A vehicle crosswind risk early warning method, device, equipment and medium

Abstract

The application relates to the technical field of intelligent transportation, and discloses a vehicle crosswind risk early warning method, device, equipment and medium, wherein the method comprises the following steps: acquiring front road features and traffic environment perception features of a vehicle driving area, the front road features are used for representing characteristic identifiers of the vehicle driving area, and the traffic environment perception features are used for representing dynamic traffic elements perceived by the vehicle; based on the front road features and the traffic environment perception features, risk components matched with different crosswind risk dimensions of the vehicle are determined; risk wind amounts of different crosswind risk dimensions are combined to determine a crosswind risk degree of the vehicle in the vehicle driving area; and based on a comparison result of the crosswind risk degree and a preset risk degree threshold, a crosswind risk early warning level corresponding to the vehicle driving area is determined. The technical scheme provided by the application can reduce the correction operation burden of the driver, and improve the safety and comfort of driving.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation, and in particular to a method, device, equipment and medium for early warning of vehicle crosswind risk. Background Technology

[0002] Crosswinds are a significant factor affecting vehicle stability during driving, especially on open roads such as elevated roads, bridges, and highways. Sudden crosswinds can cause vehicles to veer off course or even lose control, posing a threat to driving safety.

[0003] However, current lateral wind control strategies are mostly passive response control, which intervenes only after the vehicle's state has been significantly affected. This results in problems such as delayed response, abrupt intervention, and interference with normal driving intentions. While maintaining vehicle stability, it is difficult to achieve a smooth driving experience and consistent power response.

[0004] Therefore, how to enhance vehicle stability under the influence of crosswinds, reduce the driver's corrective action burden, and improve driving safety and comfort has become a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for warning of vehicle crosswind risk, which solves the technical problem of how to enhance vehicle stability under the influence of crosswinds, reduce the driver's corrective operation burden, and improve driving safety and comfort.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include:

[0007] In a first aspect, embodiments of this application provide a method for warning of crosswind risk in vehicles, the method comprising:

[0008] The system acquires the road features ahead and traffic environment perception features of the vehicle's driving area. The road features ahead are used to characterize the feature markers indicating the presence of crosswind risk in the vehicle's driving area, and the traffic environment perception features are used to characterize the dynamic traffic elements perceived by the vehicle.

[0009] Based on the road ahead features and the traffic environment perception features, determine the risk components that match the different crosswind risk dimensions of the vehicle.

[0010] By combining the risk wind volume of different crosswind risk dimensions, a crosswind risk level suitable for the vehicle in the vehicle's driving area is determined.

[0011] Based on the comparison between the crosswind risk level and the preset risk level threshold, the crosswind risk warning level corresponding to the vehicle driving area is determined.

[0012] This embodiment provides a vehicle crosswind risk warning method. It constructs risk components matching different crosswind risk dimensions using acquired road features and traffic environment perception features. Then, it constructs the crosswind risk level of the vehicle's driving area based on the risk wind volume combination of different crosswind risk dimensions. Next, it determines the crosswind risk warning level corresponding to the driving area based on the comparison result between the crosswind risk level of the driving area and a preset risk level threshold. This process achieves multi-dimensional and forward-looking perception and warning of surrounding crosswind risks by acquiring road features and traffic environment perception features, improving vehicle driving safety under complex weather and road conditions. This method not only provides early warning of crosswind risks, allowing drivers to be psychologically prepared, but also avoids mechanically adopting a single response strategy when facing different crosswind risks by establishing crosswind risk warning levels corresponding to different vehicle driving areas, thus sacrificing vehicle handling and driving comfort, and ensuring a good driving experience while ensuring safety.

[0013] In one implementation, the risk component includes a wind condition component, which is determined as follows:

[0014] Based on the left and right boundary lines of the lanes in the traffic environment perception features, the absolute road orientation angle is determined.

[0015] The crosswind weight is determined based on the absolute value of the sine of the angle between the current wind direction and the absolute road orientation angle in the traffic environment perception features.

[0016] The wind condition component is determined by multiplying the current wind speed in the traffic environment perception features with the crosswind weight.

[0017] This embodiment determines the absolute road orientation angle by acquiring the left and right boundary lines. Then, it calculates the absolute sine of the angle between the current wind direction and this road orientation angle, using this as a quantification weight for the crosswind impact. Finally, it multiplies the real-time wind speed by this weight to obtain the final wind condition component. This method incorporates the relative influence of wind direction and road orientation into the calculation through geometric relationships, thereby more accurately characterizing the actual crosswind intensity acting on vehicles. It introduces a directly related component for crosswind risk.

