A method, apparatus, electronic device, and storage medium for vehicle control

Abstract

This invention discloses a vehicle control method, apparatus, electronic device, and storage medium, comprising: the vehicle having an intelligent driving system and a chassis stability control system; the method comprising: determining the vehicle's motion state parameters and stability coefficient during the process of controlling the vehicle's driving through the intelligent driving system; acquiring collected road surface features; adjusting the intervention threshold of the electronic stability control system of the chassis stability control system according to the road surface features; and generating a target driving trajectory according to the stability coefficient and the road surface features when the motion state parameters are less than the intervention threshold of the electronic stability control system. This achieves the adjustment of the intervention threshold of the chassis stability control system according to the road surface features. Before the chassis stability control system intervenes, a trajectory is generated based on the stability coefficient and road surface features to control the vehicle, optimizing the vehicle's driving state in a certain interval before the chassis stability control system is activated, effectively improving the safety of vehicle driving.

Description

Technical Field

[0001] This invention relates to the field of vehicle control, and more specifically to a method, apparatus, electronic device, and storage medium for vehicle control. Background Technology

[0002] With the rapid development of automotive technology, in-vehicle systems are employing an increasing number of methods to control vehicle movement. Among these, the chassis stability control system is a core in-vehicle system that detects and prevents skidding or loss of control in extreme situations such as emergency obstacle avoidance and slippery road surfaces, ensuring driver safety.

[0003] In related technologies, the chassis stability control system is located within the chassis brake controller and is an independent reactive control system. The chassis stability control system only intervenes to control the vehicle when it detects a deviation between the vehicle's actual motion parameters and the driver's expectations.

[0004] In current mainstream solutions, intelligent driving control primarily focuses on trajectory tracking and obstacle avoidance, lacking awareness and utilization of overall vehicle stability; while the chassis control system only passively intervenes to correct stability when the vehicle becomes unstable. This lack of coordination between the two leads to a disconnect between intelligent driving and chassis stability control. Once stability control is triggered, the intelligent driving system is often forced to disengage, and if the driver fails to take over in time, it can easily cause an accident. Summary of the Invention

[0005] In view of the above problems, a method, apparatus, electronic device, and storage medium for vehicle control are proposed to overcome or at least partially solve the above problems, including: A method for vehicle control, the vehicle having an intelligent driving system and a chassis stability control system, the method comprising: During the process of controlling the vehicle's movement through the intelligent driving system, the vehicle's motion state parameters and stability coefficient are determined; Acquire the collected road surface features; Based on the road surface characteristics, adjust the intervention threshold of the electronic stability control system of the chassis stability control system; When the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface characteristics.

[0006] Optionally, when the motion state parameter is less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface characteristics, including: When the motion state parameter is less than the intervention threshold of the electronic stability control system, multiple candidate driving trajectories are generated; Based on the stability coefficient and the road surface characteristics, predict the stability risk of each candidate driving trajectory; Based on the stability risk, the target driving trajectory is confirmed from the candidate driving trajectories.

[0007] Optionally, predicting the stability risk of each candidate driving trajectory based on the stability coefficient and the road surface characteristics includes: Obtain the vehicle model of the vehicle being collected; Based on the motion state parameters, the vehicle model, the stability coefficient, and the road surface features, the stability risk of each candidate driving trajectory is predicted.

[0008] Optionally, based on the stability risk, confirming the target driving trajectory from the candidate driving trajectories includes: Obtain the obstacle avoidance coefficient and ride comfort coefficient of the candidate driving trajectory; Based on the stability risk, the obstacle avoidance coefficient, and the ride comfort coefficient, the target driving trajectory is determined from the candidate driving trajectories.

[0009] Optionally, based on the road surface characteristics, the intervention threshold of the electronic stability control system of the chassis stability control system is adjusted, including: Obtain the first intervention threshold of the chassis stability control system; Based on the road surface characteristics, the first intervention threshold is adjusted to obtain the second intervention threshold of the chassis stability control system.

[0010] Optionally, when the motion state parameters exceed the intervention threshold of the electronic stability control system, the chassis stability control system switches to control the vehicle's driving, and the intelligent driving system coordinates and controls the vehicle's drive and steering.

[0011] Optionally, when the intelligent driving system exits control of vehicle driving, the electronic stability control system intervention threshold of the chassis stability control system is restored to the default intervention threshold.

[0012] A vehicle control device, the device comprising: The stability coefficient determination module is used to determine the motion state parameters and stability coefficient of the vehicle during the process of controlling the vehicle's driving through the intelligent driving system. The road surface feature acquisition module is used to acquire road surface features. An electronic stability control system intervention threshold adjustment module is used to adjust the electronic stability control system intervention threshold of the chassis stability control system according to the road surface characteristics. The trajectory generation module is used to generate a target driving trajectory based on the stability coefficient and the road surface characteristics when the motion state parameters are less than the intervention threshold of the electronic stability control system.

[0013] An electronic device includes a processor, a memory, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any of the preceding embodiments.

[0014] A readable storage medium, characterized in that a program or instructions are stored on the readable storage medium, and the processor executes the computer program to implement the method described in any of the preceding claims.

