Vehicle control method, system, and electronic device

Abstract

The application provides a vehicle control method, system and electronic equipment, and relates to the field of vehicle driving control.The method obtains wheel predicted torque by analyzing the driving environment and road characteristics of the vehicle in real time, and obtains wheel required torque by analyzing the state sensor built in the vehicle in real time, realizes active perception based on the road environment and passive perception based on the vehicle, and then dynamically adjusts the driving state of the vehicle through the wheel predicted torque and the wheel required torque, so that the four-wheel drive system and the suspension system of the vehicle are dynamically adapted to the driving road condition, and the drivability and comfort of the vehicle are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle driving control, and in particular to a vehicle control method, system and electronic device. Background Technology

[0002] With the increasing prevalence of four-wheel drive and air suspension systems in vehicles, efficiently and accurately utilizing these systems to enhance the driving and passenger experience is crucial for both users and manufacturers. Existing four-wheel drive and air suspension systems require manual adjustment by the user, lacking dynamic adaptation to road conditions and hindering real-time adjustment and dynamic correction. For example, four-wheel drive systems rely on preset torque ratios for adjustment, failing to adapt to road conditions. Furthermore, they rely solely on wheel speed differences to determine wheel slippage, resulting in a relatively high misjudgment rate, impacting the system's adjustment effectiveness and consequently affecting vehicle drivability and comfort. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a vehicle control method, system and electronic device. The method obtains the wheel predicted torque by analyzing the vehicle driving environment and road characteristics in real time, and obtains the wheel demand torque by analyzing the vehicle's built-in state sensors in real time. This enables active perception based on the road environment and passive perception based on the vehicle. Then, the vehicle's driving state is dynamically adjusted by the wheel predicted torque and wheel demand torque, thereby realizing the dynamic adaptation of the vehicle's four-wheel drive system and suspension system to the driving road conditions and improving the vehicle's drivability and comfort.

[0004] In a first aspect, embodiments of the present invention provide a vehicle control method, the method comprising:

[0005] Active perception steps: Real-time acquisition of road environment data corresponding to the current time period based on the vehicle's built-in camera and detection radar; determination of the vehicle's road driving information for the next time period based on the road environment data; and determination of the vehicle's predicted wheel torque based on the road driving information.

[0006] Passive sensing steps: Obtain vehicle attitude data and vehicle control data corresponding to the current time period based on the vehicle's built-in state sensors, and determine the required torque of the vehicle's wheels based on the vehicle attitude data and vehicle control data.

[0007] Decision-making steps: Determine the torque matching result for the vehicle based on the difference between the predicted torque of the wheel and the required torque of the wheel, and obtain the control parameters for the vehicle in the next time period based on the torque matching result;

[0008] Control execution steps: Adjust the vehicle's driving status using control parameters.

[0009] Optionally, prior to the decision-making step, the method further includes a weight setting step, including:

[0010] The road surface type corresponding to the road on which the vehicle is traveling is determined based on road environment data, and the recognition accuracy of the vehicle when identifying the road surface type on the road is calculated.

[0011] Obtain weather data corresponding to the driving road based on road environment data, and determine the environmental interference level corresponding to the vehicle's identification of road surface type on the driving road based on the weather data;

[0012] Based on the difference between the recognition accuracy and the recognition accuracy, the corresponding weighting coefficient between the predicted torque and the required torque of the wheel is determined; wherein, the weighting coefficient is used to determine the distribution ratio between the predicted torque and the required torque of the wheel in the torque matching result calculation process.

[0013] Optionally, after the decision-making step, the method further includes a weight update step, including:

[0014] The vehicle's wheel rotational angular velocity, wheel rolling radius, and vehicle speed are obtained based on the vehicle's attitude data.

[0015] The slip ratio of the vehicle is calculated using the wheel rotational angular velocity, wheel rolling radius, and vehicle speed.

[0016] Obtain the cumulative duration when the slippage rate is lower than a preset first threshold within the current time period, and obtain the comparison result between the cumulative duration and a preset second threshold;

[0017] Based on the comparison results, determine the corresponding weight adjustment value for the weight coefficient, and update the weight coefficient using the weight adjustment value.

[0018] Optional, proactive sensing steps include:

[0019] The system acquires data from the vehicle's built-in cameras and radar, and uses this data to determine the road environment data corresponding to the vehicle's location on the road in real time during the current time period.

[0020] The vehicle's driving direction and speed are determined based on road environment data to determine the vehicle's road driving information for the next time period.

[0021] The vehicle's wheel load and the road surface adhesion coefficient corresponding to the driving road are determined using road driving information.

[0022] The predicted wheel torque of the vehicle is calculated based on the wheel load and the road adhesion coefficient.

[0023] Optionally, the step of acquiring the vehicle's built-in camera and radar, and determining the road environment data corresponding to the vehicle's driving route in real time based on the camera and radar, includes:

[0024] The system acquires data from the first and second cameras built into the vehicle, as well as the millimeter-wave radar and lidar built into the vehicle; wherein the frame rate of the first camera is greater than that of the second camera.

[0025] The first camera is used to acquire road surface condition data corresponding to the road where the vehicle is traveling in real time within the current time period, and the road surface friction coefficient corresponding to the road surface is determined based on the road surface condition data.

[0026] The system uses a second camera to acquire real-time road surface texture and color data, millimeter-wave radar to acquire real-time road terrain data, and lidar to acquire real-time obstacle data.

[0027] The point cloud data corresponding to the driving road is determined based on road surface texture data, road surface color data, road terrain data, and obstacle data; the obstacles contained in the driving road are determined based on the point cloud data, and the obstacle distance and obstacle height are obtained;

[0028] Road environment data are determined based on the road surface friction coefficient, obstacle distance, and obstacle height.

[0029] Optional, passive sensing steps include:

[0030] The vehicle's built-in status sensors determine the corresponding wheel sensors, inertial sensors, and driving sensors.

[0031] Wheel sensors are used to obtain the vehicle's wheel speed, inertial sensors are used to obtain the vehicle's acceleration, pitch angle, roll angle and yaw angle, and driving sensors are used to obtain the vehicle's accelerator pedal depth and brake pedal depth.

[0032] The vehicle attitude data for the current time period is determined based on acceleration, pitch angle, roll angle and yaw angle, and the vehicle control data for the current time period is determined based on accelerator pedal depth and brake pedal depth.

[0033] The torque calculation value corresponding to the vehicle's wheels not slipping within the current time period is obtained based on wheel speed, vehicle attitude data, and vehicle control data, and the required torque of the vehicle's wheels is determined based on the torque calculation value.

[0034] Optional decision-making steps include:

[0035] Calculate the torque difference between the predicted wheel torque and the wheel required torque;

[0036] Calculate the torque ratio between the torque difference and the wheel's required torque, and obtain the cumulative duration during which the torque ratio is below a preset torque threshold;

[0037] The torque matching result of the vehicle is determined based on the ratio of the cumulative duration to the total duration corresponding to the current time period;

[0038] Based on the torque matching results, determine the corresponding suspension adjustment strategy, wheel drive strategy, and power response strategy for the vehicle in the next time period.

[0039] The control parameters of the vehicle in the next time period are determined by using suspension adjustment strategy, wheel drive strategy and power response strategy.

[0040] Optionally, control the execution steps, including:

[0041] Based on the suspension adjustment strategy, wheel drive strategy, and power response strategy, the control parameters, including the suspension height adjustment parameters, wheel slippage control parameters, and power response parameters, are determined respectively.