[0018] In one embodiment, the absolute orientation angle of the road is determined as follows:

[0019] Based on the left and right boundary lines of the lane in the traffic environment perception features, determine the center line of the lane in the vehicle coordinate system;

[0020] The tangential direction between the vehicle at a designated location and the center line of the lane is determined as the local road orientation.

[0021] Determine the angle between the local road orientation and the longitudinal axis of the vehicle body, and define the angle as the road relative yaw angle;

[0022] Obtain the absolute yaw angle of the vehicle, and add the absolute yaw angle of the vehicle to the relative yaw angle of the road to obtain the absolute heading angle of the road.

[0023] This embodiment uses the sensed left and right lane boundary lines to fit the lane centerline in the vehicle coordinate system. Then, at a designated position in front of the vehicle, the tangent direction of this centerline is taken as the local road orientation. Next, the angle between this local orientation and the vehicle's longitudinal axis is calculated and defined as the relative yaw angle. Finally, the vehicle's current absolute yaw angle is added to this relative yaw angle to obtain the final absolute road orientation angle. This method, by calculating the absolute road orientation angle, introduces a crucial road geometric reference for wind condition components, thereby significantly improving the accuracy of crosswind risk assessment and road adaptability.

[0024] Secondly, embodiments of this application provide a vehicle crosswind risk warning device, the device comprising:

[0025] The feature acquisition unit is used to acquire the features of the road ahead and the traffic environment perception features of the vehicle's driving area. The road ahead features are used to characterize the feature markers indicating the presence of crosswind risk in the vehicle's driving area, and the traffic environment perception features are used to characterize the dynamic traffic elements perceived by the vehicle.

[0026] The component determination unit is used to determine risk components that match different crosswind risk dimensions of the vehicle based on the road features ahead and the traffic environment perception features.

[0027] The risk determination unit is used to combine the risk wind volume of different crosswind risk dimensions to determine the crosswind risk level that is suitable for the vehicle in the vehicle driving area.

[0028] The warning level determination unit is used to determine the crosswind risk warning level corresponding to the vehicle driving area based on the comparison result between the crosswind risk level and the preset risk level threshold.

[0029] Thirdly, embodiments of this application provide a computer device, including:

[0030] The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle crosswind risk warning method described above.

[0031] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the vehicle crosswind risk warning method described in any of the above claims. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating a vehicle crosswind risk warning method provided in this application embodiment;

[0034] Figure 2 A flowchart of step S111 provided in an embodiment of this application;

[0035] Figure 3 A flowchart of step S1111 provided in the embodiments of this application;

[0036] Figure 4 A flowchart of step S131 provided in an embodiment of this application;

[0037] Figure 5 A flowchart of step S151 provided in an embodiment of this application;

[0038] Figure 6 A flowchart of step S171 provided in an embodiment of this application;

[0039] Figure 7 A flowchart of step S191 provided in an embodiment of this application;

[0040] Figure 8 A vehicle crosswind risk warning device provided in this application embodiment;

[0041] Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] Currently, the control strategies for lateral wind disturbances in related technologies are generally passive and reactive. Their core logic is that compensation actions are only executed after the system detects a significant deviation in vehicle posture, leading to inherent problems such as control lag and a decline in driving experience. The limitations of these technologies are mainly reflected in the following three aspects:

[0044] Dynamic response-based braking intervention: ESP (Electronic Stability Program) monitors the vehicle's yaw rate and lateral acceleration in real time via an IMU (Inertial Measurement Unit). The system only applies braking force to individual wheels to generate a corrective yaw moment when vehicle stability has been compromised. The fundamental drawback of this approach is that compensation begins after instability has occurred. Furthermore, sudden braking introduces a jolt, directly impacting ride smoothness and comfort.

[0045] Simple powertrain intervention: Some strategies aim to reduce control complexity under crosswind conditions by directly reducing drive torque or even cutting off power when crosswinds are detected. While this method can mitigate risks to some extent, it can lead to abrupt interruptions in power output. In critical scenarios requiring precise power response, such as overtaking or merging onto highways, this may introduce new safety risks and impair the driving experience.