[0015] The embodiments of the present invention have the following advantages: In this embodiment of the invention, during the process of controlling vehicle driving through the intelligent driving system, the vehicle's motion state parameters and stability coefficient are determined; road surface features are acquired; based on the road surface features, the intervention threshold of the electronic stability control system of the chassis stability control system is adjusted; when the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface features. This achieves the adjustment of the intervention threshold of the chassis stability control system based on road surface features, and before the chassis stability control system intervenes, it also generates a target driving trajectory based on the stability coefficient and road surface features to control the vehicle, optimizing the vehicle's driving state in a certain range before the chassis stability control system is activated, effectively improving vehicle driving safety. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a vehicle control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the architecture of a vehicle control method provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating another vehicle control method provided in an embodiment of the present invention; Figure 4 This is a flowchart illustrating another vehicle control method provided in an embodiment of the present invention; Figure 5 This is a timing diagram of a vehicle control method provided in an embodiment of the present invention; Figure 6 This is a structural block diagram of a vehicle control device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Reference Figure 1 The diagram illustrates a flowchart of a vehicle control method according to an embodiment of the present invention, which may specifically include the following steps: Step 101: During the process of controlling the vehicle's driving through the intelligent driving system, determine the vehicle's motion state parameters and stability coefficient; In some examples, the intelligent driving system can be an intelligent driving system or an intelligent driving stability control system. An intelligent driving system is a vehicle control system used to control vehicle movement and is responsible for maintaining dynamic stability and preventing loss of control. Motion state parameters can be parameters characterizing the vehicle's movement during operation, and these parameters can include one or more of the following: sideslip angle, yaw rate, road surface adhesion coefficient, and longitudinal vehicle speed. The stability coefficient can be a coefficient characterizing the degree of urgency with respect to the vehicle's instability boundary at a certain moment during operation.

[0019] When the driver activates the intelligent driving system, the central control system can confirm that the vehicle is in intelligent driving mode and will begin to collect motion state parameters of the vehicle during driving. Then, based on the motion state parameters of the vehicle, the stability coefficient of the vehicle is calculated.

[0020] In practical applications, such as Figure 2 As shown, the control architecture for deploying the intelligent driving system in the intelligent driving controller consists of three functional modules: a stability estimation module, a preventive stability control module, and an adaptive stability control module. The stability estimation module calculates the parameters required for stability control and predicts the working boundary of chassis stability control.

[0021] The preventative stability control module, before the chassis stability control system is activated, proactively controls the chassis actuators by controlling single-wheel braking torque, steering angle, and yaw moment, taking into account stability during obstacle avoidance and fully releasing the actuators' potential, if a risky scenario is detected and the stability control boundary is reached. The adaptive stability control module adaptively adjusts the intervention threshold according to the scenario, intervening to stabilize the vehicle when the activation threshold is reached. If the chassis ESC (Electronic Stability Control) is activated, the adaptive stability control module and ESC of the intelligent driving system work together to control the vehicle.

[0022] During vehicle operation, once the intelligent driving system is activated, the central control system confirms that the vehicle is in intelligent driving mode. It then begins collecting raw sensor data, such as wheel speed from the brake controller, during braking and sends this data to the stability estimation module. The stability estimation module then outputs the vehicle motion state parameters and stability function operating boundaries required for stability control in real time. These motion state parameters may include one or more of the following: sideslip angle, yaw rate, road surface friction coefficient, and longitudinal vehicle speed. The sideslip angle is used to determine whether the vehicle deviates from the driver's desired trajectory, the yaw rate is used to determine understeer or oversteer, and the road surface friction coefficient determines the "maximum grip limit" that the road surface can provide. After the stability estimation module outputs the vehicle motion state parameters, the preventative stability control module calculates the vehicle's current stability coefficient based on these parameters.

[0023] Step 102: Obtain the collected road surface features.

[0024] In some examples, road surface features can be the road surface type of the road surface the vehicle is currently traveling on or the adhesion coefficient of the road surface the vehicle will subsequently travel on.

[0025] During vehicle operation, the system can collect sensor data from the wheels based on the vehicle's chassis estimation module and visual perception information from the vehicle's vision sensors based on the intelligent driving system. Based on the sensor data and visual perception information, the system can determine the current road surface type and, consequently, the coefficient of friction. Furthermore, based on the visual perception information, the intelligent driving system can further determine the road surface features the vehicle will subsequently encounter, and, based on these features, determine the coefficient of friction for that next surface.

[0026] In practical applications, historical data from wheel sensors can be collected based on the vehicle's chassis estimation module. Through wheel speed sensors and accelerometer sensors, wheel slip ratio, vertical acceleration, and vibration frequency can be obtained. Based on the visual history information collected from the vehicle's vision sensors by the intelligent driving system, the visual history information of the current driving road surface can be identified, including information such as color, texture, markings, and puddles. Based on historical sensor data such as wheel slip rate, vertical acceleration, and vibration frequency, and visual historical information such as road surface color, texture, markings, and puddles, a model is used to extract features from the sensor historical data and visual historical information. The extracted sensor features and visual features are then input into a neural network (NN), and the neural network model learns the relationship between the two.

[0027] While the vehicle is in motion, the system can collect sensor data from the wheels based on the vehicle's chassis estimation module and visual perception information from the vehicle's visual sensors based on the intelligent driving system. A trained neural model is then used to determine the road surface characteristics the vehicle is currently traveling on, identifying different road types such as dry asphalt, wet roads, and icy / snowy roads. Based on the determined road type, the coefficient of friction can be determined.

[0028] Furthermore, road surface features can also be the road surface type of the road surface the vehicle is currently driving on or the road surface condition of the road surface the vehicle will drive on in the future. Based on the visual perception information collected by the intelligent driving system from the vehicle's visual sensors, it can also be determined whether the current or subsequent road surface features are obstacle scene features. Based on the determined road surface features, the adhesion coefficient of the road surface can be determined.

[0029] Step 103: Adjust the intervention threshold of the electronic stability control system of the chassis stability control system according to the road surface characteristics. In some examples, the chassis stability control system is a control system within the vehicle's braking system that maintains vehicle posture and trajectory stability through subsystems such as braking, steering, and suspension. The electronic stability control system intervention threshold is the threshold at which the chassis stability control system intervenes to control the vehicle when it detects that the vehicle's motion parameters have become uncontrollable.

[0030] When acquiring the road surface features that the vehicle is currently traveling on, and determining the adhesion coefficient corresponding to the road surface features, the intelligent driving system can adjust the intervention threshold of the chassis stability control system to control the vehicle based on different adhesion coefficients.

[0031] In some embodiments of the present invention, adjusting the intervention threshold of the electronic stability control system of the chassis stability control system according to the road surface characteristics includes: Sub-step 11: Obtain the first intervention threshold of the chassis stability control system.

