Driving assistance method, device and equipment

Abstract

The embodiment of the invention relates to a driving assistance method, device and equipment, which can realize a detection array of an intelligent surface of an automobile to wind pressure and correction compensation of the intelligent surface to intelligent driving, so that an automobile body is kept in a stable state, and meanwhile, a driver can know the wind direction / wind pressure change of an environment outside the automobile in time; therefore, intervention or stop driving can be adopted when the condition is worsened, and the driving safety is ensured; through automatic correction of intelligent driving, severe speed fluctuation caused by increase of front wind resistance is avoided, so that energy consumption is reduced, and the driving cruising ability is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of automotive electronics technology, specifically to a driving assistance method, device, and equipment. Background Technology

[0002] In the field of contemporary automotive technology, the sensing capabilities of smart surfaces are becoming increasingly sophisticated, encompassing a variety of technologies such as touch sensing, proximity sensing, force feedback, ambient light sensing, biometrics, gesture recognition, status monitoring, and health analysis. These technologies are widely applied in automotive interiors and control interfaces to enhance the vehicle's interactive experience and safety. Meanwhile, intelligent driving systems rely on sensors such as cameras and radar to collect real-time information about the external environment, adjusting driving strategies and states to achieve autonomous driving functions. However, these traditional sensing methods still face recognition difficulties in some complex scenarios, particularly in effectively addressing the impact of dynamic aerodynamic changes. Vehicles often face significant challenges from wind resistance during operation, including strong longitudinal winds from the front and crosswinds from the sides. Crosswinds may originate from natural strong winds or momentary strong winds caused by large vehicles passing at high speeds on the side; these factors can disrupt vehicle stability, leading to skidding or drifting, greatly increasing safety risks. Strong longitudinal winds, on the other hand, can cause fluctuations in the powertrain, resulting in unstable speed and increased energy consumption.

[0003] Existing intelligent driving systems are mainly based on visual and distance perception data, lacking the ability to monitor airflow in real time. As a result, they cannot adjust speed or direction control angles in a timely manner to counteract wind resistance interference. This limits the performance and reliability of the system in adverse weather or complex road conditions, and there is an urgent need for an innovative solution that can integrate aerodynamic perception to make up for this deficiency. Summary of the Invention

[0004] In view of the above problems, this invention provides a driving assistance method, device, and equipment to solve the problem that existing intelligent driving systems mainly rely on visual and distance perception data and lack the ability to monitor airflow in real time, thus failing to adjust speed or direction control angles in a timely manner to counteract wind resistance interference.

[0005] According to one aspect of the present invention, a driving assistance method is provided, the method comprising: Real-time acquisition of wind pressure array data from any one or more surfaces of a vehicle; The wind pressure array data is converted into a point cloud map, and the vehicle's vector drag is calculated based on the point cloud map. Based on the vector drag, the target compensation value is calculated and obtained through a preset vehicle power mode; The vehicle driving assistance is adjusted based on the target compensation value.

[0006] In some alternative implementations, the wind pressure array data is collected by wind pressure sensors located at one or more of the front, rear, left, right, upper, and lower surfaces of the vehicle.

[0007] In some alternative implementations, the wind pressure array data is converted into a point cloud map, and the vehicle's vector drag is calculated based on the point cloud map, specifically including: Obtain the pressure value, normal vector, and micro-area of ​​each wind pressure sensor; calculate the force vector at a single point based on the pressure value, normal vector, and micro-area. Multiple single-point force vectors are obtained, and the multiple single-point force vectors are summed to obtain the vector wind resistance, wherein the vector wind resistance includes longitudinal wind resistance, lateral wind resistance and vertical wind resistance.

[0008] In some alternative implementations, the target compensation value includes at least one or more of the power output compensation value and the deflection angle compensation value.

[0009] In some alternative implementations, the target compensation value is calculated based on the vector drag using a preset vehicle power mode, specifically including: Drag acceleration is obtained through the longitudinal drag of vector drag. The power output compensation value is obtained by calculating the negative of the wind resistance acceleration, wherein the target compensation value is the power output compensation value.

[0010] In some alternative implementations, the vehicle driving assistance is adjusted based on the target compensation value, specifically including: If the wind resistance acceleration exceeds a preset first safety threshold, the vehicle's power output is adjusted using a power output compensation value; otherwise, no action is taken.