[0046] Reliance on specific hardware configurations: Some high-end solutions improve stability by actively lowering the suspension height to change the aerodynamic center. However, these solutions heavily rely on a fully adjustable suspension system, which is neither feasible nor economical for most mainstream models that do not have this hardware, resulting in extremely poor versatility.

[0047] In conclusion, how to enhance vehicle stability under the influence of crosswinds, reduce the driver's corrective action burden, and improve driving safety and comfort has become a technical problem that needs to be solved.

[0048] To address the aforementioned technical problems, an embodiment of a vehicle crosswind risk warning method is provided according to the present application. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0049] This embodiment provides a method for warning of crosswind risk to vehicles. Figure 1 A flowchart of a vehicle crosswind risk warning method provided in this application embodiment is shown below. Figure 1 As shown, the process includes the following steps:

[0050] Step S1: Obtain the road features ahead and traffic environment perception features of the vehicle's driving area. The road features ahead are used to characterize the feature markers indicating the presence of crosswind risk in the vehicle's driving area, and the traffic environment perception features are used to characterize the dynamic traffic elements perceived by the vehicle.

[0051] Specifically, the identification of road features ahead involves real-time acquisition of road images via an onboard camera, followed by real-time analysis and processing of the images using image processing and computer vision technologies. Key visual information is extracted from the image data to determine whether the features conform to preset non-fixed road characteristics, including but not limited to bridges, elevated sections, tunnel exits, and valley entrances. The acquisition of traffic environment perception features includes the following steps: First, wind speed and direction data are collected using environmental sensors, while simultaneously capturing road images using an onboard camera. Second, image processing technology is used to identify the left and right lane boundaries of the vehicle's lane, and the angle between the wind direction and the road orientation is calculated based on this boundary information. Furthermore, fixed traffic signs along the road, such as "Caution: Crosswind" warning signs, are identified using cameras, and this information is fused with sensor data to comprehensively assess the current traffic environment.

[0052] Step S3: Based on the characteristics of the road ahead and the traffic environment perception characteristics, determine the risk components that match the different crosswind risk dimensions of the vehicle.

[0053] Specifically, this embodiment constructs a multi-dimensional crosswind risk assessment system, whose core risk components include: wind condition components characterizing the impact of basic wind force, vehicle speed and road curvature components reflecting the dynamic interaction between vehicles and roads, environmental change components for responding to sudden disturbances, and key signage components for identifying high-risk road sections.

[0054] Step S5: Combine the risk wind volumes of different crosswind risk dimensions to determine the crosswind risk level suitable for the vehicle in the driving area.

[0055] Specifically, the risk wind volume for different crosswind risk dimensions includes wind condition components, vehicle speed and road curvature components, environmental abrupt change components, and sign components. The crosswind risk level is calculated by weighted summation:

[0056]

[0057] in Indicates wind condition component; This represents the vehicle speed and the road curvature component; Indicates the environmental mutation component; Indicates the flag component. , , , These calibrable parameters are typically determined through real-vehicle testing or simulation to meet certain requirements. .

[0058] Crosswind risk assessment integrates multi-dimensional crosswind risk factors to achieve a comprehensive evaluation of the impact of crosswinds, thereby effectively improving the forecasting ability and accuracy of early warnings.

[0059] Step S7: Based on the comparison results between the crosswind risk level and the preset risk level threshold, determine the crosswind risk warning level corresponding to the vehicle driving area.

[0060] Specifically, a multi-level crosswind risk warning mechanism is established. This mechanism dynamically classifies risk levels based on the comparison between the real-time calculated crosswind risk level and the preset threshold, thereby implementing differentiated and progressive response strategies for different levels of crosswind risk to achieve a balance between safety and comfort. At the same time, it avoids taking mechanical and singular response measures due to a simple comparison between the crosswind risk level and the preset threshold, which would affect driving smoothness and user experience.

[0061] This embodiment provides a vehicle crosswind risk warning method. It constructs risk components matching different crosswind risk dimensions using acquired road features and traffic environment perception features. Then, it constructs the crosswind risk level of the vehicle's driving area based on the risk wind volume combination of different crosswind risk dimensions. Next, it determines the crosswind risk warning level corresponding to the driving area based on the comparison result between the crosswind risk level of the driving area and a preset risk level threshold. This process achieves multi-dimensional and forward-looking perception and warning of surrounding crosswind risks by acquiring road features and traffic environment perception features, improving vehicle driving safety under complex weather and road conditions. This method not only provides early warning of crosswind risks, allowing drivers to be psychologically prepared, but also avoids mechanically adopting a single response strategy when facing different crosswind risks by establishing crosswind risk warning levels corresponding to different vehicle driving areas, thus sacrificing vehicle handling and driving comfort, and ensuring a good driving experience while ensuring safety.