[0032] In some examples, the first intervention threshold is the initial intervention threshold at which the chassis stability control system intervenes to control the vehicle.

[0033] During the process of controlling the vehicle's movement, the intelligent driving system will continuously collect the initial intervention threshold of the chassis stability control system to control the vehicle.

[0034] Sub-step 12: Based on the road surface characteristics, adjust the first intervention threshold to obtain the second intervention threshold of the chassis stability control system.

[0035] After acquiring the collected road surface features, different road surface features are judged to determine the adhesion coefficient of the road surface. Based on the adhesion coefficient, it can be determined whether the road surface belongs to a high-adhesion road surface or a low-adhesion road surface. During the adjustment of the default intervention threshold, when the adhesion coefficient is a high-adhesion road surface, the first intervention threshold (default intervention threshold) will be widened, allowing the chassis stability control system to intervene in vehicle control later when the vehicle loses control. Conversely, when the road surface features are low-adhesion road surfaces, the first intervention threshold will be narrowed, requiring the chassis stability control system to intervene in vehicle control earlier.

[0036] In practical applications, after acquiring the collected road surface features, different road surface features are judged. When the road surface feature (type) is a dry, rough asphalt road or a dry, intact cement road, it can be determined that the road surface is a high adhesion road with a high adhesion coefficient. If the road surface feature is a road surface after rain, ice and snow, or sand, it can be determined that the road surface is a low adhesion road with a low adhesion coefficient.

[0037] When the road surface is characterized by high grip, the tires have strong traction, allowing for significant sideslip or yaw before intervention. Therefore, the first intervention threshold (default intervention threshold) is widened to obtain the second intervention threshold. This allows the chassis stability control system to intervene later when the vehicle loses control, reducing unnecessary intervention and allowing ESC (Electronic Stability Control) to intervene later, maintaining driving smoothness. Conversely, when the road surface is characterized by low grip, the tires have weak traction and are prone to instability. Therefore, the first intervention threshold is narrowed to obtain the second intervention threshold, requiring the chassis stability control system to intervene earlier, allowing ESC to intervene earlier to prevent loss of control.

[0038] Step 104: When the motion state parameter is less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface characteristics.

[0039] During vehicle operation, the central control system will monitor the vehicle's motion state parameters in real time. When the vehicle's motion state parameters are less than the threshold for intervention of the electronic stability control system, the intelligent driving system can generate a target driving trajectory based on the stability coefficient and road surface characteristics, and control the vehicle's driving through the trajectory.

[0040] In practical applications, motion state parameters, including sideslip angle, yaw rate, and longitudinal speed, can be acquired to characterize vehicle motion. If any of these parameters deviates significantly or exceeds a set threshold, the vehicle's motion state parameter is considered to be above the intervention threshold of the electronic stability control system. Conversely, if the parameter is below the threshold, the motion state parameter is considered to be below the intervention threshold. Furthermore, the real-time yaw rate measured by the vehicle's yaw rate sensor can be compared with the target yaw rate calculated in real-time based on the steering wheel angle and vehicle speed. A significant deviation between the real-time and target yaw rates indicates that the vehicle's motion state parameter exceeds the intervention threshold of the electronic stability control system.

[0041] In some examples of the present invention, when the motion state parameter is less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface characteristics, including: Sub-step 21: In response to the motion state parameters being less than the intervention threshold of the electronic stability control system, multiple candidate driving trajectories are generated. In some examples, multiple candidate driving trajectories can be generated by the intelligent driving system based on the acquired user intent and environmental information around the vehicle, resulting in multiple predicted trajectories.

[0042] During vehicle operation, the chassis system can acquire the current vehicle motion state parameters. The central control system then determines whether the current vehicle motion state parameters are less than the intervention threshold of the electronic stability control system. When the motion state parameters are less than the intervention threshold of the electronic stability control system, the system will control the planner to generate many possible future candidate driving trajectories based on the acquired user intent and the surrounding environmental information of the vehicle.

[0043] During vehicle operation, the system uses the vehicle's motion state parameters obtained through the chassis system to determine whether the parameters, such as the center of gravity sideslip angle and yaw rate, are below the intervention threshold of the electronic stability control system. When the motion state parameters are below the intervention threshold, the system uses the visual perception information collected by the vehicle's visual sensors and the user's driving intentions perceived by the vehicle's cockpit to generate multiple candidate driving trajectories.

[0044] By collecting visual perception information from the vehicle's visual sensors through the intelligent driving system, the system can obtain visual information about the vehicle's surroundings, identify lane lines, obstacles, road boundaries, drivable areas, and other visual perception information. By using the visual sensors in the vehicle's cockpit, the sensors on the steering wheel, and the vehicle's motion state parameters, and utilizing a pre-trained Large Language Model (LLM), the system can predict the driving intention and confirm that the vehicle will perform driving intentions such as maintaining lane direction, changing lanes, avoiding obstacles, and following other vehicles.

[0045] Finally, based on visual perception information and driving intention, the system's planner will generate multiple candidate driving trajectories for the future. For example, if the vehicle is currently driving straight and the vehicle in front slows down, causing an obstacle to be crossed, multiple driving schemes will be generated, such as changing lanes to the left or right or slowing down to follow the vehicle ahead. Based on these driving schemes, multiple candidate driving trajectories can be generated.

[0046] Sub-step 22: Based on the stability coefficient and road surface characteristics, predict the stability risk of each candidate driving trajectory.

[0047] After predicting multiple candidate driving trajectories, the intelligent driving system will predict the stability risk of each candidate driving trajectory based on the vehicle's current stability coefficient and road surface characteristics.

[0048] In some embodiments of the present invention, predicting the stability risk of each candidate driving trajectory based on the stability coefficient and the road surface characteristics includes: Sub-step 211: Obtain the vehicle model of the vehicle being collected.

[0049] In some examples, the vehicle model can be a mathematical model of the vehicle derived from prior dynamic simulation.