[0011] In some alternative implementations, the target compensation value is calculated based on the vector drag using a preset vehicle power mode, specifically including: The following parameters were collected: front wheel lateral stiffness Cf, wheelbase L, distance a from the front axle to the center of gravity, height of the lateral force of wind resistance dy, longitudinal wind resistance Fmx, and lateral wind resistance Fmy. Lateral force compensation is calculated using the formula (-Fmy) / (2 * Cf); The yaw moment compensation is calculated using the formula (Fmy * dy) / (2 * Cf * a + (-Fmx) * L / 2). The angle change value is obtained by calculating the sum of lateral force compensation and yaw moment compensation; The target compensation value is the angle change value.

[0012] In some alternative implementations, the vehicle driving assistance is adjusted based on the target compensation value, specifically including: Determine whether the angle change value exceeds a preset second safety threshold. If so, adjust the vehicle's driving angle based on the angle change value; otherwise, do not perform any action.

[0013] According to another aspect of the present invention, a driving assistance device is provided, the device comprising: The data acquisition module is used to collect wind pressure array data from any one or more surfaces of the vehicle in real time. The data processing module is used to convert the wind pressure array data into a point cloud map and calculate the vehicle's vector drag based on the point cloud map. The compensation calculation module is used to calculate and obtain the target compensation value based on the vector wind resistance and a preset vehicle power mode. And an adjustment module, used to adjust the vehicle driving assistance according to the target compensation value.

[0014] According to another aspect of the present invention, a driving assistance device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform operations such as the driving assistance method described above.

[0015] This invention provides a driving assistance method, device, and equipment, with the following advantages: The invention collects wind pressure array data from any one or more surfaces of a vehicle in real time; converts the wind pressure array data into a point cloud map, and calculates the vehicle's vector drag based on the point cloud map; calculates a target compensation value based on the vector drag and a preset vehicle power mode; and adjusts the vehicle's driving assistance based on the target compensation value. Through this method, the invention can realize the detection array of wind pressure on the vehicle's intelligent surfaces, and the correction and compensation of intelligent driving by these intelligent surfaces, thereby maintaining the vehicle's stability. It also allows the driver to be aware of changes in wind direction / pressure in the external environment, enabling intervention or stopping the vehicle when conditions worsen, ensuring driving safety. Furthermore, the automatic correction by intelligent driving avoids drastic speed fluctuations caused by increased wind resistance, thereby reducing energy consumption and improving driving range.

[0016] The above description is merely an overview of the technical solutions of this invention. In order to better understand the technical means of the embodiments of this invention, it can be implemented in accordance with the contents of the specification. Furthermore, in order to make the above and other objects, features and advantages of the embodiments of this invention more apparent and understandable, specific embodiments of this invention are described below. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic flowchart of the driving assistance method according to Embodiment 1 of the present invention is shown; Figure 2 A flowchart illustrating step 120 of Embodiment 1 of the present invention is shown; Figure 3 This invention provides a first flowchart illustrating step 130 of Embodiment 1. Figure 4 This invention provides a second flowchart illustrating step 130 of Embodiment 1. Figure 5 A schematic diagram of the driving assistance device according to Embodiment 2 of the present invention is shown; Figure 6 A schematic diagram of the structure of the driving assistance device according to Embodiment 3 of the present invention is shown. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0019] Example 1: Figure 1 This illustration shows an embodiment of a driving assistance method according to the present invention, which addresses the problem that existing intelligent driving systems, primarily based on visual and distance perception data, lack real-time monitoring capabilities of airflow, thus failing to adjust speed or direction control angles in a timely manner to counteract wind resistance interference. The method includes: 110, Real-time acquisition of wind pressure array data from any one or more surfaces of the vehicle; 120. Convert the wind pressure array data into a point cloud map, and calculate the vehicle's vector drag based on the point cloud map; 130. Based on the vector drag, the target compensation value is calculated and obtained through the preset vehicle power mode. 140. Adjust the vehicle's driving assistance based on the target compensation value.

[0020] In steps 110-140, the wind pressure array data refers to the pressure information collected by sensor groups distributed at different locations on the vehicle body. Specifically, this can be implemented using a microelectromechanical system (MEMS) pressure sensor array, with each sensor node measuring the pressure value of its corresponding area. The point cloud map refers to a spatial model formed by mapping discrete pressure data to a three-dimensional coordinate system. Specifically, this can be generated by associating sensor positions with pressure values ​​using coordinate transformation algorithms. Vector drag refers to the three-dimensional vector of air resistance calculated through a pressure distribution model. Specifically, this can be obtained by vector integration of the pressure value, effective area, and normal vector of each sensor region. The target compensation value refers to the amount of control parameter adjustment required to counteract the influence of wind drag. Specifically, this can be achieved by converting the wind drag vector into a powertrain output correction amount based on the vehicle dynamics model.