[0062] Figure 2 A flowchart illustrating the construction method of wind condition components provided in this application embodiment, the process may include the following steps:

[0063] Step S111: Determine the absolute direction angle of the road based on the left and right boundary lines of the lanes in the traffic environment perception features.

[0064] Specifically, the absolute orientation angle of a road refers to the direction in which the lane centerline extends in a global coordinate system (e.g., with north as the 0° reference), and is usually expressed as the angle (0°~360°) with true north. As a key parameter for vehicle positioning, route planning, and high-precision map matching, this angle can be calculated based on geometric relationships using the perceived left and right boundary lines of the lane, thereby accurately depicting the actual direction of the lane centerline's extension in the global coordinate system.

[0065] Step S113: Determine the crosswind weight based on the absolute value of the sine of the angle between the current wind direction and the absolute road orientation angle in the traffic environment perception features.

[0066] Specifically, the absolute value of the sine of the angle between the current wind direction and the absolute road orientation is used as the weighting coefficient for the crosswind effect. This weight reflects the relative geometric relationship between the wind direction and the absolute road orientation, and is used to quantify the actual impact of crosswinds on the lateral stability of vehicles, providing key parameters for subsequent risk assessment.

[0067] Step S115: Multiply the current wind speed and crosswind weight in the traffic environment perception features to determine the wind condition component.

[0068] Specifically, the wind condition component is obtained by multiplying the current wind speed and the crosswind weight. :

[0069]

[0070] in, θ represents the current wind speed (m / s); θ is the angle between the current wind direction and the road orientation angle; the wind component quantifies the geometric relationship between the wind direction and the vehicle's travel path, directly determining the magnitude and direction of the lateral aerodynamic force. When the angle is close to 90 degrees (orthogonal crosswind), the lateral component of the wind force is the largest, causing the most severe interference with the vehicle's yaw and lateral displacement; the smaller or larger the angle is compared to 90 degrees, the smaller the effective lateral component after wind force decomposition. This angle is a key input for the chassis control system to calculate the actual lateral risk. Combined with wind speed and vehicle speed, it can accurately predict the lateral moment caused by the wind, thereby enhancing stability control in advance and maintaining the driving trajectory on road sections with frequent crosswinds, such as bridges and coastal highways.

[0071] This embodiment determines the absolute road orientation angle by acquiring the left and right boundary lines. Then, it calculates the absolute sine of the angle between the current wind direction and this road orientation angle, using this as a quantification weight for the crosswind impact. Finally, it multiplies the real-time wind speed by this weight to obtain the final wind condition component. This method incorporates the relative influence of wind direction and road orientation into the calculation through geometric relationships, thereby more accurately characterizing the actual crosswind intensity acting on vehicles. It introduces a directly related component for crosswind risk.

[0072] Figure 3 A flowchart illustrating the method for determining the absolute road orientation angle provided in this application embodiment, the process may include the following steps:

[0073] Step S1111: Determine the lane centerline in the vehicle coordinate system based on the left and right lane boundary lines in the traffic environment perception features.

[0074] Specifically, images of the road ahead are captured using an onboard camera, and the left and right boundary lines of the lane are identified and fitted based on a deep learning lane detection model. In the vehicle coordinate system, the lane centerline is further calculated and generated using a fixed pre-aiming distance in front as a reference.

[0075] Step S1113: Determine the tangential direction between the vehicle at the designated location and the center line of the lane as the local road direction.

[0076] Specifically, by extracting the tangent direction of the lane centerline at a fixed forward aiming point, for example, taking 30 meters in front of the vehicle's center point as the fixed forward aiming point, where the fixed aiming point is obtained through a camera, the local direction of the road is quantified into a continuous direction angle, describing the instantaneous extension direction of the road at that point, which is used to determine the road's relative yaw angle.

[0077] Step S1115: Determine the angle between the local road direction and the longitudinal axis of the vehicle body, and define the angle as the relative yaw angle of the road.

[0078] Specifically, it is obtained through the angle between the local road orientation and the longitudinal axis of the vehicle body. This angle directly represents the instantaneous deviation between the vehicle's current heading and the desired direction of the road, and it is an important parameter for obtaining the absolute heading angle of the road.