[0050] During vehicle operation, the intelligent driving system will continuously acquire vehicle motion state parameters such as center of gravity sideslip angle, yaw rate, road surface slip coefficient, and longitudinal speed, while also acquiring the vehicle mathematical model derived from pre-simulated dynamics.

[0051] Sub-step 212: Based on the motion state parameters, the vehicle model, the stability coefficient, and the road surface features, predict the stability risk of each candidate driving trajectory.

[0052] After acquiring the current vehicle's motion state parameters, vehicle model, stability coefficient, and road surface features, the intelligent driving system will further predict the stability risk of each candidate driving trajectory.

[0053] In practical applications, the current vehicle motion state parameters include: center of gravity sideslip angle. β0， yaw rate γ 0, longitudinal speed v x0; Road surface adhesion coefficient μ Candidate Driving Trajectory: Future N Step control input, each step u k =[δ f,k , a x,k ] (Front wheel steering angle, longitudinal acceleration); Time step Δt Predicting steps N=3s / Δt It can be derived based on the monorail model (vehicle model): For k = 0,1,2,..., N 1 From the current state ( β k , γ k , v xk ) and control u k Calculate the state at the next time step: Front and rear tire slip angles:

[0054]

[0055] (a and b are the distances from the center of mass to the front and rear axes) Lateral forces of front and rear tires:

[0056]

[0057] ( , For the lateral stiffness, L = a + b, where clip indicates confinement within the friction circle. The derivatives of yaw acceleration and sideslip angle of the center of mass:

[0058]

[0059] Status Update: Yaw angular velocity:

[0060] Side slip angle of the center of mass:

[0061] Longitudinal speed:

[0062] Calculate the instantaneous stability risk coefficient K of the candidate driving trajectory at this moment:

[0063] in: The stability boundary is obtained by looking up the table for the current μ and vehicle speed (the higher the vehicle speed and the lower μ, the smaller the boundary). If the calculated value is greater than 1.0, then take 1.0 (indicating that the prediction has become unstable).

[0064] Sub-step 23: Based on the stability risk, confirm the target driving trajectory from the candidate driving trajectories.

[0065] After calculating the stability risk of each candidate driving trajectory, the system can select one trajectory from multiple candidate driving trajectories as the target driving trajectory based on the stability risk.

[0066] In some embodiments of the present invention, determining a target driving trajectory from the candidate driving trajectories based on the stability risk includes: Sub-step 231: Obtain the obstacle avoidance coefficient and ride comfort coefficient of the candidate driving trajectory; In some examples, the obstacle avoidance coefficient can be a prediction of the spatial deviation or collision risk between the candidate driving trajectory and existing obstacles, road boundaries, or reference lines in the future. The ride comfort coefficient can be calculated based on the vehicle's motion parameters during driving on a candidate driving trajectory, representing the comfort level of the vibration environment experienced by the driver or passengers, or the vehicle's ability to protect the integrity of transported goods.

[0067] After the system acquires stability risks, it calculates multiple candidate driving trajectories. Based on each candidate driving trajectory, it calculates the collision risk or geometric relationship between the candidate driving trajectory and the current road obstacle or the current road reference line by collecting visual perception information and vehicle motion state parameters from the intelligent driving system, and obtains the obstacle avoidance coefficient of this candidate driving trajectory.

[0068] The intelligent driving system calculates the vehicle's motion state parameters when it needs to reach the predicted candidate driving trajectory based on the current vehicle's progress. Based on the motion state parameters, the smoothness coefficient of the driving process of this candidate driving trajectory is determined.

[0069] Sub-step 232: Based on the stability risk, the obstacle avoidance coefficient and the ride comfort coefficient, determine the target driving trajectory from the candidate driving trajectories.

[0070] The intelligent driving system first quantifies the stability risk into a stability cost coefficient, and then determines the target driving trajectory from the candidate driving trajectories based on the stability cost coefficient, the obstacle avoidance coefficient, and the ride comfort coefficient.

[0071] When the target driving trajectory is determined from the candidate driving trajectories, the intelligent driving system first quantifies the stability risk into a stability cost coefficient. Then, based on the stability cost coefficient, the obstacle avoidance coefficient, and the ride comfort coefficient, the scores of the three coefficients are added together, and the candidate driving trajectory with the smallest score is taken as the target driving trajectory.

[0072] In some embodiments of the present invention, a target driving trajectory is determined from the candidate driving trajectories based on stability risk, the obstacle avoidance coefficient, and the ride comfort coefficient, including: In response to a stability risk exceeding a preset stability risk warning threshold, the stability risk is increased.

[0073] In some examples, the stability risk warning line can characterize the stability safety boundary of the current vehicle traveling on the predicted candidate trajectory.

[0074] After the intelligent driving system is activated, the system can set a stability risk warning line based on default or preset values. When the system predicts that the stability risk of a candidate driving trajectory will be greater than the set stability risk warning line during the calculation of the candidate driving trajectory, the system will exponentially increase the stability risk of the trajectory. This will amplify the stability cost coefficient when the stability risk is quantified, making the final score of the trajectory higher than that of other candidate driving trajectories.

[0075] In practical applications, after activating the intelligent driving system, the system can set a stability intervention threshold based on default or preset values. During the calculation of candidate driving trajectories, a score is obtained by combining the obstacle avoidance coefficient, ride comfort coefficient, and stability cost coefficient. If the system predicts that the stability risk of this candidate driving trajectory at some future moment exceeds the stability intervention threshold or exceeds the warning line of the stability intervention threshold (slightly below the stability intervention threshold), the system will exponentially increase the stability coefficient of this candidate driving trajectory, thereby increasing the stability cost coefficient corresponding to the stability coefficient. Finally, based on the scores of the candidate driving trajectories, the candidate driving trajectory with the lowest score is selected as the target trajectory.