[0042] Obtain the suspension height value corresponding to the suspension height adjustment parameters, and use the suspension height adjustment parameters to control the vehicle's suspension to reach the suspension height value;

[0043] Obtain the wheel slip speed threshold corresponding to the wheel slip control parameters, and use the wheel slip speed threshold to adjust the timing of wheel slip intervention.

[0044] Obtain the power adjustment curves corresponding to the power response parameters, and use the power adjustment curves to adjust the vehicle's driving state.

[0045] In a second aspect, the present invention provides a vehicle control system, the system comprising:

[0046] Active perception module: It is used to acquire road environment data corresponding to the current time period in real time based on the vehicle's built-in camera and detection radar, determine the vehicle's road driving information in the next time period based on the road environment data, and determine the vehicle's wheel predicted torque based on the road driving information.

[0047] Passive sensing module: used to acquire vehicle attitude data and vehicle control data corresponding to the current time period based on the vehicle's built-in state sensors, and to determine the required torque of the vehicle's wheels based on the vehicle attitude data and vehicle control data.

[0048] Decision determination module: used to determine the torque matching result of the vehicle based on the difference between the predicted torque of the wheel and the required torque of the wheel, and to obtain the control parameters of the vehicle in the next time period based on the torque matching result;

[0049] Control execution module: Used to adjust the vehicle's driving state using control parameters.

[0050] Thirdly, embodiments of the present invention also provide an electronic device, which includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the vehicle control method provided in the first aspect.

[0051] Fourthly, embodiments of the present invention also provide a storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the steps of the vehicle control method provided in the first aspect.

[0052] This invention provides a vehicle control method, system, and electronic device. During the control of the suspension and four-wheel drive systems of a vehicle in motion, the method first acquires real-time road environment data corresponding to the current time period using the vehicle's built-in camera and detection radar. Based on this road environment data, it determines the vehicle's road driving information for the next time period and determines the predicted wheel torque. Then, it acquires vehicle attitude data and vehicle control data corresponding to the current time period using the vehicle's built-in state sensors and determines the required wheel torque. Subsequently, it determines the torque matching result based on the difference between the predicted wheel torque and the required wheel torque, and obtains the control parameters for the next time period based on the torque matching result. Finally, it adjusts the vehicle's driving state using the control parameters. This method obtains the predicted wheel torque by analyzing the vehicle's driving environment and road characteristics in real time, and obtains the required wheel torque by analyzing the vehicle's built-in state sensors in real time. This achieves both active perception based on the road environment and passive perception based on the vehicle, and dynamically adjusts the vehicle's driving state based on the predicted wheel torque and the required wheel torque. This enables dynamic adaptation of the vehicle's four-wheel drive system and suspension system to the driving conditions, improving the vehicle's drivability and comfort.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0056] Figure 1 A flowchart of a vehicle control method provided in an embodiment of the present invention;

[0057] Figure 2 This is a flowchart of the active sensing step S101 in a vehicle control method provided by an embodiment of the present invention;

[0058] Figure 3 This is a flowchart of step S201 in a vehicle control method provided by an embodiment of the present invention;

[0059] Figure 4 A flowchart of the passive sensing step S102 in a vehicle control method provided in an embodiment of the present invention;

[0060] Figure 5 This is a flowchart of the decision determination step S103 in a vehicle control method provided by an embodiment of the present invention;

[0061] Figure 6 This is a flowchart of control execution step S104 in a vehicle control method provided by an embodiment of the present invention;

[0062] Figure 7 A flowchart of another vehicle control method provided in an embodiment of the present invention;

[0063] Figure 8 A flowchart of the weight setting step S703 in a vehicle control method provided in an embodiment of the present invention;

[0064] Figure 9 A flowchart of the weight update step S705 in a vehicle control method provided in an embodiment of the present invention;

[0065] Figure 10 This is a schematic diagram of the structure of a vehicle control system provided in an embodiment of the present invention;

[0066] Figure 11 This is a schematic diagram of another vehicle control system provided in an embodiment of the present invention;

[0067] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0068] icon:

[0069] 1010 - Active sensing module; 1020 - Passive sensing module; 1030 - Decision making module; 1040 - Control execution module;

[0070] 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0072] To facilitate understanding of this embodiment, a vehicle control method disclosed in this embodiment of the invention will be described below, such as... Figure 1 As shown, the method includes:

[0073] Active perception step S101: Real-time acquisition of road environment data corresponding to the current time period based on the vehicle's built-in camera and detection radar, determination of the vehicle's road driving information for the next time period based on the road environment data, and determination of the vehicle's wheel predicted torque based on the road driving information.

[0074] The core of this step is to use vehicle environmental perception hardware to acquire road features in advance and calculate the appropriate torque, achieving proactive control "prepared before the event." Specifically, this includes the following:

[0075] Data Acquisition: Relying on the vehicle's built-in high-definition cameras (including forward-looking and surround-view cameras) and multi-band detection radar (millimeter-wave radar + lidar), the system collects real-time road environment data for the current driving section, including but not limited to: road surface type (asphalt, gravel, water, ice and snow, etc.), road surface adhesion coefficient, slope / angle, curve curvature, obstacle location and movement trend, road surface smoothness (such as potholes, speed bump distribution), and other multi-dimensional information.

[0076] Data processing and road surface prediction: Real-time analysis of collected data is performed using vehicle-mounted algorithms (such as deep learning image recognition and point cloud data modeling) to accurately identify road surface features and predict road driving information for the next time period; for example, if it is identified that the road surface 50 meters ahead is gravel with a coefficient of friction ≤0.3, it is predicted that the vehicle is about to enter a low-grip section; if it is identified that the curvature of the curve ahead is ≥15°, it is predicted that the vehicle needs to adjust its steering.

[0077] Predicted torque calculation: Based on the predicted road driving information and combined with the vehicle dynamics model (including tire friction characteristics and vehicle weight distribution parameters), the "predicted torque" corresponding to each wheel is calculated. The core objective of this torque is to adapt to the road surface limits: for example, on low-grip roads, the maximum torque of a single wheel needs to be reduced to avoid slippage; on uphill sections, the torque of the drive wheels needs to be increased to improve traction; and on curved sections, the torque of the left and right wheels needs to be differentiated to enhance steering stability. Ultimately, a torque distribution benchmark that accurately matches the road surface characteristics of the next time period is formed.

[0078] Passive sensing step S102: Obtain vehicle attitude data and vehicle control data corresponding to the current time period based on the vehicle's built-in state sensors, and determine the required wheel torque of the vehicle based on the vehicle attitude data and vehicle control data.

[0079] This step focuses on the vehicle's own state and the driver's operating intentions, dynamically capturing real-time needs to ensure that torque adjustment matches the current driving behavior. The specific execution is as follows:

[0080] Two types of core data are acquired simultaneously through the vehicle's built-in multi-dimensional status sensors:

[0081] Vehicle attitude data includes body roll angle, pitch angle, yaw rate, wheel bounce, suspension compression / tension, etc., collected by acceleration sensors, gyroscopes, and suspension travel sensors;

[0082] Vehicle control data includes driver operation commands (steering angle, throttle opening, braking intensity) collected by steering angle sensor, accelerator pedal position sensor, brake pedal travel sensor, and vehicle speed sensor, as well as real-time vehicle operating parameters (current vehicle speed, wheel speed, engine output power).