[0079] Step S1117: Obtain the vehicle's absolute yaw angle, add the vehicle's absolute yaw angle and the road's relative yaw angle to obtain the road's absolute heading angle.

[0080] Specifically, the vehicle's current absolute heading is obtained through the in-vehicle integrated navigation system (GNSS / IMU). This angle is based on geographical due north. The absolute heading angle of the road is obtained by adding the vehicle's absolute heading angle to the road's relative yaw angle.

[0081]

[0082] The road orientation angle will be compared with the real-time wind direction angle, and the difference between the two will be:

[0083]

[0084] It is used to assess the angle between the crosswind and the road, and is a key input for calculating the crosswind risk level.

[0085] For example, the vehicle's current absolute heading angle can be obtained through an in-vehicle integrated navigation system (GNSS / IMU). =45° (driving northeast). The camera detected a slight left curve in the lane markings at the pre-aiming point and calculated the relative yaw angle of the road. (The plus sign indicates that the road direction is to the left of the vehicle's heading). Therefore, the absolute angle of the road's orientation... If the real-time wind direction angle (Southeast wind), then the angle between the road direction angle and the real-time wind direction angle. It is close to vertical, with a high risk of crosswinds.

[0086] This embodiment uses the sensed left and right lane boundary lines to fit the lane centerline in the vehicle coordinate system. Then, at a designated position in front of the vehicle, the tangent direction of this centerline is taken as the local road orientation. Next, the angle between this local orientation and the vehicle's longitudinal axis is calculated and defined as the relative yaw angle. Finally, the vehicle's current absolute yaw angle is added to this relative yaw angle to obtain the final absolute road orientation angle. This method, by calculating the absolute road orientation angle, introduces a crucial road geometric reference for wind condition components, thereby significantly improving the accuracy of crosswind risk assessment and road adaptability.

[0087] Figure 4 A flowchart illustrating the method for constructing vehicle speed and road curvature components provided in this application embodiment, the process may include the following steps:

[0088] Step S131: Take the absolute value of the road curvature in the traffic environment perception features to obtain the curve sharpness weight.

[0089] Specifically, road curvature S is a digital parameter used to quantify the sharpness of a curve, calculated in real-time through image recognition or directly obtained from high-precision maps. Typically, the absolute value of the road curvature, |S|, is used as the weight for the curve's sharpness to objectively evaluate its shape. Distinguishing between sharp and gentle curves is crucial for driving safety. For example, a larger |S| value may indicate a sharp turn, while a smaller |S| value indicates a gentle curve.

[0090] Step S133: Multiply the current vehicle speed and the curve steepness weights in the traffic environment perception features to determine the vehicle speed and road curvature components.

[0091] Specifically, the vehicle speed and road curvature components are obtained by multiplying the current vehicle speed and the curve sharpness weights. :

[0092]

[0093] in Let |S| represent the current vehicle speed (m / s) and |S| represent the curve sharpness weight. The vehicle speed and road curvature components represent the dynamic coupling relationship between vehicle speed, curve sharpness weight, and crosswind stability: as the vehicle speed increases or the curve sharpness weight increases, the lateral force required for the vehicle to maintain its trajectory increases accordingly, and the vehicle speed and road curvature components also increase, thus reducing their stability against crosswind interference. Especially when passing through high-weight sharp curves at high speeds, the impact of crosswinds on the vehicle's lateral stability is significantly amplified, easily causing the vehicle to deviate from its expected trajectory. Therefore, the core function of this component is to quantify crosswind risk, providing a key decision-making basis for the control system to proactively compensate for crosswind effects when cornering at high speeds.

[0094] This embodiment demonstrates the sensitivity of a vehicle to crosswind interference while cornering by incorporating vehicle speed and road curvature components into the crosswind risk assessment. The higher the vehicle speed or the sharper the curve, the greater the lateral force required for the vehicle to maintain its intended trajectory. In this case, the impact of crosswinds on lateral stability is more significant, especially during high-speed cornering, where the vehicle is more prone to deviating from the expected path. Therefore, the core purpose of introducing this component is to quantitatively assess the instability risk caused by crosswinds, thereby improving the vehicle's trajectory-keeping ability and driving safety through pre-judgment.

[0095] Figure 5 A flowchart illustrating the construction method of the environmental mutation component provided in the embodiments of this application is shown. The process may include the following steps:

[0096] Step S151: Identify occlusion and abrupt change regions in the road features ahead.