[0076] Selecting the trajectory with the lowest score is equivalent to choosing the target driving trajectory with the lowest total cost. Based on the target driving trajectory, the first control action can be output. This first action (e.g., "turn the steering wheel 2 degrees and apply slight braking") is then sent to the vehicle actuator, controlling the vehicle to execute the action. As long as no exceedance of the set threshold occurs, the selected target driving trajectory continues to be executed.

[0077] In some embodiments of the present invention, after determining the target driving trajectory from the candidate driving trajectories based on stability risk, the obstacle avoidance coefficient, and the ride comfort coefficient, the method further includes: When the stability coefficient is less than a preset stability threshold, the vehicle is controlled by adjusting the steering angle, total braking torque, and drive torque. When the stability coefficient is greater than a preset stability threshold, the vehicle is controlled by adjusting the steering angle and single-wheel braking torque.

[0078] In some examples, the stability threshold can be a dynamic threshold used in the intelligent driving system to trigger the active stability control mode; the stability threshold can be a default value set after the intelligent driving system is started, or it can be dynamically generated in real time based on the current road surface slip adhesion coefficient through a preset mapping relationship.

[0079] In practical applications, after the intelligent driving system is activated, it obtains the road surface adhesion coefficient and adjusts the stability threshold accordingly. When the adhesion coefficient is high, the stability threshold can be widened, and vice versa. When the adhesion coefficient is low, the stability threshold can be narrowed.

[0080] When the stability coefficient is less than the preset stability threshold, there is no need for advance stabilization control. The vehicle is mainly controlled in the lateral and longitudinal directions by means of steering angle, total braking torque, and driving torque. In case of emergency, the intelligent driving system can also issue a yaw torque (Mz) request to enable the vehicle to quickly reach the target state.

[0081] When the stability coefficient is greater than a preset stability threshold, the vehicle is controlled in coordination by steering angle, single-wheel braking torque, yaw torque request and drive torque, wherein the single-wheel braking torque is used to precisely adjust the slip ratio of each wheel to maintain the expected controllable slip state.

[0082] In some embodiments of the present invention, when the motion state parameters are greater than the intervention threshold of the electronic stability control system, the chassis stability control system switches to control the vehicle's driving, and the intelligent driving system coordinates and controls the vehicle's driving and steering.

[0083] When the central control system detects in real time that the motion state parameters are greater than the intervention threshold of the electronic stability control system, the intelligent driving system switches the vehicle control to the chassis stability control system. The intelligent driving system can still coordinate and control the vehicle's drive and steering.

[0084] In practical applications, when the motion parameters exceed the intervention threshold of the electronic stability control system (ESC), the chassis stability control system will intervene, activating the chassis ESC (Electronic Stability Control). After ESC activation, the intelligent driving system does not need to disengage. The overall control strategy learns from experienced drivers' stable vehicle control experience when ESC is activated, and preventative stability control stops outputting single-wheel braking torque or yaw moment (Mz) control. The adaptive stability control module coordinates the control of drive, steering, etc., achieving collaborative control with ESC under this condition. During this stage, vehicle stability control is primarily based on ESC, with intelligent driving assisting ESC with steering angle control for stability control, ensuring the continuity of intelligent driving functions.

[0085] In some embodiments of the present invention, when the intelligent driving system exits control of vehicle driving, the intervention threshold of the electronic stability control system of the chassis stability control system is restored to the default intervention threshold.

[0086] When the driver does not need to use the intelligent system to control the vehicle, after selecting the intelligent driving system to exit vehicle driving control, the intervention threshold of the electronic stability control system of the chassis stability control system, which is adjusted according to road characteristics, will be restored to the default intervention threshold.

[0087] The embodiments of the present invention have the following advantages: In this embodiment of the invention, during the process of controlling vehicle driving through the intelligent driving system, the vehicle's motion state parameters and stability coefficient are determined; road surface features are acquired; based on the road surface features, the intervention threshold of the electronic stability control system of the chassis stability control system is adjusted; when the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface features. This achieves the adjustment of the intervention threshold of the chassis stability control system based on road surface features, and before the chassis stability control system intervenes, it also generates a target driving trajectory based on the stability coefficient and road surface features to control the vehicle, optimizing the vehicle's driving state in a certain range before the chassis stability control system is activated, effectively improving vehicle driving safety.

[0088] Reference Figure 3 The diagram illustrates a flowchart of a vehicle control method according to an embodiment of the present invention, which may specifically include the following steps: Step 301: During the process of controlling the vehicle's driving through the intelligent driving system, determine the vehicle's motion state parameters and stability coefficient.

[0089] When the intelligent driving system is activated while the vehicle is in motion, and the vehicle is in an intelligent driving state controlled by the intelligent driving system, the intelligent driving system will continuously acquire the collected motion state parameters of the vehicle, and determine the stability coefficient of the vehicle at a certain moment from the vehicle instability boundary based on the motion state parameters.

[0090] Step 302: Obtain the collected road surface features.

[0091] The vehicle's chassis estimation module provides sensor data from the wheels during the vehicle's current driving process, along with visual perception information collected by the vehicle's visual sensors from the intelligent driving system. Based on the sensor data and visual perception information, the characteristics or type of the road surface the vehicle is currently traveling on can be determined, and the adhesion coefficient of the current road surface can be determined accordingly.

[0092] Step 303: Obtain the first intervention threshold of the chassis stability control system.

[0093] In some examples, the first intervention threshold is the initial intervention threshold at which the chassis stability control system intervenes to control the vehicle.

[0094] During the process of controlling the vehicle's movement, the intelligent driving system will continuously collect the initial intervention threshold of the chassis stability control system to control the vehicle.

[0095] Step 304: Based on the road surface characteristics, adjust the first intervention threshold to obtain the second intervention threshold of the chassis stability control system.

[0096] After acquiring the collected road surface features, different road surface features are judged to determine whether the road surface feature belongs to a high-adhesion road surface or a low-adhesion road surface. During the adjustment of the default intervention threshold, when the road surface feature is a high-adhesion road surface, the first intervention threshold (default intervention threshold) will be widened, allowing the chassis stability control system to intervene in vehicle control later when the vehicle loses control. Conversely, when the road surface feature is a low-adhesion road surface, the first intervention threshold will be narrowed, requiring the chassis stability control system to intervene in vehicle control earlier.