[0083] During the torque demand calculation process, the above data is fused and analyzed using a dynamic algorithm to generate the wheel torque demand. This torque directly responds to the real-time state: for example, when the driver presses the accelerator pedal deeply (throttle opening ≥ 60%), the torque demand increases synchronously with the engine output power; when the vehicle is cornering (steering angle ≥ 10°), the torque demand of the inner wheel decreases while the torque demand of the outer wheel increases to counteract centrifugal force; when the vehicle body experiences significant body roll (roll angle ≥ 5°), the torque demand of the corresponding wheel is finely adjusted to maintain posture stability, ensuring that the torque demand matches both the driver's operating intention and the vehicle's real-time operating state.

[0084] Decision determination step S103: Determine the torque matching result corresponding to the vehicle based on the difference between the predicted torque of the wheel and the required torque of the wheel, and obtain the control parameters corresponding to the vehicle in the next time period based on the torque matching result.

[0085] This step is crucial in connecting perception and execution. By quantitatively analyzing the differences between the two types of torque, it outputs a coordinated control strategy for the four-wheel drive system and the air suspension. The specific process is as follows:

[0086] Torque difference analysis: Calculating the difference between the predicted torque and the required torque for each wheel forms a torque matching matrix. Difference types include positive differences (predicted torque > required torque), such as when a slippery road surface is anticipated (low predicted torque), but the driver still presses the accelerator hard (high required torque), indicating insufficient adaptation between the operation and the road surface, posing a risk of slippage; and negative differences (predicted torque < required torque), such as when an uphill section is anticipated (high predicted torque), but the driver lightly presses the accelerator (low required torque), requiring additional torque to improve climbing ability.

[0087] When determining the torque matching result, the matching level is divided into complete matching, slight mismatch, moderate mismatch, and severe mismatch according to the absolute value of the difference and the duration. The determination can also be weighted by the urgency of the road surface (e.g., icy and snowy road surface > ordinary gravel road surface).

[0088] When outputting control parameters, precise control parameters for the next time period are generated based on the matching results, covering both systems:

[0089] Four-wheel drive system parameters: front and rear wheel torque distribution ratio, left and right wheel torque compensation coefficient (if the difference between the predicted torque and the required torque of a single wheel is large, the torque ratio of that wheel will be adjusted separately).

[0090] Air suspension parameters include damping adjustment coefficient (e.g., increasing damping on bumpy roads), vehicle height adjustment value (e.g., raising the vehicle height by 3-5cm on potholes), and suspension stiffness distribution ratio (e.g., increasing the stiffness of the outer suspension when cornering). These parameters ensure coordinated operation of the two systems.

[0091] Control execution step S104: Adjust the vehicle's driving state using control parameters.

[0092] This step translates control parameters into actual operations through actuators, while continuously optimizing via feedback mechanisms to achieve closed-loop control of real-time adaptation, effect verification, and continuous adjustment. Specifically, the four-wheel drive system adjusts the torque output of each wheel in real time through the electronically controlled transfer case and active torque manager based on torque distribution parameters. For example, on slippery surfaces (slight mismatch, predicted torque < required torque), the maximum torque output ratio of a single wheel is reduced, while the torque ratio of the non-slipping wheels is increased to avoid misjudgment and slippage caused by excessive wheel speed differences; on uphill sections (negative difference), the rear-wheel drive torque ratio is increased to 60%-80% to enhance traction.

[0093] The air suspension system adjusts the suspension state via electromagnetic control valves and an air compressor based on damping and height parameters. For example, if it anticipates bumpy road surfaces (predicted torque for low grip) and the vehicle body experiences vibrations (attitude data feedback), it immediately increases the suspension damping coefficient and raises the vehicle body to reduce vibration transmission; on smooth asphalt roads (fully adapted), it lowers the vehicle height and reduces damping to improve handling response and fuel economy.

[0094] During the adjustment process, the state sensor and environmental perception hardware synchronously collect the adjusted vehicle posture data (such as whether the roll has been alleviated and whether the wheel speed difference has been reduced) and the actual road feedback (such as whether slippage still exists). The above data is used to correct the predicted torque, required torque and control parameters for the next cycle in real time, so as to ensure that the adjustment effect continuously matches the changes in road conditions and driving needs, and avoid the adaptation lag problem caused by one-time adjustment.

[0095] Through the above four-step design, the vehicle can achieve dual perception of road surface prediction and real-time vehicle conditions, allowing the four-wheel drive system and air suspension to break through the limitations of manual adjustment, thereby dynamically adapting to different road conditions and driving operations. This can fundamentally reduce the wheel slippage misjudgment rate, improve the system adjustment accuracy, and ultimately achieve a simultaneous upgrade in drivability (grip, handling) and comfort (shock absorption, posture stability).

[0096] Optionally, the active sensing step S101, such as Figure 2 As shown, it includes:

[0097] Step S201: Obtain the built-in camera and detection radar in the vehicle, and determine the road environment data corresponding to the vehicle's driving road in real time based on the camera and detection radar during the current time period.

[0098] This step forms the basis of active perception, and its core is acquiring raw data on current road conditions through the vehicle's onboard environmental perception hardware. Specifically, it utilizes the vehicle's built-in high-definition cameras (including forward-facing and surround-view cameras to capture road images, lane lines, road textures, and obstacle shapes) and various types of detection radars (such as millimeter-wave radar and lidar to measure distances and relative speeds to obstacles, as well as detect three-dimensional features such as road surface undulations and slopes). These sensors synchronously collect data at a high frequency (typically 10-30 times per second) and transmit it in real time to the onboard processing unit, which then aggregates the data to form road environment data for the current time period, covering key information such as road surface type (e.g., asphalt, gravel, ice, snow, water), distribution of surrounding obstacles, road slope / aspect, and road surface smoothness (potholes, speed bump locations).

[0099] Step S202: Determine the vehicle's road driving information for the next time period based on the vehicle's driving direction and speed, and road environment data.

[0100] Based on current road environment data and combined with the vehicle's real-time driving status (driving direction and speed), the road surface characteristics of the next time period (e.g., within the next 1-3 seconds) are further predicted. The driving direction is determined by a combination of steering wheel angle, GPS positioning, and lane line recognition (e.g., determining whether the vehicle is about to enter a curve, straight section, or intersection). The driving speed is directly obtained from the vehicle speed sensor; the higher the speed, the farther the distance covered in the next time period (e.g., at 60 km / h, approximately 16.7 meters are traveled per second). By matching the current road environment data with the vehicle's trajectory, the road surface information for the next time period is finally determined, specifically including: the road surface type of the upcoming segment, the coefficient of friction, the gradient (e.g., a 5° uphill slope), the curvature of the curve (e.g., a radius of 50 meters), and the presence of continuous bumps or obstacles, providing accurate road surface characteristic data for subsequent torque calculations.

[0101] Step S203: Determine the vehicle's wheel load and the road surface adhesion coefficient corresponding to the driving road using road driving information.

[0102] After obtaining the road driving information for the next time period, the system focuses on extracting two core parameters:

[0103] Wheel load: This refers to the weight of the vehicle body borne by each wheel, and its magnitude is directly related to the slope and curve characteristics in the road driving information. For example, when going uphill, the vehicle's center of gravity shifts rearward, increasing the load on the rear wheels and decreasing the load on the front wheels; when cornering, centrifugal force causes the vehicle to tilt, increasing the load on the outer wheels and decreasing the load on the inner wheels. Specifically, the real-time load of each wheel in the next time period can be calculated using a preset vehicle dynamics model (inputting parameters such as vehicle weight, wheelbase, and track width) combined with the predicted slope and curve curvature.