[0097] Specifically, when vehicles travel to areas with abrupt terrain changes such as tunnel exits and valley entrances, the crosswind intensity increases sharply due to the combined effects of the "tunneling effect" and complex airflow interference. This not only causes sudden changes in aerodynamic loads and a sharp increase in wind speed, but also directly threatens the stability and safety of high-speed trains and road vehicles. Therefore, these areas with abrupt terrain changes are key safety hazard sections that require focused identification and control. Specifically, identification can be achieved by combining real-time sensor data with high-precision maps to update the current environmental conditions and identify potential areas with abrupt terrain changes.

[0098] Step S153: Determine the environmental mutation component based on the identified occlusion mutation region.

[0099] Specifically, the environmental mutation component:

[0100]

[0101] This embodiment incorporates environmental abrupt changes into the crosswind risk level, enabling more precise quantification of potential risks. This allows vehicles to proactively mitigate potential crosswind hazards and activate active prevention strategies accordingly, thereby improving driving stability and safety.

[0102] Figure 6 A flowchart illustrating the construction method of the flag component provided in this application embodiment, the process may include the following steps:

[0103] Step S171: Identify traffic sign information in the traffic environment perception features.

[0104] Specifically, wind warning signs include warning icons such as "Caution: Crosswinds" as well as temporary warning signs; wind warning signs provide vehicles with crucial crosswind forecast information by giving advance notice of the risk of strong crosswinds in specific road sections.

[0105] Step S173: Determine the sign component based on the identified traffic sign information.

[0106] Specifically, the flag components:

[0107]

[0108] This embodiment incorporates the marker component into the crosswind risk assessment system, which helps to more accurately identify road sections prone to crosswinds and improves the sensitivity and comprehensiveness of risk identification. This method effectively avoids missed detections caused by insufficient weight or low response of other assessment components, ensuring accurate early warnings for high-risk areas and thus enhancing the reliability of overall crosswind risk prediction.

[0109] Figure 7 A flowchart illustrating the construction method of the flag component provided in this application embodiment, the process may include the following steps:

[0110] Step S191: If the comparison result shows that the crosswind risk level is greater than or equal to the preset risk level threshold, the vehicle driving area is determined to be a high crosswind risk area.

[0111] Specifically, threshold It can adaptively adjust according to vehicle speed:

[0112]

[0113] in Based on the threshold, To adjust the coefficient, It can be obtained from an empirical formula. When the crosswind risk level... ≥ If the current area is determined to be a high-risk area for crosswinds, an "early warning state" will be immediately entered.

[0114] Step S193, map the difference between the crosswind risk degree and the preset risk degree threshold to the crosswind risk warning level corresponding to the high crosswind risk area.

[0115] Specifically, by calculating the difference between the crosswind risk degree and the preset risk degree threshold, an indicator indicating the crosswind risk situation can be obtained: , according to the calculated risk degree difference △R, it can be mapped to the specific crosswind risk warning level. Usually, a grading standard can be used to divide the crosswind risk warning level into multiple intervals, such as:

[0116] Low risk: (0 < <T1) (The crosswind risk degree is slightly higher than the preset risk degree threshold, but still within the acceptable range)

[0117] Medium risk: (T1 < <T2) (The crosswind risk degree is significantly higher than the preset risk degree threshold, and attention is required)

[0118] High risk: ( >T2) (The crosswind risk degree is extremely high, and immediate measures need to be taken)

[0119] Among them, T1 and T2 are critical values set according to the actual situation, which can effectively distinguish different risk levels.

[0120] Once the current risk warning level is determined, a fusion control instruction can be sent to each actuator of the vehicle:

[0121] The power system gradually adjusts the driver-requested torque according to the crosswind risk warning level. Specifically, the system will successively reduce the torque output by 5%, 10%, and 30% in proportion. This gentle torque suppression strategy can moderately reduce the vehicle speed and kinetic energy while ensuring the overall driving experience, thereby reducing the impact of wind load on vehicle stability. By avoiding sudden drops or interruptions in power, the system achieves a smooth and coherent control process, further ensuring driving safety and comfort.

[0122] The steering system control switches the steering mode to the "sport" or "stable" mode. In this mode, the system enhances the steering damping or increases the steering force feedback, enabling the driver to obtain a more solid and stable feel, effectively suppressing abnormal steering wheel shaking caused by crosswind interference, and thus enhancing the driver's precise perception and control of the vehicle driving trajectory.