[0021] Specifically, a pressure sensor array continuously collects pressure distribution data on the vehicle surface. The position coordinates and pressure values ​​of each sensor are input into a point cloud generation algorithm to construct a three-dimensional pressure distribution model. By calculating the product of the pressure and the area of ​​effect of each sensor region, combined with the surface normal vector direction, the force vector of each micro-element region is obtained. Spatial integration is performed on all micro-element vectors to obtain the longitudinal, lateral, and vertical wind resistance components experienced by the vehicle. Based on the current vehicle driving mode, the wind resistance components are input into the powertrain control model to calculate the required driving force or steering angle correction. Finally, the compensation parameters are input into the electronic stability program or electronic steering system to achieve dynamic adjustment of the driving state.

[0022] Compared to existing technologies, traditional solutions rely solely on visual obstacle recognition or inertial sensors to detect changes in vehicle posture, resulting in response lag. This invention directly monitors the source of aerodynamic forces, calculating compensation parameters the instant wind resistance is generated, allowing the control system to intervene earlier. Furthermore, vector wind resistance calculation can distinguish forces acting in different directions, providing precise input for multi-dimensional control, whereas traditional methods cannot differentiate between the effects of crosswinds and longitudinal winds.

[0023] Through the above technical solution, this application can maintain the stability of the vehicle's driving trajectory in strong wind environments and effectively suppress deviation caused by lateral wind forces. At the same time, by compensating for longitudinal wind resistance in real time, it maintains a dynamic balance between power output and driving resistance, reduces vehicle speed fluctuations caused by sudden changes in wind speed, and improves energy utilization efficiency.

[0024] In step 110, wind pressure array data is collected by wind pressure sensors located at one or more of the front, rear, left, right, upper, and lower surfaces of the vehicle.

[0025] In this embodiment of the invention, a wind pressure sensor is a device capable of measuring the pressure exerted by gas or fluid on the surface of an object. Specifically, it can be implemented using a piezoelectric sensor, a capacitive sensor, or a piezoresistive sensor, and its function is to convert the wind pressure on different surfaces into an electrical signal. Furthermore, the wind pressure sensor can also be a sensor array capable of detecting wind pressure, such as a pitot tube or a barometer.

[0026] Among them, the front surface, rear surface, left surface, right surface, upper surface and lower surface refer to the six main outer surface areas covered by the vehicle shell. Specifically, this can be achieved by installing sensors at the front, rear, doors, roof and chassis of the vehicle. Their function is to capture multi-dimensional wind pressure changes by covering the stress areas of different directions of the vehicle through multi-point distribution.

[0027] Specifically, wind pressure sensors are deployed in one or more key areas on the vehicle's exterior surface, such as the front bumper, rearview mirrors, roof, or side skirts. As the vehicle moves, the differences in wind pressure experienced by different surfaces are collected in real time by the sensors, forming a wind pressure array data covering the entire vehicle or a specific area. For example, in crosswind scenarios, sensors on the left and right surfaces can detect the wind pressure difference between the two sides, while in strong longitudinal wind scenarios, sensors on the front and rear surfaces can capture changes in the front-to-rear wind pressure gradient. By selectively deploying sensors on specific surfaces, redundant data interference can be avoided, and the monitoring range can be flexibly adjusted for different driving conditions.

[0028] Through the above technical solution, this application can dynamically select the optimal sensor layout according to the vehicle driving status and external environment, effectively improve the comprehensiveness and pertinence of wind pressure data acquisition, provide high-precision input data for subsequent vector drag calculation, and thus enhance the driving assistance system's response capability to aerodynamic interference.

[0029] In step 120, the wind pressure array data is converted into a point cloud map, and the vehicle's vector drag is calculated based on the point cloud map. See [link to relevant documentation]. Figure 2 Specifically, it includes: 210. Obtain the pressure value, normal vector, and micro-area of ​​each wind pressure sensor; calculate the single-point force vector based on the pressure value, normal vector, and micro-area. 220. Obtain multiple single-point force vectors, and sum the multiple single-point force vectors to obtain vector wind resistance, where vector wind resistance includes longitudinal wind resistance, lateral wind resistance and vertical wind resistance.