[0097] Step 305: In response to the motion state parameters being less than the intervention threshold of the electronic stability control system, multiple candidate driving trajectories are generated.

[0098] During vehicle operation, when the motion state parameters are less than the threshold for intervention of the electronic stability control system, the system control planner generates many possible future candidate driving trajectories based on the user's intent and the environmental information around the vehicle.

[0099] Step 306: Obtain the vehicle model of the vehicle being collected.

[0100] During vehicle operation, the intelligent driving system will continuously acquire vehicle motion state parameters such as center of gravity sideslip angle, yaw rate, road surface slip coefficient, and longitudinal speed, while also acquiring the vehicle mathematical model derived from pre-simulated dynamics.

[0101] Step 307: Based on the motion state parameters, the vehicle model, the stability coefficient, and the road surface features, predict the stability risk of each candidate driving trajectory.

[0102] After acquiring the current vehicle's motion state parameters, vehicle model, stability coefficient, and road surface data, the intelligent driving system will further predict the stability risk of each candidate driving trajectory.

[0103] Step 308: Based on the stability risk, confirm the target driving trajectory from the candidate driving trajectories.

[0104] After calculating the stability risk of each candidate driving trajectory, the system can select one trajectory from multiple candidate driving trajectories as the target driving trajectory based on the stability risk.

[0105] The embodiments of the present invention have the following advantages: In this embodiment of the invention, during the process of controlling vehicle driving through the intelligent driving system, the vehicle's motion state parameters and stability coefficient are determined; road surface features are acquired; the intervention threshold of the electronic stability control system of the chassis stability control system is adjusted according to the road surface features; when the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface features. This achieves the adjustment of the intervention threshold of the chassis stability control system based on road surface features. Before the chassis stability control system intervenes, a target driving trajectory is generated based on the stability coefficient and road surface features to control the vehicle. This optimizes the vehicle's driving state within a certain range before the chassis stability control system is activated. By integrating intelligent driving and chassis information for real-time stability estimation, it overcomes the limitations of fixed experience constraints in intelligent driving control and the perception dimensions of chassis sensors, effectively improving vehicle driving safety.

[0106] Reference Figure 4 The following is combined Figure 4 The embodiments of the present invention are described by way of example: 401. The system will determine whether the vehicle has entered intelligent driving stability control during the current driving process, that is, whether the current vehicle is in the intelligent driving state controlled by the intelligent driving system.

[0107] 402. When the vehicle is in intelligent driving mode, the system will set the default base threshold (stability threshold) for intelligent driving and the default threshold for ESC intervention to the base value (electronic stability control system intervention threshold).

[0108] 403. The chassis estimation module provides sensor data of the wheels during the current driving process of the vehicle and visual perception information collected by the intelligent driving system from the vehicle's visual sensors. Based on the sensor data and visual perception information, the type of road surface in the current driving scene of the vehicle can be perceived and determined.

[0109] 404. After acquiring the road surface type (road surface feature), different road surface types are judged. When the road surface feature is a high-adhesion road surface, the first threshold of ESC intervention (the default threshold of ESC intervention) will be widened to obtain the second threshold of ESC intervention, allowing higher vehicle body dynamics to not be intervened.

[0110] 405. When the road surface is characterized as a low-adhesion road surface, the first threshold for ESC intervention (the default threshold for ESC intervention) will be narrowed to obtain the second threshold for ESC intervention, allowing ESC to intervene in control earlier.

[0111] 406. The preventive stability control module in the intelligent driving system can integrate the vehicle motion state parameters output by the road surface type and stability estimation module to obtain forward-looking candidate driving trajectory stability parameters as constraints for the candidate driving trajectory.

[0112] 407. Select the target driving trajectory with the minimum cost based on the constraints of the candidate driving trajectories. Then, output the first control action based on the target driving trajectory, and perform vehicle stabilization control in advance through single-wheel braking torque, etc.

[0113] 408. The system will continuously monitor the vehicle status in real time to determine whether the vehicle parameters have reached the adjusted second threshold for ESC intervention.

[0114] 409. When the vehicle parameters reach the adjusted second threshold for ESC intervention, ESC intervention activates the vehicle's chassis stability control system. However, the intelligent driving system function does not need to be disengaged at this time. The preventive stability control stops outputting single-wheel braking torque or yaw torque (Mz) control. The adaptive stability control module coordinates and controls the drive, steering, etc., to achieve collaborative control with ESC under this condition. During this stage, the vehicle stability control is mainly based on ESC, and the intelligent driving system assists ESC in stability control with steering angle control.

[0115] 410. When the driver does not need to use the intelligent system to control the vehicle, after selecting the intelligent driving system to exit the vehicle driving control, the intervention threshold of the electronic stability control system of the chassis stability control system, which is adjusted according to the road characteristics, will be restored to the default intervention threshold.

[0116] In this embodiment of the invention, during the process of controlling vehicle driving through the intelligent driving system, the vehicle's motion state parameters and stability coefficient are determined; road surface features are acquired; based on the road surface features, the intervention threshold of the electronic stability control system of the chassis stability control system is adjusted; when the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface features. This achieves the adjustment of the intervention threshold of the chassis stability control system based on road surface features, and before the chassis stability control system intervenes, a target driving trajectory is generated based on the stability coefficient and road surface features to control the vehicle, optimizing the vehicle's driving state in a certain range before the chassis stability control system is activated, effectively improving vehicle driving safety.