[0104] Road surface adhesion coefficient: Reflects the frictional ability between the road surface and the tire, and is directly determined by the road surface type in the road driving information (e.g., approximately 0.8-1.0 for dry asphalt, approximately 0.4-0.6 for wet roads, and approximately 0.1-0.3 for icy and snowy roads). Specifically, it is cross-validated through image recognition (e.g., road surface texture, reflectivity) and radar detection (e.g., road surface hardness feedback) to accurately match the corresponding adhesion coefficient value, ensuring parameter accuracy.

[0105] Step S204: Calculate the predicted wheel torque of the vehicle based on the wheel load and the road adhesion coefficient.

[0106] Based on the wheel load and road adhesion coefficient obtained in step S203, the predicted torque of each wheel is calculated using vehicle dynamics formulas. The core principle is that the maximum driving torque that a tire can transmit (to prevent slippage) is directly proportional to the road adhesion coefficient and wheel load, as shown in the following formula: ,in, Predict the wheel torque for each wheel. The road surface adhesion coefficient, For wheel load, Let the four wheels be numbered. For each wheel, substituting its corresponding load and road adhesion coefficient, calculate the optimal torque value under the road conditions in the next time period that satisfies the driving force requirement without causing slippage; this is the wheel's predicted torque. The constraints used in solving for the wheel's predicted torque are:

[0107] ;

[0108] ;

[0109] in, Driven by aggregate demand; To provide braking force for each wheel; The radius of the wheel's rolling radius; This is the rolling resistance coefficient; For the overall vehicle weight; For vehicle speed; The frontal area of ​​the vehicle; This refers to the drag coefficient; air density; Longitudinal slope; for; This is the road surface adhesion coefficient.

[0110] This torque provides a forward-looking benchmark for the dynamic adjustment of the subsequent four-wheel drive system, ensuring that the vehicle is adapted to the upcoming road conditions in advance.

[0111] Optionally, step S201 involves acquiring the vehicle's built-in camera and detection radar, and determining the road environment data corresponding to the vehicle's location on the road in real time based on the camera and detection radar, as follows: Figure 3 As shown, it includes:

[0112] Step S301: Acquire the first and second cameras built into the vehicle, and acquire the millimeter-wave radar and lidar built into the vehicle; wherein the acquisition frame rate of the first camera is greater than that of the second camera.

[0113] This step first acquires data from two types of cameras (first camera and second camera) and two types of radar (millimeter-wave radar and lidar) built into the vehicle. The first camera has a higher frame rate (e.g., above 30fps), making it suitable for capturing rapidly changing road dynamics; the second camera has a lower frame rate (e.g., below 30fps), making it more suitable for static feature acquisition; the millimeter-wave radar and lidar focus on long-range terrain detection and high-precision 3D environment modeling, respectively, forming a complementary perception system.

[0114] Step S302: Use the first camera to acquire road surface condition data corresponding to the vehicle's driving road in real time during the current time period, and determine the road surface friction coefficient corresponding to the driving road based on the road surface condition data.

[0115] The system uses a first camera to capture real-time images of the road surface, collecting data on road conditions (such as the presence of water, snow, and dryness). Image recognition analysis of these conditions, combined with pre-defined road surface condition-friction coefficient rules (e.g., the friction coefficient of a wet road surface is approximately 0.4-0.6), calculates the road surface friction coefficient for the current segment, reflecting the tire's grip on the road surface.

[0116] Step S303: Use the second camera to acquire road surface texture data and road surface color data corresponding to the driving road in real time, use millimeter-wave radar to acquire road terrain data corresponding to the driving road in real time, and use lidar to acquire obstacle data contained in the driving road in real time.

[0117] The second camera acquires real-time road surface texture data (such as asphalt particle size and cement joints) and color data (such as the white of snow and the brown of mud) to help determine the road surface type; millimeter-wave radar detects road terrain data, including slope, undulation, and curvature of curves; and lidar scans and acquires obstacle data in the driving road (such as the outlines of pedestrians, vehicles, rocks, etc.).

[0118] Step S304: Determine the point cloud data corresponding to the driving road based on road surface texture data, road surface color data, road terrain data, and obstacle data; determine the obstacles contained in the driving road based on the point cloud data, and obtain the obstacle distance and obstacle height corresponding to the obstacles.

[0119] By fusing road surface texture, color, terrain, and obstacle data, a 3D point cloud of the driving road is generated. Obstacles are identified based on the point cloud data, and their key parameters are calculated: the distance between the obstacle and the vehicle (to assess collision risk) and the height of the obstacle (to assess the difficulty of passage, such as the height of speed bumps).

[0120] Step S305: Determine road environment data based on road surface friction coefficient, obstacle distance, and obstacle height.

[0121] The road surface friction coefficient (reflecting grip), obstacle distance (reflecting safe distance), and obstacle height (reflecting traffic obstruction) obtained from the above steps are integrated to form complete road environment data, providing a comprehensive basis for subsequent road surface prediction.

[0122] Optionally, the passive sensing step S102, such as Figure 4 As shown, it includes:

[0123] Step S401: Determine the corresponding wheel sensors, inertial sensors, and driving sensors of the vehicle through the vehicle's built-in status sensors.

[0124] First, the vehicle's built-in status sensors are invoked and divided into three core devices, each corresponding to different dimensions of vehicle status monitoring.

[0125] Wheel sensors are installed on each wheel to directly monitor the wheel's rotation status;

[0126] Inertial sensors, including accelerometers and gyroscopes, are integrated into the vehicle body (such as near the center of gravity) to capture dynamic changes in the vehicle's attitude.

[0127] Driving sensors are installed on control components such as the accelerator pedal and brake pedal to sense the driver's operating intentions.

[0128] These three types of sensors work together to cover the entire chain of state perception, from wheel movement to vehicle posture to driving commands.

[0129] Step S402: Use wheel sensors to obtain the vehicle's wheel speed, use inertial sensors to obtain the vehicle's acceleration, pitch angle, roll angle and yaw angle, and use driving sensors to obtain the vehicle's accelerator pedal depth and brake pedal depth.

[0130] Various sensors collect data at preset frequencies (usually 10-100Hz to ensure real-time performance). Wheel sensors are used to output the wheel speed of each wheel in real time (unit: r / min or km / h), reflecting how fast the wheel rotates and providing basic data for determining whether slippage has occurred.

[0131] Inertial sensors are used to synchronously collect the vehicle's longitudinal / lateral acceleration (reflecting centrifugal force during acceleration, deceleration, or steering), pitch angle (the degree of forward / backward tilt of the vehicle body, corresponding to the center of gravity shift during uphill / downhill or braking / acceleration), roll angle (the degree of left / right tilt of the vehicle body, corresponding to cornering or unilateral bumping), and yaw angle (the angle of rotation of the vehicle body about the vertical axis, reflecting dynamic stability during steering).

[0132] Driving sensors are used to accurately measure accelerator pedal depth (percentage, such as 0-100%, reflecting the intensity of the driver's acceleration demand) and brake pedal depth (or braking pressure, reflecting the intensity of deceleration demand), which are directly related to the driver's driving intentions.

[0133] Step S403: Determine the vehicle attitude data corresponding to the vehicle in the current time period based on acceleration, pitch angle, roll angle and yaw angle, and determine the vehicle control data corresponding to the vehicle in the current time period based on accelerator pedal depth and brake pedal depth.

[0134] The collected raw data was categorized and integrated to form two core data categories:

[0135] Vehicle attitude data: Compiled from the outputs of inertial sensors, including parameters such as acceleration, pitch angle, roll angle, and yaw angle, comprehensively reflecting the current dynamic attitude of the vehicle (e.g., "roll angle 5° + yaw angle 2°" indicates the vehicle is taking a gentle curve, "pitch angle 3° + longitudinal acceleration -2m / s²" indicates the vehicle is taking a gentle curve). 2 (This indicates the vehicle is braking while going downhill).