[0030] In steps 210-220, the pressure value refers to the airflow pressure at the location of the wind pressure sensor, which can be implemented using a piezoelectric or capacitive sensor to quantify the local wind pressure intensity. The normal vector is the vertical vector of the surface area where the sensor is located, which can be determined using pre-calibrated three-dimensional coordinate system data or surface curvature algorithms, and is used to define the direction of force. The micro-element area refers to the surface area corresponding to a single wind pressure sensor, which can be calculated using sensor layout density and surface topology, and is used to convert pressure into force. The single-point force vector is the vector generated by multiplying the pressure value, normal vector, and micro-element area, which can be implemented using vector operations, and is used to characterize the aerodynamic effect at a single sensor location. Vector drag is the vector sum of all single-point force vectors, which can be implemented using a spatial vector superposition algorithm, and is used to decompose the aerodynamic components of the vehicle in three-dimensional space.

[0031] In a specific example, steps 210-220 can be implemented in the following way.

[0032] Establish a global coordinate system for the vehicle, where the origin is the geometric center of the vehicle's cuboid, x represents the wind direction (i.e., the direction of incoming flow), y represents the crosswind direction, and z represents the vertical direction.

[0033] To obtain key parameters, the vehicle has rectangular dimensions Lx*Ly*Lz (length*width*height), and there are N sensors evenly distributed across the vehicle's surfaces. The data for the i-th sensor includes: position ri = (xi, yi, zi), pressure pi, and normal vector. , micro-area dAi; The formula for calculating the force vector at a single point, i.e., the infinitesimal force generated by pressure on the surface, is as follows: A negative sign indicates that the direction of the pressure is opposite to the outward normal vector of the surface. =pi* *dAi; The total wind force vector is calculated by using multiple single-point force vectors: The resultant force component is ; The cuboid surface grouping calculation method can be used as follows: For the front surface of the vehicle, the normal vector =[1,0,0], area Af=Ly*Lz, the contribution of the resultant force components is F(x,f)=- ,in, This refers to the total wind pressure on the front surface of the vehicle. This refers to the micro-area of ​​wind pressure on the front surface of the vehicle.

[0034] For the rear surface of the vehicle, the normal vector =[-1,0,0], area Ab=Ly*Lz, the contribution of the resultant force components is F(x,b)= ,in, This refers to the total wind pressure on the rear surface of the vehicle. This represents the micro-area of ​​wind pressure on the rear surface of the vehicle.

[0035] For the left surface of the vehicle, the normal vector =[0,-1,0], area Af=Lx*Lz, the contribution of the resultant force components is F(y,l)= ,in, This refers to the wind pressure on the left surface of the vehicle. This represents the micro-area of ​​wind pressure on the left surface of the vehicle.

[0036] For the right surface of the vehicle, the normal vector =[0,1,0], area Af=Ly*Lz, the contribution of the resultant force components is F(y,r)=- ,in, This refers to the wind pressure on the right surface of the vehicle. This represents the micro-area of ​​wind pressure on the right surface of the vehicle.

[0037] For the upper surface of the vehicle, the normal vector =[0,0,1], area Af=Lx*Ly, the contribution of the resultant force components is F(z,t)=- ,in, This refers to the wind pressure on the entire upper surface of the vehicle. This refers to the micro-area of ​​wind pressure on the upper surface of the vehicle.

[0038] For the lower surface of the vehicle, the normal vector =[0,0,-1], area Af=Lx*Ly, the contribution of the resultant force components is F(x,d)= ,in, This refers to the wind pressure across the entire lower surface of the vehicle. This refers to the micro-area of ​​wind pressure on the lower surface of the vehicle.

[0039] The total resultant force component is Vector drag Fm=[Fx,Fy,Fz].

[0040] As can be seen, the pressure data collected by the wind pressure sensor array is mapped onto a 3D point cloud model, with each sensor node corresponding to a spatial coordinate point. By reading the pressure value of each node, the pre-stored normal vector parameters, and the corresponding micro-element area parameters, the instantaneous force vector of each node is calculated using vector multiplication. For example, when the sensor is placed on the surface of the vehicle's front bumper, its normal vector points directly forward, and the micro-element area is dynamically adjusted according to the shape of the bumper surface. After spatial superposition of the force vectors of all nodes, a total wind resistance vector containing longitudinal, lateral, and vertical components is generated. Thus, when a vehicle encounters crosswinds, the lateral wind resistance component can be accurately calculated, providing a data foundation for subsequent compensation control.