[0117] Reference Figure 5 This shows a timing diagram of a vehicle control method provided in an embodiment of the present invention, combined with... Figure 5 The embodiments of the present invention are described by way of example: In current mainstream solutions, intelligent driving control and chassis stability control are disconnected. When the intelligent driving stability function is activated, the intelligent driving function is deactivated, and the driver may not be able to take over in time, leading to accidents. In addition, the chassis stability control system can only passively correct the situation when vehicle instability has already occurred or is about to occur, and its control has an inherent lag. At the same time, its performance under extreme conditions is insufficient. Under extremely complex conditions (such as emergency obstacle avoidance on low-adhesion roads and the "moose test"), the vehicle dynamics are close to the physical limits. Traditional ESC, due to the lack of forward-looking information and global planning, often intervenes drastically and conservatively, and may not be able to achieve globally optimal stability control.

[0118] When the intelligent driving system is activated while the vehicle is in motion, and the vehicle is in an intelligent driving state where the intelligent driving system controls the vehicle's movement, the intelligent driving function is activated. Assuming the vehicle is traveling at 120 km / h on a dry asphalt road, if Target 1 suddenly slows down in the lane ahead, the vehicle needs to quickly steer to avoid the obstacle.

[0119] In the S1 stage, the vehicle's chassis estimation module provides sensor data of the wheels during the current driving process and visual perception information collected by the vehicle's visual sensors by the intelligent driving system. Based on the sensor data and visual perception information, the type of road surface in the current driving scene can be perceived and determined. It is determined that the current scene is a high-attachment obstacle avoidance scenario, and requests to widen the first threshold of ESC intervention (the default threshold of ESC intervention) to obtain the second threshold of ESC intervention. This allows higher vehicle body dynamics to not be intervened, leaving more control space for intelligent driving control. The S1 stage is further divided into two stages: S1-1 is the initial steady-state stage. In this stage, the intelligent driving system's preventive stability control detects that the vehicle's motion state is less than the set threshold. The intelligent driving system combines the stability control boundary and requests torque reduction, applies total braking torque and Mz within the instability boundary range to quickly achieve deceleration and steering. S1-2 represents the imminent instability phase. During this phase, the intelligent driving system's preventative stability control detects that the vehicle's stability coefficient exceeds a set threshold. It then performs pre-stability control through single-wheel braking and steering, considering stability while avoiding obstacles and controlling the slip ratio of each wheel within the optimal range as much as possible. Traditional solutions, because intelligent driving cannot perceive real-time stability boundaries, use overly conservative boundaries set based on empirical values. According to the friction circle theory, longitudinal and lateral forces are mutually constrained, and the resultant force cannot exceed a "friction circle" with the vertical load as its radius. If it does, the tires will slip or become unstable. Therefore, the system generally first reduces torque and brakes to slow down the vehicle. After the vehicle speed has decreased to a certain range, it then steers to avoid obstacles. The entire process is not smooth, and due to the late steering, obstacle avoidance may not be completed. In the S2 stage, multiple actuators are pre-controlled by the intelligent driving system (S1) to provide stability control. This reduces the probability of VDC activation compared to the traditional solution, which has a larger margin for instability adjustment and VDC threshold adjustment. In the traditional solution, during the S2 stage, the vehicle exhibits significant dynamics, the ESC function is activated, and the intelligent driving function is deactivated. Before human intervention, the vehicle is in a free and uncontrollable state, which greatly increases the risk of accidents. Furthermore, traditional solutions do not adjust the ESC intervention threshold. If the ESC threshold is not widened in stage S1, the following problems exist: ① Using yaw scenarios in S1-1 may cause unexpected ESC activation, resulting in poor system smoothness; ② In stage S2-2, traditional ESC solutions do not have a global view and the activation threshold is conservative. When the slip exceeds the set threshold, the braking system enters the nonlinear region, and ESC activation pulls the vehicle back to the stable region (linear region); Stage S3 occurs when the vehicle cannot be stabilized in the aforementioned stages. In this stage, ESC is activated. This invention adopts a strategy of not disengaging the intelligent driving function. Intelligent driving uses the experience of experienced drivers to control the vehicle. For example, when oversteering occurs, the steering wheel is counter-steering; when understeering occurs, the steering wheel is turned forward. The intelligent driving control layer uses vehicle dynamic stability parameters such as vehicle stability emergency coefficient and slip rate to adjust the steering wheel control rate and angle in real time to achieve coordinated vehicle control with ESC.

[0120] Through the above strategies, this intelligent driving solution can accurately control the slip ratio, make full use of the nonlinear region, and complete obstacle avoidance while ensuring vehicle stability. This can improve the obstacle avoidance performance of the system. Furthermore, the trajectory 2 obtained by the solution based on this invention is smoother and has a higher obstacle avoidance capability than the trajectory 1 obtained by the traditional solution. It can fully realize the adaptive adjustment of the stability intervention threshold, break through the traditional fixed threshold, and dynamically adjust the trigger threshold according to scenarios such as low adhesion and emergency obstacle avoidance, so as to realize the "on-demand intervention" of chassis stability control and avoid intervention that is too late or too frequent.

[0121] In this embodiment of the invention, during the process of controlling vehicle driving through the intelligent driving system, a stability coefficient is determined; road surface features are acquired; the intervention threshold of the electronic stability control system of the chassis stability control system is adjusted according to the road surface features; when the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface features. This achieves the adjustment of the intervention threshold of the chassis stability control system based on road surface features, and before the chassis stability control system intervenes, a target driving trajectory is generated based on the stability coefficient and road surface features to control the vehicle, optimizing the vehicle's driving state in a certain range before the chassis stability control system is activated, effectively improving vehicle driving safety.

[0122] Reference Figure 6 The diagram shows a structural schematic of a vehicle interaction device according to an embodiment of the present invention, which may specifically include the following modules: The vehicle coefficient determination module 601 is used to determine the motion state parameters and stability coefficient of the vehicle during the process of controlling the vehicle's driving through the intelligent driving system.

[0123] The road surface feature acquisition module 602 is used to acquire road surface features.

[0124] The electronic stability control intervention threshold adjustment module 603 is used to adjust the electronic stability control system intervention threshold of the chassis stability control system according to the road surface characteristics.