[0123] The energy recovery system control switches the energy recovery mode to the "weakest" gear. Strong energy recovery will bring an obvious drag feeling, which is equivalent to an unstable longitudinal braking force and may exacerbate the vehicle's attitude imbalance in crosswinds. Weakening the recovery force can make the vehicle slide more smoothly and is conducive to maintaining stability.

[0124] In addition, there is an exit mechanism: once the inertial measurement unit signal stabilizes and no excessive yaw motion is detected for a certain period of time (e.g., 3-5 seconds), or the vehicle leaves the risk area or no longer enters the warning state, the vehicle automatically and smoothly restores all control modes to the user's original settings.

[0125] This embodiment calculates the difference between the crosswind risk level and a preset threshold in real time. The system dynamically assesses risk levels and classifies them into low, medium, and high warning levels. Based on these levels, the system performs integrated control of the powertrain, steering, and energy recovery systems: the powertrain gradually reduces torque by 5%, 10%, and 30% to smoothly decelerate; the steering system switches to "Sport" or "Stable" mode to enhance feel and stability; and energy recovery is adjusted to its weakest setting to avoid drag interference. When the system detects that yaw motion has stabilized and lasted for 3-5 seconds, or when the vehicle leaves the risk area, it automatically and smoothly returns to the user's original settings, thus ensuring driving safety and comfort throughout the journey.

[0126] Accordingly, please refer to Figure 8 A block diagram of a vehicle crosswind risk warning device provided in this application embodiment, the device comprising:

[0127] The feature acquisition unit 101 is used to acquire the features of the road ahead and the traffic environment perception features of the vehicle's driving area. The features of the road ahead are used to characterize the feature markers of crosswind risk in the vehicle's driving area, and the traffic environment perception features are used to characterize the dynamic traffic elements perceived by the vehicle.

[0128] The component determination unit 103 is used to determine risk components that match different crosswind risk dimensions of the vehicle based on the characteristics of the road ahead and the traffic environment perception characteristics.

[0129] The risk determination unit 105 is used to combine the risk wind volume of different crosswind risk dimensions to determine the crosswind risk level that is suitable for the vehicle in the vehicle driving area.

[0130] The warning level determination unit 107 is used to determine the crosswind risk warning level corresponding to the vehicle driving area based on the comparison results between the crosswind risk level and the preset risk level threshold.

[0131] In some optional implementations, the dataset feature acquisition unit 101 is as follows:

[0132] Determine the absolute road orientation angle based on the left and right boundary lines of the lanes in the traffic environment perception features;

[0133] The crosswind weight is determined based on the absolute value of the sine of the angle between the current wind direction and the absolute road orientation angle in the traffic environment perception characteristics.

[0134] The wind condition component is determined by multiplying the current wind speed and crosswind weights in the traffic environment perception features.

[0135] In some alternative implementations, the absolute orientation angle of the road is determined as follows:

[0136] Based on the left and right boundary lines of the lane in the traffic environment perception characteristics, determine the center line of the lane in the vehicle coordinate system.

[0137] The tangential direction between the vehicle at a designated location and the center line of the lane is determined as the local road orientation.

[0138] Determine the angle between the local road alignment and the longitudinal axis of the vehicle body, and define the angle as the relative yaw angle of the road.

[0139] Obtain the vehicle's absolute yaw angle, and add the vehicle's absolute yaw angle to the road's relative yaw angle to obtain the road's absolute heading angle.

[0140] In some alternative implementations, the risk components include vehicle speed and road curvature components, which are determined as follows:

[0141] The absolute value of the road curvature in the traffic environment perception features is used to obtain the weight of the curve's sharpness or gentleness.

[0142] The current vehicle speed and the weight of the curve's sharpness are multiplied together in the traffic environment perception features to determine the vehicle speed and road curvature components.

[0143] In some optional implementations, the risk component includes an environmental mutation component, which is determined as follows:

[0144] Identify occlusion and abrupt changes in road features ahead;

[0145] The environmental mutation component is determined based on the identified occlusion mutation regions.

[0146] In some optional implementations, the risk component includes a marker component, which is determined as follows:

[0147] Identify traffic sign information in traffic environment perception features;

[0148] The sign component is determined based on the identified traffic sign information.

[0149] In some optional implementations, the warning level determination unit 107 includes:

[0150] If the comparison result shows that the crosswind risk level is greater than or equal to the preset risk level threshold, the vehicle driving area is determined to be a high crosswind risk area.