[0041] Through the above technical solution, this application can dynamically identify the three-dimensional aerodynamic distribution of different areas on the vehicle surface, providing high-precision drag vector data for the driver assistance system. For example, in strong crosswind scenarios, the lateral drag component can be calculated in real time and input into the steering compensation algorithm, thereby actively correcting the steering wheel angle to counteract lateral deviation; in longitudinal headwind scenarios, the powertrain can dynamically adjust torque output according to the longitudinal drag component to maintain vehicle speed stability. This effectively solves the control lag problem caused by the lack of real-time aerodynamic perception in traditional systems.

[0042] In step 130, the target compensation value includes at least one or more of the power output compensation value and the deflection angle compensation value.

[0043] In this embodiment of the invention, the power output compensation value refers to a numerical value used to adjust the output power or torque of the vehicle's power system. Specifically, it can be achieved by calculating the inverse of the wind resistance acceleration, for example, by inversely superimposing the acceleration caused by longitudinal wind resistance onto the power system to counteract the wind resistance effect. The yaw angle compensation value refers to a numerical value used to correct the vehicle's driving direction by adjusting the steering angle. Specifically, it can be achieved by superimposing the lateral force compensation and yaw moment compensation, for example, by adjusting the front wheel steering angle to balance the lateral offset caused by lateral wind resistance.

[0044] Specifically, in longitudinal drag scenarios, the power output compensation value maintains vehicle speed stability by counteracting wind resistance acceleration. For example, when a vehicle encounters strong headwinds, the powertrain automatically increases output power to counteract drag. In lateral drag scenarios, the yaw angle compensation value compensates for lateral deviation by adjusting the front wheel steering angle. For example, when a vehicle is affected by crosswinds, the steering system actively corrects the driving trajectory to avoid skidding. These two compensation values ​​can be applied independently or in combination; for example, under combined drag conditions, power output and steering angle can be adjusted simultaneously to achieve multi-dimensional control.

[0045] Through the above technical solution, this application solves the problem of vehicle power fluctuation and driving trajectory deviation caused by aerodynamic interference in the prior art. It maintains longitudinal speed stability by power output compensation value and corrects lateral driving direction by deflection angle compensation value, effectively reducing safety risks in strong wind environment and improving energy consumption efficiency.

[0046] In one embodiment of step 130, based on vector drag and a preset vehicle power mode, a target compensation value is calculated and obtained, see [link to relevant documentation]. Figure 3 Specifically, it includes: 310, wind resistance acceleration is obtained through the longitudinal wind resistance of vector wind resistance; 320, the power output compensation value is obtained by calculating the negative of the wind resistance acceleration, where the target compensation value is the power output compensation value.

[0047] In steps 310-320, the longitudinal drag of the vector wind resistance refers to the air resistance component of the vehicle in the driving direction. Specifically, it can be obtained by combining the pressure data collected by the wind pressure sensor with the area of ​​the micro-element and the normal vector to calculate the single-point force vector and then summing them up. This is used to characterize the magnitude of the longitudinal air resistance experienced by the vehicle.

[0048] Among them, wind resistance acceleration refers to the change in vehicle acceleration caused by longitudinal wind resistance. Specifically, it can be calculated by dividing the longitudinal wind resistance by the vehicle mass and is used to quantify the dynamic impact of wind resistance on the vehicle's power system.

[0049] Among them, the power output compensation value refers to the adjustment parameter used to counteract wind resistance acceleration. Specifically, it can be achieved by taking the opposite of the wind resistance acceleration and superimposing it on the current power output to maintain the stability of the vehicle's driving speed.

[0050] Specifically, during vehicle operation, wind pressure sensors collect longitudinal wind resistance data in real time. By calculating the ratio of longitudinal wind resistance to vehicle mass, wind resistance acceleration is obtained. Subsequently, the inverse of the wind resistance acceleration is used as a power output compensation value and directly added to the vehicle's power control system. For example, when longitudinal wind resistance causes the vehicle to decelerate, the power output compensation value will correspondingly increase the power output to offset the acceleration change caused by wind resistance and maintain a stable vehicle speed.

[0051] Through the above technical solution, this application can offset the power fluctuations caused by longitudinal wind resistance in real time, effectively maintain the stability of vehicle speed, avoid frequent adjustments of the power system caused by strong wind interference, thereby reducing energy loss and improving driving smoothness.