[0125] The trajectory generation module 604 is used to generate a target driving trajectory based on the stability coefficient and the road surface characteristics when the motion state parameters are less than the intervention threshold of the electronic stability control system.

[0126] In some embodiments of the present invention, the trajectory generation module includes: The candidate driving trajectory generation submodule is used to generate multiple candidate driving trajectories in response to motion state parameters being less than the intervention threshold of the electronic stability control system.

[0127] The stability risk prediction submodule is used to predict the stability risk of each candidate driving trajectory based on the stability coefficient and the road surface characteristics.

[0128] The target driving trajectory confirmation submodule is used to confirm the target driving trajectory from the candidate driving trajectories based on the stability risk.

[0129] In some embodiments of the present invention, the stability risk prediction submodule includes: The vehicle parameter and model acquisition unit is used to acquire the vehicle model of the vehicle.

[0130] The stability risk prediction unit is used to predict the stability risk of each candidate driving trajectory based on the motion state parameters, the vehicle model, the stability coefficient, and the road surface features.

[0131] In some embodiments of the present invention, the target driving trajectory confirmation submodule includes: The coefficient acquisition unit is used to acquire the obstacle avoidance coefficient and ride comfort coefficient of the candidate driving trajectory.

[0132] The target driving trajectory acquisition and determination unit is used to determine the target driving trajectory from the candidate driving trajectories based on the stability risk, the obstacle avoidance coefficient and the ride comfort coefficient.

[0133] In some embodiments of the present invention, the electronic stability control system intervenes in the threshold adjustment module, including: The first intervention threshold acquisition unit is used to acquire the first intervention threshold of the chassis stability control system.

[0134] The second intervention threshold confirmation unit adjusts the first intervention threshold according to the road surface characteristics to obtain the second intervention threshold of the chassis stability control system.

[0135] In some embodiments of the present invention, the apparatus further includes: The chassis stability control system switching module is used to switch to the chassis stability control system to control the vehicle's driving when the motion state parameter is greater than the intervention threshold of the electronic stability control system, and the intelligent driving system coordinates and controls the vehicle's driving and steering.

[0136] In some embodiments of the present invention, the transpose further includes: The recovery threshold module is used to restore the electronic stability control system intervention threshold of the chassis stability control system to the default intervention threshold when the intelligent driving system exits vehicle driving control.

[0137] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0138] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0139] Some embodiments of the present invention also provide a vehicle including a processor, a memory, and a computer program stored in the computer memory and capable of running on the processor, wherein the method described above is determined to be implemented when the computer program is executed by the processor.

[0140] Some embodiments of the present invention also provide an electronic device that may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described above.

[0141] Some embodiments of the present invention also provide a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and wherein the computer program is executed by a processor to implement the method described above.

[0142] Some embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0143] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0144] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0148] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0149] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the aforementioned element.

[0150] The above provides a detailed description of a vehicle interaction method, device, vehicle, and storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as limiting the present invention.

Claims

1. A method for vehicle control, characterized in that, The vehicle has an intelligent driving system and a chassis stability control system, and the method includes: During the process of controlling the vehicle's movement through the intelligent driving system, the vehicle's motion state parameters and stability coefficient are determined; Acquire the collected road surface features; Based on the road surface characteristics, adjust the intervention threshold of the electronic stability control system of the chassis stability control system; When the motion state parameters are less than the intervention threshold of the electronic stability control system, a target driving trajectory is generated based on the stability coefficient and the road surface characteristics.

2. The method according to claim 1, characterized in that, When the motion state parameters are less than the intervention threshold of the electronic stability control system, the target driving trajectory is generated based on the stability coefficient and the road surface characteristics, including: When the motion state parameters are less than the intervention threshold of the electronic stability control system, multiple candidate driving trajectories are generated; Based on the stability coefficient and the road surface characteristics, predict the stability risk of each candidate driving trajectory; Based on the stability risk, the target driving trajectory is confirmed from the candidate driving trajectories.

3. The method according to claim 2, characterized in that, The step of predicting the stability risk of each candidate driving trajectory based on the stability coefficient and the road surface characteristics includes: Acquire the vehicle model; Based on the motion state parameters, the vehicle model, the stability coefficient, and the road surface features, the stability risk of each candidate driving trajectory is predicted.

4. The method according to claim 2, characterized in that, The step of confirming the target driving trajectory from the candidate driving trajectories based on the stability risk includes: Obtain the obstacle avoidance coefficient and ride comfort coefficient of the candidate driving trajectory; Based on the stability risk, the obstacle avoidance coefficient, and the ride comfort coefficient, the target driving trajectory is determined from the candidate driving trajectories.

5. The method according to claim 1, characterized in that, The step of adjusting the electronic stability control system intervention threshold of the chassis stability control system according to the road surface characteristics includes: Obtain the first intervention threshold of the chassis stability control system; Based on the road surface characteristics, the first intervention threshold is adjusted to obtain the second intervention threshold of the chassis stability control system.

6. The method according to claim 1, characterized in that, The method further includes: when the motion state parameter is greater than the intervention threshold of the electronic stability control system, switching to the chassis stability control system to control the vehicle driving, and the intelligent driving system coordinating and controlling the vehicle's drive and steering.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: when the intelligent driving system exits control of vehicle driving, restoring the electronic stability control system intervention threshold of the chassis stability control system to the default intervention threshold.

8. A vehicle control device, characterized in that, The device includes: The vehicle coefficient determination module is used to determine the motion state parameters and stability coefficient of the vehicle during the process of controlling the vehicle's driving through the intelligent driving system. The road surface feature acquisition module is used to acquire road surface features. An electronic stability control system intervention threshold adjustment module is used to adjust the electronic stability control system intervention threshold of the chassis stability control system according to the road surface characteristics. The trajectory generation module is used to generate a target driving trajectory based on the stability coefficient and the road surface characteristics when the motion state parameters are less than the intervention threshold of the electronic stability control system.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the vehicle driving control method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the vehicle driving control method as described in any one of claims 1 to 7.

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