[0136] Vehicle control data: determined by the output of driving sensors, namely the accelerator pedal depth and brake pedal depth, which directly map the driver's operating intentions (e.g., 80% accelerator pedal depth indicates a strong acceleration demand, and 50% brake pedal depth indicates a moderate deceleration demand).

[0137] Step S404: Obtain the torque calculation value corresponding to the vehicle's wheels not slipping in the current time period based on wheel speed, vehicle attitude data and vehicle control data, and determine the vehicle's wheel torque requirement based on the torque calculation value.

[0138] By combining wheel speed, vehicle attitude data, and vehicle control data, the required torque for each wheel is calculated using a dynamic model. First, a basic torque requirement is initially determined based on the accelerator / brake pedal depth (control data) (e.g., deeper accelerator pedal press corresponds to higher torque demand). Then, the basic torque is corrected by incorporating vehicle attitude data (e.g., adjusting torque distribution between left and right wheels during roll and adjusting the torque ratio between front and rear wheels during pitch) to ensure the torque is appropriate for the vehicle's attitude. Next, the maximum torque for a given wheel is limited by its wheel speed (if a wheel's speed is significantly higher than others, it indicates a risk of slippage), and the calculated torque value for when the wheel does not slip is determined. Finally, this calculated torque value is used as the required torque for the wheel, satisfying the driver's control intentions, adapting to the vehicle's real-time attitude, and preventing wheel slippage.

[0139] Through the above four steps, the passive perception process realizes a complete process from sensor data acquisition to state and intent interpretation, and then to accurate calculation of required torque, providing a torque benchmark that fits the current state for the real-time dynamic adjustment of the vehicle.

[0140] Optionally, the decision-making step S103, such as Figure 5 As shown, it includes:

[0141] Step S501: Calculate the torque difference between the predicted wheel torque and the required wheel torque.

[0142] For each wheel, the difference between the predicted torque (adaptive torque based on road environment prediction) and the required torque (real-time required torque based on current vehicle status and driving intention) is calculated to obtain the torque difference value. This difference can be positive or negative: a positive difference indicates that the predicted torque is greater than the required torque (there may be a risk of torque over-limit, such as a predicted slippery road surface but a high current required torque); a negative difference indicates that the predicted torque is less than the required torque (there may be insufficient torque, such as a predicted uphill section but a low current required torque).

[0143] Step S502: Calculate the torque ratio between the torque difference and the wheel's required torque, and obtain the cumulative duration during which the torque ratio is lower than a preset torque threshold.

[0144] Based on the torque difference obtained in step S501, the torque ratio (i.e., the ratio of the absolute value of the torque difference to the required torque of the wheel) is further calculated to quantify the degree of deviation in torque matching (e.g., a ratio of 10% indicates a small deviation, and 50% indicates a significant deviation). Simultaneously, the cumulative duration for which the torque ratio is below a preset torque threshold is recorded. The preset torque threshold (e.g., 20%) is the standard for judging whether torque matching is acceptable. The cumulative duration refers to the total time within the current time period during which the torque deviation is within an acceptable range (e.g., if the ratio is <20% for 8 seconds out of 10 seconds, the cumulative duration is 8 seconds).

[0145] Step S503: Determine the vehicle torque matching result based on the ratio of the cumulative duration to the total duration corresponding to the current time period.

[0146] The ratio of cumulative duration to the total duration of the current time period (i.e., the percentage of successful matching) is used as the core indicator to classify the torque matching results into different levels. For example:

[0147] If the ratio is ≥90%, it is considered a perfect match (torque supply and demand are basically matched, and no major adjustments are needed).

[0148] If 60% ≤ ratio < 90%, it is judged as a slight mismatch (there is a deviation in a local time period, which needs to be fine-tuned);

[0149] If 30% ≤ ratio < 60%, it is judged as moderate mismatch (significant deviation in many time periods, requiring targeted adjustment);

[0150] If the ratio is less than 30%, it is considered a severe mismatch (significant deviation in most time periods, requiring comprehensive optimization).

[0151] The matching results directly reflect the degree of fit between the predicted torque and the required torque, providing a basis for subsequent strategy formulation.

[0152] Step S504: Based on the torque matching results, determine the corresponding suspension adjustment strategy, wheel drive strategy, and power response strategy for the vehicle in the next time period.

[0153] Based on the torque matching results of step S503, three core strategies are specifically generated, covering the vehicle's key adjustment systems:

[0154] Suspension adjustment strategy: Based on the matching results and road surface predictions (such as bumps and slopes), determine the direction of air suspension adjustment. For example, in the case of severe mismatch and prediction of bumpy roads, the strategy is to raise the vehicle height by 3cm and increase the damping coefficient by 20%; in the case of mild mismatch and flat roads, the strategy is to maintain the current height and fine-tune the damping to comfort mode.

[0155] Wheel drive strategy: Focus on the torque distribution of the four-wheel drive system and adjust according to the direction of the torque difference. For example, when the negative difference is dominant (insufficient torque) and climbing is in progress, the strategy is to increase the torque share of the rear wheels to 70%; when the positive difference is dominant (excess torque) and the road surface is slippery, the strategy is to limit the maximum torque output of a single wheel to reduce the risk of slippage.

[0156] Power response strategy: Corresponding to the output characteristics of the engine / motor to adapt to driving intentions and road conditions. For example, when there is a moderate mismatch and the driver presses the accelerator hard, the strategy is to slow down the increase in power output to avoid a sudden increase in torque that could cause slippage; when there is a perfect match, the strategy is to maintain power response sensitivity to ensure smooth driving.

[0157] Step S505: Determine the control parameters of the vehicle in the next time period using the suspension adjustment strategy, wheel drive strategy, and power response strategy.

[0158] The three strategies in step S504 are converted into executable quantitative parameters, i.e., control parameters. For example, the parameters corresponding to the suspension adjustment strategy are: vehicle height adjustment value (e.g., +3cm), specific value of damping coefficient (e.g., 1.2N·s / m), and suspension stiffness distribution ratio (e.g., 70% on the outer side / 30% on the inner side).

[0159] Parameters corresponding to the wheel drive strategy: front and rear wheel torque distribution ratio (e.g., 30:70), left and right wheel torque compensation coefficient (e.g., left wheel 0.9 / right wheel 1.1);

[0160] Parameters corresponding to the power response strategy: throttle opening-torque output delay time (e.g., 0.3 seconds), maximum torque limit (e.g., 200N). m).

[0161] These parameters precisely guide the actuators of the four-wheel drive system and air suspension, ensuring that the adjustment actions conform to the strategic objectives.

[0162] Through the above five steps, the decision-making process achieves a closed-loop decision-making process, from quantifying torque differences to determining matching levels, and then to formulating strategies and converting parameters, providing a clear and executable control basis for vehicle dynamic adjustment.

[0163] Optionally, control the execution of step S104, such as... Figure 6 As shown, it includes:

[0164] Step S601: Determine the suspension height adjustment parameters, wheel slip control parameters, and power response parameters included in the control parameters according to the suspension adjustment strategy, wheel drive strategy, and power response strategy, respectively.