[0052] In one embodiment of step 140, the vehicle driving assistance is adjusted according to the target compensation value, specifically including: determining whether the wind resistance acceleration exceeds a preset first safety threshold; if so, adjusting the vehicle's power output through the power output compensation value; otherwise, no processing is performed.

[0053] In the above implementation, wind resistance acceleration refers to the change in vehicle acceleration caused by wind resistance. Specifically, it can be calculated by combining wind pressure data collected by a pressure sensor with vehicle mass parameters, and is used to characterize the degree of interference of external wind force on the longitudinal movement of the vehicle. The power output compensation value refers to the amount of power adjustment used to offset the effect of wind resistance acceleration. Specifically, it can be achieved through motor torque compensation or engine power compensation, and its value is positively correlated with the absolute value of wind resistance acceleration. The first safety threshold is a pre-set critical value for acceleration change, which can be dynamically adjusted according to vehicle type, driving speed range, or environmental conditions, and is used to determine whether the power compensation mechanism is triggered.

[0054] Specifically, the longitudinal drag component is obtained by calculating the longitudinal drag after real-time wind pressure data is collected during vehicle operation. Combined with vehicle mass parameters, the drag acceleration can be derived. The system compares the real-time drag acceleration with a preset first safety threshold. If it exceeds this threshold, it indicates that wind interference has reached a level affecting driving stability. In this case, the acceleration fluctuation is offset by reverse compensation of power output. For example, when a positive drag acceleration is detected and exceeds the threshold, the control unit sends a command to the power system to increase torque output; if it does not exceed the threshold, the current power state is maintained to avoid energy loss caused by frequent fine-tuning.

[0055] Through the above technical solution, this application can automatically maintain the stability of vehicle power output in strong wind environment, effectively prevent vehicle stalling or abnormal acceleration caused by sudden changes in longitudinal wind resistance, and avoid excessive system intervention through threshold judgment mechanism to ensure the balance between energy utilization efficiency and driving smoothness.

[0056] In another embodiment of step 130, the target compensation value is calculated and obtained based on the vector drag and a preset vehicle power mode, see [link to relevant documentation]. Figure 4 Specifically, it includes: 410, collect the vehicle's front wheel lateral stiffness Cf, wheelbase L, distance a from the front axle to the center of gravity a, height of the lateral force of wind resistance dy, longitudinal wind resistance Fmx and lateral wind resistance Fmy; 420, the lateral force compensation is obtained by calculating (-Fmy) / (2 * Cf); 430, the yaw moment compensation is calculated using the formula (Fmy * dy) / (2 * Cf * a + (-Fmx) * L / 2); 440, the angle change value is obtained by calculating the sum of lateral force compensation and yaw moment compensation; In steps 410-440, the target compensation value is the angle change value. The front wheel lateral stiffness Cf refers to the lateral force corresponding to a unit lateral angle generated by the tire under lateral force, which can be obtained through tire mechanical property testing or real-time sensor measurement, used to quantify the tire's response to lateral forces. The wheelbase L refers to the distance between the centers of the front and rear wheels of the vehicle, which can be obtained through vehicle design parameters, used to construct the geometric relationships of the vehicle dynamics model. The distance a from the front axle to the center of gravity a refers to the horizontal distance from the vehicle's center of gravity to the front axle, which can be obtained through mass distribution calculation or inertial measurement unit, used to determine the location of the lateral force application point. The lateral drag force application height dy refers to the vertical distance of the lateral drag line of action relative to the vehicle's center of gravity, which can be obtained through wind tunnel testing or spatial coordinate calculation using a pressure sensor array, used to evaluate the impact of drag torque. The longitudinal drag Fmx and lateral drag Fmy represent the drag components along the vehicle's transverse and longitudinal directions, respectively, which can be obtained through vector decomposition calculations using data collected by the wind pressure sensor array, used to characterize the degree of interference of drag on vehicle attitude.

[0057] Specifically, during vehicle operation, when lateral wind resistance is detected, real-time vehicle dynamics parameters and wind resistance component data are collected. The lateral wind resistance Fmy is substituted into the formula (-Fmy) / (2 * Cf) to calculate the lateral force compensation value. This value reflects the additional steering angle required to counteract tire lateral deflection caused by wind resistance. Simultaneously, based on the lateral force application height dy, wheelbase L, and distance a from the front axle to the center of gravity, the yaw moment compensation value is calculated using the formula (Fmy * dy) / (2 * Cf * a + (-Fmx) * L / 2). This value corrects the vehicle yaw angle deviation caused by wind resistance moment. The two compensation values ​​are then superimposed to generate an angle change value. If this value exceeds a preset safety threshold, the steering system dynamically adjusts the wheel deflection angle to counteract the impact of lateral wind resistance on the vehicle's trajectory.

