling Optimization and Execution: Real-time updates: Sensor data is updated every 10ms to recalculate the optimal braking force distribution. And output to the EMB actuator through the underlying controller; Power coordination control: If the outer rear wheel brakes, the torque of the motor on the same side of the front wheel is simultaneously increased. To enhance steering thrust, among which, This is the torque gain coefficient. That is the reference torque.

10. The vehicle stability control method for a composite braking system according to claim 9, characterized in that, The four-wheel pure EMB vehicle stability control algorithm also includes a unified safety mechanism and real-time monitoring, including: Actuator constraint integration: In the solution of the four-wheel pure EMB vehicle stability control algorithm, the following constraints are embedded through a quadratic programming optimizer: Normal force Limited by suspension characteristics, Ensure rear axle load, where b is the distance from the center of gravity to the rear axle, and L is the wheelbase. It is the total weight of the vehicle. It is gravitational acceleration; Limiting the rate of change of yaw rate of motion to avoid excessive torque abrupt changes that could lead to drastic changes in vehicle attitude; Real-time security monitoring: Sensor verification: Kalman filtering is used to fuse wheel speed sensor data to suppress noise; if a wheel speed signal changes abruptly, it is marked as abnormal and switched to a backup sensor; Watchdog mechanism: Check the running status of the central domain controller every 1ms. If there is no response within the timeout period, force the safe mode to be triggered, reduce the vehicle speed and drive in a straight line.

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