[0165] This step extracts three categories of key execution parameters from the control parameters generated during the decision-making phase, based on the corresponding strategy:

[0166] Suspension height adjustment parameters: derived from suspension adjustment strategy, including target vehicle height (e.g., +3cm, -2cm), adjustment rate (e.g., 1cm per second), etc., which directly determine the range and speed of air suspension lifting and lowering;

[0167] Wheel slip control parameters: generated based on wheel drive strategy, the core of which is the wheel slip speed threshold (e.g., the speed of a certain wheel is 30% higher than that of other wheels), used to determine when to trigger anti-slip adjustment;

[0168] Power response parameters: determined by the power response strategy, with the power adjustment curve as the core (such as the correspondence between throttle opening and torque output), to control the power output characteristics of the engine / motor.

[0169] Step S602: Obtain the suspension height value corresponding to the suspension height adjustment parameter, and use the suspension height adjustment parameter to control the vehicle's suspension to reach the suspension height value.

[0170] This step extracts the specific suspension height value from the suspension height adjustment parameters (e.g., when the current road surface is uneven, the target height is "standard height + 5cm"), and sends instructions to the actuators of the air suspension system (e.g., air compressor, solenoid valve):

[0171] If it is necessary to raise the vehicle body, the compressor inflates the suspension airbags to increase the airbag volume and raise the vehicle body.

[0172] If it is necessary to lower the vehicle body, the solenoid valve opens to release air, reducing the airbag volume to lower the vehicle body;

[0173] During the adjustment process, the suspension travel sensor provides real-time feedback on the current height until the target height value is reached, ensuring that the vehicle's posture adapts to the road surface (e.g., raising it to avoid scratches when passing speed bumps, and lowering it to improve stability when driving at high speeds).

[0174] Step S603: Obtain the wheel slip speed threshold corresponding to the wheel slip control parameters, and use the wheel slip speed threshold to adjust the timing of wheel slip intervention.

[0175] This step determines the wheel slippage speed threshold based on wheel slippage control parameters (this threshold is dynamically adjusted according to road surface prediction: for example, the threshold is set at 20% speed difference on icy and snowy roads and 40% speed difference on dry roads), and inputs it into the anti-slip control module of the four-wheel drive system. When the wheel sensor detects that the difference between the speed of a certain wheel and that of other wheels exceeds this threshold, it is determined that "slippage is imminent," and immediate intervention is made (such as by braking that wheel or distributing more torque to other wheels).

[0176] Compared to the traditional method of relying solely on a fixed wheel speed difference, dynamically adjusted thresholds can reduce misjudgments (such as intervening earlier to prevent slippage on wet roads and avoiding unnecessary power restrictions on dry roads), thus improving the responsiveness of the four-wheel drive system.

[0177] Step S604: Obtain the power adjustment curve corresponding to the power response parameters, and use the power adjustment curve to adjust the driving state of the vehicle.

[0178] This step calls upon the power adjustment curve in the power response parameters (this curve shows the dynamic relationship between throttle opening and torque output: for example, the curve is flatter on wet and slippery roads to avoid a sudden increase in torque; the curve is steeper when climbing hills to quickly output large torque), and uses this to control the engine throttle / motor output. When the driver presses the accelerator pedal, the corresponding torque is output according to the current pedal depth, referring to the adjustment curve (rather than outputting it directly according to a fixed ratio).

[0179] For example, when anticipating a gravel road surface, the curve is set to "output 60% of the maximum torque when the throttle opening is 50%", which satisfies the driving needs while avoiding excessive torque that could cause slippage; on a flat road surface, the curve returns to a linear relationship to ensure direct driving response.

[0180] Through the above four steps, the control execution steps transform the strategy of the decision-making stage into specific mechanical actions, realizing the coordinated adjustment of suspension height, anti-slip timing, and power output, ultimately allowing the vehicle's driving state to dynamically adapt to the road environment and driving needs, thereby improving drivability and comfort.

[0181] like Figure 7 The flowchart of another vehicle control method shown here, specifically, before the decision determination step, the method also includes a weight setting step S703, such as... Figure 8 As shown, it includes:

[0182] Step S801: Determine the road surface type corresponding to the vehicle's driving road based on the road environment data, and calculate the recognition accuracy corresponding to the vehicle's identification of the road surface type in the driving road.

[0183] This step identifies the current road surface type (such as dry asphalt, flooded road, icy / snowy road, gravel road, etc.) based on acquired road environment data (e.g., road surface texture, color, friction coefficient, etc.). Simultaneously, the algorithm calculates the accuracy of this road surface type identification, i.e., the reliability of the road surface type judgment (e.g., through cross-validation of multi-sensor data, the accuracy of identifying "currently an icy / snowy road surface" is 85%). Higher accuracy indicates a more reliable road surface feature judgment.

[0184] Step S802: Obtain the weather data corresponding to the driving road based on the road environment data, and determine the environmental interference degree corresponding to the vehicle's identification of road surface type on the driving road based on the weather data.

[0185] Weather data (such as sunny, rainy, foggy, and snowy weather) is extracted from road environment data, and the environmental interference level is determined based on the degree to which weather affects sensor recognition. For example, rainy weather can cause camera lens reflection and increased noise in LiDAR point clouds, interfering with road feature recognition, resulting in a high environmental interference level (e.g., 70%); while sunny weather with no interference results in a low environmental interference level (e.g., 10%). This indicator is used to quantify the negative impact of the external environment on road recognition accuracy.

[0186] Step S803: Based on the difference between the recognition accuracy and the recognition accuracy, determine the corresponding weighting coefficient between the predicted torque of the wheel and the required torque of the wheel; wherein, the weighting coefficient is used to determine the distribution ratio between the predicted torque of the wheel and the required torque of the wheel in the torque matching result calculation process.

[0187] Based on the difference between recognition accuracy and environmental interference (e.g., recognition accuracy 85% minus environmental interference 30%, the difference is 55%), the weighting coefficients for predicted wheel torque and required wheel torque are determined. This coefficient is used to adjust the proportion of the two in the torque matching calculation: if the difference is large (accurate recognition, low interference), the predicted torque has a higher weight (e.g., 7:3), indicating more reliable road surface prediction and should be given priority; if the difference is small (inaccurate recognition, high interference), the required torque has a higher weight (e.g., 3:7), indicating a greater reliance on real-time vehicle status judgment.

[0188] Ultimately, the influence of the two types of torque is balanced by a weighting coefficient, making the torque matching result more consistent with the actual scenario. The weighting coefficient affects the execution degree of the active prediction value; that is, the vehicle's execution parameters (such as four-wheel torque and braking force) follow the active prediction value. If passive perception participates in the correction, the active prediction will fail for a fixed period of time, and the allocation of vehicle execution parameters (four-wheel torque and braking force) will use the passive perception calculation result. For example, when the weighting coefficient is greater than 90%, the passive perception participation in the correction is 0; when the weighting coefficient is less than 50%, it completely enters the passive perception state.

[0189] Optionally, after the decision-making step, the method further includes a weight update step S705, such as... Figure 9 As shown, it includes:

[0190] Step S901: Obtain the vehicle's wheel rotational angular velocity, wheel rolling radius, and vehicle speed based on the vehicle attitude data.

[0191] Key parameters related to wheel slippage are obtained from vehicle attitude data. Specifically, the wheel rotational angular velocity is monitored in real time by wheel sensors, reflecting how fast the wheel rotates; the wheel rolling radius is an inherent parameter of the vehicle (such as the standard radius corresponding to the tire model), affecting the calculation of the actual rolling speed of the wheel; and the vehicle speed is obtained by vehicle speed sensors or GPS, representing the overall driving speed of the vehicle.

[0192] Step S902: Calculate and obtain the vehicle's slip ratio using the wheel rotational angular velocity, wheel rolling radius, and vehicle speed.