[0058] Through the above technical solution, this application can calculate the combined angle value of tire side slip compensation and yaw moment compensation in real time when a vehicle encounters strong crosswinds, and determine whether to trigger steering adjustment based on a safety threshold. For example, in a highway overtaking scenario, when a large vehicle in an adjacent lane passes by and generates a transient crosswind, the system can quickly generate an angle change command, causing the vehicle to automatically and slightly adjust the front wheel steering angle to maintain its driving trajectory and avoid the risk of vehicle deviation or loss of control due to sudden changes in wind pressure.

[0059] In another embodiment of step 140, the vehicle driving assistance is adjusted according to the target compensation value, specifically including: determining whether the angle change value exceeds a preset second safety threshold; if so, adjusting the vehicle's driving angle based on the angle change value; otherwise, no processing is performed.

[0060] In the above implementation, the processor refers to the hardware unit that executes calculation and control instructions, specifically a multi-core central processing unit or an embedded microcontroller, used to parse wind pressure array data and generate vector drag calculation results. The memory refers to the physical medium that stores program instructions and data, specifically flash memory or dynamic random access memory, used to store the raw data collected by the wind pressure sensor and compensation algorithm parameters. The communication interface refers to the module that enables data transmission between devices, specifically a CAN bus interface or an Ethernet interface, used to receive wind pressure sensor signals and send adjustment commands to the vehicle actuators. The communication bus refers to the physical channel connecting the various hardware components, specifically a serial bus or a parallel bus, used to ensure data synchronization between the processor, memory, and communication interface.

[0061] Specifically, during equipment operation, data collected by the wind pressure sensor is transmitted to the processor via a communication interface. The processor converts the wind pressure array into a point cloud map according to a preset algorithm and calculates the vector drag. It then calls upon the vehicle power mode parameters stored in the memory to calculate the target compensation value. For example, when the lateral drag exceeds a threshold, the processor sends the angle change value to the steering control module via the communication bus, driving the electric power steering system to make real-time corrections. During this process, the communication bus ensures low-latency data transmission between multiple hardware units, while the safety threshold parameters stored in the memory prevent erroneous adjustments due to momentary interference.

[0062] Through the above technical solution, this application solves the problem of insufficient control precision caused by the lack of aerodynamic perception in existing driving systems. It achieves active compensation for longitudinal and lateral wind resistance through hardware collaboration, effectively reducing the risk of sideslip and power fluctuations of vehicles in strong wind environments, and improving driving stability under complex road conditions.

[0063] Example 2: Figure 5 An embodiment of a driving assistance device 500 is shown, which includes a data acquisition module 510, a data processing module 520, a compensation calculation module 530, and an adjustment module 540. The data acquisition module 510 is used to perform step 110 in embodiment 1.

[0064] The data processing module 520 is used to execute step 120 in embodiment 1.

[0065] The compensation calculation module 530 is used to perform step 130 in embodiment 1.

[0066] And adjustment module 540, used to perform step 140 in embodiment 1.

[0067] In steps 110-140, the wind pressure array data refers to the pressure information collected by sensor groups distributed at different locations on the vehicle body. Specifically, this can be implemented using a microelectromechanical system (MEMS) pressure sensor array, with each sensor node measuring the pressure value of its corresponding area. The point cloud map refers to a spatial model formed by mapping discrete pressure data to a three-dimensional coordinate system. Specifically, this can be generated by associating sensor positions with pressure values ​​using coordinate transformation algorithms. Vector drag refers to the three-dimensional vector of air resistance calculated through a pressure distribution model. Specifically, this can be obtained by vector integration of the pressure value, effective area, and normal vector of each sensor region. The target compensation value refers to the amount of control parameter adjustment required to counteract the influence of wind drag. Specifically, this can be achieved by converting the wind drag vector into a powertrain output correction amount based on the vehicle dynamics model.