[0193] Based on the above parameters, the slip ratio (a core indicator reflecting the degree of wheel slippage) is calculated using a formula. The calculation formula can be simplified to: ;in, The angular velocity of the wheel's rotation. The radius of the wheel's rolling motion. For vehicle speed, It represents the slip ratio.

[0194] Step S903: Obtain the cumulative duration when the slippage rate is lower than the preset first threshold within the current time period, and obtain the comparison result between the cumulative duration and the preset second threshold.

[0195] This step pre-sets a first threshold (e.g., 5%, representing the upper limit of the slip rate when the wheel has no obvious slippage) and counts the cumulative time during which the slip rate is lower than this threshold (i.e., the total time the wheel operates normally). Then, this cumulative time is compared with a second preset threshold (e.g., 80% of the total time during the current period, representing the minimum standard for achieving the slip rate target) to obtain the comparison result (e.g., whether the cumulative time meets the target or not).

[0196] Step S904: Determine the weight adjustment value corresponding to the weight coefficient based on the comparison results, and update the weight coefficient using the weight adjustment value.

[0197] Based on the comparison results in step S903, the weight adjustment value is determined. If the cumulative duration is greater than or equal to the second threshold (good slip condition), it indicates that the current weight coefficient is reasonable and the adjustment value is small (e.g., ±5%), and fine-tuning is sufficient. If the cumulative duration is less than the second threshold (frequent slip), it indicates that the weight allocation may be biased (e.g., the predicted torque weight is too high, resulting in insufficient anti-slip), and the adjustment value is large (e.g., ±15%), and the predicted torque weight needs to be reduced and the demand torque weight increased.

[0198] Finally, the original weight coefficients are updated by adjusting the weight values ​​to ensure that the subsequent torque matching results are more in line with the actual slippage situation and to optimize the adjustment accuracy.

[0199] In real-world scenarios, based on signals from the active perception system—that is, road surface types identified in advance through cameras, radar, etc.—the system determines potential vehicle travel conditions and predicts the status of each wheel, such as front wheels or rear wheels being off-center. Then, it applies braking force to the off-center wheels or activates electronic limited-slip differentials to transfer power to the on-center wheels. On paved roads, slippage is generally not a problem, only occurring when turning, where there is a speed difference between the left and right wheels. On cobblestone roads, however, single-wheel slippage, two-wheel slippage, or cross-axle slippage may occur. In these cases, the slippage threshold for braking intervention can be lowered, the braking intervention rate increased, and the torque transfer rate between the front and rear axles increased. If the active perception matches the slippage situation analyzed by passive data, the process continues. If it does not match the passive data, the decision-making system optimizes the current traction distribution parameters. In real-world scenarios, under favorable weather conditions, this method can achieve an accuracy rate of over 95%.

[0200] Suppose a user is driving a vehicle on an asphalt road and is about to pass a cross-axle dirt road. 100 meters from the dirt road, the vehicle's camera in the active perception system detects a change in road conditions, from paved road to an unpaved cross-axle dirt road. After the decision module receives and confirms the information, it inputs parameters suitable for driving on the cross-axle dirt road into each subsystem before the vehicle enters the dirt road. For example, the slip control parameters are increased by 50% compared to the paved road, the power response gradient is increased by 20% compared to the paved road, and the suspension height parameters are increased by 30% compared to the paved road. When passing through the cross-axle dirt road, braking forces proportional to the drive axle torque are applied to the diagonally opposite wheels that may be suspended, or the electronic limited-slip differential is activated to ensure that the power is used on the wheels with higher contact.

[0201] As can be seen from the above vehicle control method, this method obtains the wheel predicted torque by analyzing the vehicle's driving environment and road characteristics in real time, and obtains the wheel demand torque by analyzing the vehicle's built-in state sensors in real time. This enables active perception based on the road environment and passive perception based on the vehicle. Then, the vehicle's driving state is dynamically adjusted through the wheel predicted torque and wheel demand torque, thereby realizing the dynamic adaptation of the vehicle's four-wheel drive system and suspension system to the driving road conditions, and improving the vehicle's drivability and comfort.

[0202] Corresponding to the above vehicle control method embodiments, this invention also provides a vehicle control system, such as... Figure 10 As shown, the system includes:

[0203] Active perception module 1010: It is used to acquire road environment data corresponding to the current time period in real time based on the vehicle's built-in camera and detection radar, determine the vehicle's road driving information in the next time period based on the road environment data, and determine the vehicle's wheel predicted torque based on the road driving information.

[0204] Passive sensing module 1020: used to acquire vehicle attitude data and vehicle control data corresponding to the current time period based on the vehicle's built-in state sensors, and to determine the required wheel torque of the vehicle based on the vehicle attitude data and vehicle control data.

[0205] Decision determination module 1030: used to determine the torque matching result of the vehicle based on the difference between the predicted torque of the wheel and the required torque of the wheel, and to obtain the control parameters of the vehicle in the next time period based on the torque matching result;

[0206] Control execution module 1040: used to adjust the driving state of the vehicle using control parameters.

[0207] As can be seen from the above vehicle control system, the system obtains the wheel prediction torque by analyzing the vehicle's driving environment and road characteristics in real time, and obtains the wheel demand torque by analyzing the vehicle's built-in state sensors in real time. This enables active perception based on the road environment and passive perception based on the vehicle. Then, the system dynamically adjusts the vehicle's driving state based on the wheel prediction torque and wheel demand torque, thereby achieving dynamic adaptation of the vehicle's four-wheel drive system and suspension system to the driving road conditions, and improving the vehicle's drivability and comfort.

[0208] like Figure 11 The diagram shows another vehicle control system structure; the active perception module 1010 may include: a high-speed camera for identifying the road surface state where the wheels will be, providing input for analyzing the adhesion coefficient of each wheel on the road; a camera for collecting road surface texture and color; a millimeter-wave radar for detecting terrain; a lidar for detecting obstacles and terrain; and an active signal processing module for processing the active perception signals and converting them into signals for the decision module.

[0209] The passive sensing module 1020 may include: a wheel speed sensor for collecting the wheel speed of each wheel; an inertial sensor for collecting vehicle acceleration, vehicle pitch angle, roll angle and yaw rate; a vehicle communication module for transmitting key signals such as accelerator pedal depth and brake pedal depth; and a passive signal processing module for processing passive sensing signals and converting them into signals for the decision module to process.

[0210] The decision-making module 1030 is used to comprehensively process the signals transmitted by the active and passive sensing modules, and after decision calculation, sends execution instructions to each actuator of the vehicle.

[0211] The control execution module 1040 is used to execute various instructions sent by the decision module, such as suspension height control, power output control, steering assist control, and braking force control.

[0212] As can be seen from the above vehicle control system, the system acquires road condition information through both active identification and passive perception, and dynamically adjusts the vehicle's torque output, torque distribution, reference speed calculation method, and braking response rate to ensure that users can achieve the best vehicle performance simply by driving normally, regardless of the road conditions encountered.

[0213] The vehicle control system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned vehicle control method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned vehicle control method embodiment.

[0214] This embodiment also provides an electronic device, the structural schematic diagram of which is shown below. Figure 12 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the above-described vehicle control method.

[0215] Figure 12 The electronic device shown also includes a bus 103 and a communication interface 104, with the processor 101, communication interface 104 and memory 102 connected via the bus 103.

[0216] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0217] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0218] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0219] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the steps of the vehicle control method described in the foregoing embodiments.