[0068] Through the above technical solution, this application can maintain the stability of the vehicle's driving trajectory in strong wind environments and effectively suppress deviation caused by lateral wind forces. At the same time, by compensating for longitudinal wind resistance in real time, it maintains a dynamic balance between power output and driving resistance, reduces vehicle speed fluctuations caused by sudden changes in wind speed, and improves energy utilization efficiency.

[0069] Example 3: Figure 6 The diagram shows a structural schematic of one embodiment of the driving assistance device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the driving assistance device.

[0070] like Figure 6 As shown, the driving assistance device may include: a processor, a communications interface, a memory, and a communication bus.

[0071] The processor 610, communication interface 640, and memory 620 communicate with each other via communication bus 630. The communication interface is used to communicate with other devices, such as clients or gateways for other servers. The processor executes program 650, specifically performing the relevant steps described above in the driving assistance method embodiment.

[0072] Specifically, a program may include program code, which includes computer-executable instructions.

[0073] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The driver assistance device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0074] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0075] The program can be called by the processor to enable the driver assistance system to execute. Figure 1 Steps 110-140.

[0076] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0077] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0078] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0079] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A driving assistance method, characterized in that, The method includes: Real-time acquisition of wind pressure array data from any one or more surfaces of a vehicle; The wind pressure array data is converted into a point cloud map, and the vehicle's vector drag is calculated based on the point cloud map. Based on the vector drag, the target compensation value is calculated and obtained through a preset vehicle power mode; The vehicle driving assistance is adjusted based on the target compensation value.

2. The driving assistance method according to claim 1, characterized in that, The wind pressure array data is collected by wind pressure sensors located at one or more of the front, rear, left, right, upper, and lower surfaces of the vehicle.

3. The driving assistance method according to claim 2, characterized in that, The wind pressure array data is converted into a point cloud map, and the vehicle's vector drag is calculated based on the point cloud map, specifically including: Obtain the pressure value, normal vector, and micro-area of ​​each wind pressure sensor; calculate the force vector at a single point based on the pressure value, normal vector, and micro-area. Multiple single-point force vectors are obtained, and the multiple single-point force vectors are summed to obtain the vector wind resistance, wherein the vector wind resistance includes longitudinal wind resistance, lateral wind resistance and vertical wind resistance.

4. The driving assistance method according to claim 3, characterized in that, The target compensation value includes at least one or more of the power output compensation value and the deflection angle compensation value.

5. The driving assistance method according to claim 4, characterized in that, Based on the vector drag, and through a preset vehicle power mode, the target compensation value is calculated and obtained, specifically including: Drag acceleration is obtained through the longitudinal drag of vector drag. The power output compensation value is obtained by calculating the negative of the wind resistance acceleration, wherein the target compensation value is the power output compensation value.

6. The driving assistance method according to claim 5, characterized in that, Based on the target compensation value, the vehicle's driving assistance is adjusted, specifically including: If the wind resistance acceleration exceeds a preset first safety threshold, the vehicle's power output is adjusted using a power output compensation value; otherwise, no action is taken.

7. The driving assistance method according to claim 4, characterized in that, Based on the vector drag, and through a preset vehicle power mode, the target compensation value is calculated and obtained, specifically including: The following parameters were collected: front wheel lateral stiffness Cf, wheelbase L, distance from front axle to center of gravity a, height of lateral force action dy, longitudinal drag Fmx, and lateral drag Fmy. Lateral force compensation is calculated using the formula (-Fmy) / (2 * Cf); The yaw moment compensation is calculated using the formula (Fmy * dy) / (2 * Cf * a + (-Fmx) * L / 2). The angle change value is obtained by calculating the sum of lateral force compensation and yaw moment compensation; The target compensation value is the angle change value.

8. The driving assistance method according to claim 7, characterized in that, Based on the target compensation value, the vehicle's driving assistance is adjusted, specifically including: If the angle change value exceeds a preset second safety threshold, the vehicle's driving angle is adjusted based on the angle change value; otherwise, no action is taken.

9. A driving assistance device, characterized in that, The device includes: The data acquisition module is used to collect wind pressure array data from any one or more surfaces of the vehicle in real time. The data processing module is used to convert the wind pressure array data into a point cloud map and calculate the vehicle's vector drag based on the point cloud map. The compensation calculation module is used to calculate and obtain the target compensation value based on the vector wind resistance and a preset vehicle power mode. And an adjustment module, used to adjust the vehicle driving assistance according to the target compensation value.

10. A driving assistance device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the driving assistance method as described in any one of claims 1-8.

Images