[0220] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0221] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0222] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0223] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0224] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A vehicle control method, characterized in that, The method includes: Active perception steps: Real-time acquisition of road environment data corresponding to the current time period based on the vehicle's built-in camera and detection radar; determination of the vehicle's road driving information for the next time period based on the road environment data; and determination of the vehicle's predicted wheel torque based on the road driving information. Passive sensing steps: Obtain vehicle attitude data and vehicle control data corresponding to the current time period based on the vehicle's built-in state sensors, and determine the required wheel torque of the vehicle based on the vehicle attitude data and vehicle control data; Decision-making steps: Determine the torque matching result corresponding to the vehicle based on the difference between the predicted torque of the wheel and the required torque of the wheel, and obtain the control parameters of the vehicle corresponding to the next time period based on the torque matching result; Control execution steps: Adjust the driving state of the vehicle using the control parameters; Prior to the decision-making step, the method further includes a weight setting step, comprising: The road surface type corresponding to the road on which the vehicle is traveling is determined based on the road environment data, and the recognition accuracy of the vehicle when identifying the road surface type on the road is calculated. Based on the road environment data, obtain the weather data corresponding to the driving road, and determine the environmental interference degree corresponding to the vehicle identifying the road surface type on the driving road based on the weather data; Based on the difference between the recognition accuracy and the environmental interference, a weighting coefficient is determined between the predicted wheel torque and the required wheel torque; wherein, the weighting coefficient is used to determine the distribution ratio between the predicted wheel torque and the required wheel torque during the torque matching process.

2. The vehicle control method according to claim 1, characterized in that, Following the decision-making step, the method further includes a weight update step, comprising: The vehicle's wheel rotational angular velocity, wheel rolling radius, and vehicle speed are obtained based on the vehicle attitude data. The slip ratio of the vehicle is calculated and obtained using the wheel rotation angular velocity, the wheel rolling radius, and the vehicle speed. Obtain the cumulative duration when the slip ratio is lower than a preset first threshold within the current time period, and obtain the comparison result between the cumulative duration and a preset second threshold; Based on the comparison results, the weight adjustment value corresponding to the weight coefficient is determined, and the weight coefficient is updated using the weight adjustment value.

3. The vehicle control method according to claim 1, characterized in that, The active sensing step includes: The system acquires the built-in camera and detection radar in the vehicle, and determines the road environment data corresponding to the vehicle's driving road in real time based on the camera and detection radar during the current time period. The road driving information of the vehicle in the next time period is determined by the vehicle's driving direction and speed and based on the road environment data. The vehicle's wheel load and the road surface adhesion coefficient corresponding to the driving road are determined using the road surface driving information. The predicted wheel torque of the vehicle is calculated based on the wheel load and the road surface adhesion coefficient.

4. The vehicle control method according to claim 3, characterized in that, The steps of acquiring the built-in camera and detection radar in the vehicle, and determining the road environment data corresponding to the vehicle's driving road in real time within the current time period based on the camera and the detection radar, include: The system acquires the first and second cameras built into the vehicle, as well as the millimeter-wave radar and lidar built into the vehicle; wherein the frame rate of the first camera is greater than that of the second camera. The first camera is used to acquire road surface condition data corresponding to the road where the vehicle is traveling in real time during the current time period, and the road surface friction coefficient corresponding to the road is determined based on the road surface condition data. The second camera is used to acquire road surface texture data and road surface color data corresponding to the driving road in real time, the millimeter-wave radar is used to acquire road terrain data corresponding to the driving road in real time, and the lidar is used to acquire obstacle data contained in the driving road in real time. Based on the road surface texture data, road surface color data, road terrain data, and obstacle data, the point cloud data corresponding to the driving road is determined; based on the point cloud data, the obstacles contained in the driving road are determined, and the obstacle distance and obstacle height corresponding to the obstacles are obtained; The road environment data is determined based on the road surface friction coefficient, the distance to the obstacle, and the height of the obstacle.

5. The vehicle control method according to claim 1, characterized in that, The passive sensing step includes: The vehicle's built-in state sensors determine the corresponding wheel sensors, inertial sensors, and driving sensors. The wheel speed of the vehicle is obtained using the wheel sensor, the acceleration, pitch angle, roll angle and yaw angle of the vehicle are obtained using the inertial sensor, and the accelerator pedal depth and brake pedal depth of the vehicle are obtained using the driving sensor. The vehicle attitude data corresponding to the vehicle in the current time period is determined based on the acceleration, pitch angle, roll angle and yaw angle, and the vehicle control data corresponding to the vehicle in the current time period is determined based on the accelerator pedal depth and brake pedal depth. The torque calculation value corresponding to the vehicle's wheels not slipping in the current time period is obtained based on the wheel speed, vehicle posture data, and vehicle control data, and the wheel torque requirement of the vehicle is determined based on the torque calculation value.

6. The vehicle control method according to claim 1, characterized in that, The decision-making steps include: Calculate the torque difference between the predicted torque of the wheel and the required torque of the wheel; Calculate the torque ratio between the torque difference and the required torque of the wheel, and obtain the cumulative duration during which the torque ratio is lower than a preset torque threshold; The torque matching result of the vehicle is determined based on the ratio of the cumulative duration to the total duration corresponding to the current time period; Based on the torque matching results, the suspension adjustment strategy, wheel drive strategy, and power response strategy of the vehicle in the next time period are determined. The control parameters of the vehicle in the next time period are determined using the suspension adjustment strategy, the wheel drive strategy, and the power response strategy.

7. The vehicle control method according to claim 6, characterized in that, The control execution steps include: The suspension height adjustment parameter, wheel slippage control parameter, and power response parameter included in the control parameters are determined according to the suspension adjustment strategy, the wheel drive strategy, and the power response strategy, respectively. Obtain the suspension height value corresponding to the suspension height adjustment parameter, and use the suspension height adjustment parameter to control the vehicle's suspension to reach the suspension height value; Obtain the wheel slip speed threshold corresponding to the wheel slip control parameters, and use the wheel slip speed threshold to adjust the timing of wheel slip intervention of the vehicle; Obtain the power adjustment curve corresponding to the power response parameters, and use the power adjustment curve to adjust the driving state of the vehicle.

8. A vehicle control system, characterized in that, The system includes: Active perception module: used to acquire road environment data corresponding to the current time period in real time based on the vehicle's built-in camera and detection radar, determine the vehicle's road driving information corresponding to the next time period based on the road environment data, and determine the vehicle's wheel predicted torque based on the road driving information. Passive sensing module: used to acquire vehicle attitude data and vehicle control data corresponding to the current time period based on the vehicle's built-in state sensors, and to determine the required wheel torque of the vehicle based on the vehicle attitude data and the vehicle control data; The weight setting module is used to determine the road surface type corresponding to the vehicle's driving road based on the road environment data, and calculate the recognition accuracy of the vehicle when identifying the road surface type on the driving road; obtain the weather data corresponding to the driving road based on the road environment data, and determine the environmental interference degree corresponding to the vehicle when identifying the road surface type on the driving road based on the weather data; and determine the weighting coefficient between the predicted wheel torque and the required wheel torque based on the difference between the recognition accuracy and the environmental interference degree; wherein, the weighting coefficient is used to determine the distribution ratio of the predicted wheel torque and the required wheel torque in the torque matching result calculation process; Decision determination module: used to determine the torque matching result of the vehicle based on the difference between the predicted torque of the wheel and the required torque of the wheel, and to obtain the control parameters of the vehicle in the next time period based on the torque matching result; Control execution module: used to adjust the driving state of the vehicle using the control parameters.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the vehicle control method according to any one of claims 1 to 7.

Images