[0151] The difference between the crosswind risk level and the preset risk level threshold is mapped to the crosswind risk warning level corresponding to the high crosswind risk area.

[0152] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0153] In this embodiment, a vehicle crosswind risk warning device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0154] Please see Figure 9 , Figure 9 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. Figure 9 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0155] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0156] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0157] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0158] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0159] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0160] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0161] The apparatus and units described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0162] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0163] Those skilled in the art will understand that the embodiments of this application can be provided as methods or apparatus. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, and devices according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0168] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0169] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0170] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for early warning of crosswind risk in vehicles, characterized in that, The method includes: The system acquires the road features ahead and traffic environment perception features of the vehicle's driving area. The road features ahead are used to characterize the feature markers indicating the presence of crosswind risk in the vehicle's driving area, and the traffic environment perception features are used to characterize the dynamic traffic elements perceived by the vehicle. Based on the road ahead features and the traffic environment perception features, determine the risk components that match the different crosswind risk dimensions of the vehicle. The risk wind volume of different crosswind risk dimensions is combined to determine the crosswind risk level suitable for the vehicle in the vehicle's driving area; wherein, the risk wind volume of different crosswind risk dimensions includes wind condition component, vehicle speed and road curvature component, environmental change component, and sign component; the crosswind risk level is constructed by weighted summation: in, Indicates wind condition component; This represents the vehicle speed and the road curvature component; Indicates the environmental mutation component; Indicates the flag component; , , , For calibrable parameters, they are determined through real vehicle testing or simulation to meet the requirements. The wind component is determined as follows: based on the left and right lane boundary lines in the traffic environment perception features, the absolute road orientation angle is determined; based on the absolute value of the sine of the angle between the current wind direction in the traffic environment perception features and the absolute road orientation angle, the crosswind weight is determined; the current wind speed in the traffic environment perception features and the crosswind weight are multiplied to determine the wind component. W represents the current wind speed. The angle between the current wind direction and the road orientation. The crosswind weight is used; the absolute road orientation angle is determined as follows: based on the left and right boundary lines of the lane in the traffic environment perception features, the lane centerline in the vehicle coordinate system is determined; the tangent direction between the vehicle at a specified position and the lane centerline is determined as the local road orientation; the angle between the local road orientation and the longitudinal axis direction of the vehicle is determined, and the angle is determined as the relative road yaw angle; the absolute vehicle yaw angle is obtained, and the absolute vehicle yaw angle and the relative road yaw angle are added together to obtain the absolute road orientation angle; Based on the comparison between the crosswind risk level and the preset risk level threshold, the crosswind risk warning level corresponding to the vehicle driving area is determined; according to the crosswind risk warning level, the torque requested by the driver is gradually adjusted.

2. The method according to claim 1, characterized in that, The risk components include vehicle speed and road curvature components, which are determined as follows: The absolute value of the road curvature in the traffic environment perception features is used to obtain the curve sharpness weight. The current vehicle speed in the traffic environment perception features is multiplied by the curve sharpness weight to determine the vehicle speed and road curvature components.

3. The method according to claim 1, characterized in that, The risk component includes an environmental mutation component, which is determined as follows: Identify occlusion abrupt changes in the features of the road ahead; The environmental mutation component is determined based on the identified occlusion mutation regions.

4. The method according to claim 1, characterized in that, The risk component includes a marker component, which is determined as follows: Identify traffic sign information in the traffic environment perception features; The sign component is determined based on the identified traffic sign information.

5. The method according to claim 1, characterized in that, The determination of the crosswind risk warning level corresponding to the vehicle driving area based on the comparison result between the crosswind risk level and the preset risk level threshold includes: If the comparison result shows that the crosswind risk level is greater than or equal to a preset risk level threshold, the vehicle driving area is determined to be a high crosswind risk area. The difference between the crosswind risk level and the preset risk level threshold is mapped to the crosswind risk warning level corresponding to the high crosswind risk area.

6. A vehicle crosswind risk warning device, characterized in that, The device includes: The feature acquisition unit is used to acquire the features of the road ahead and the traffic environment perception features of the vehicle's driving area. The road ahead features are used to characterize the feature markers indicating the presence of crosswind risk in the vehicle's driving area, and the traffic environment perception features are used to characterize the dynamic traffic elements perceived by the vehicle. The component determination unit is used to determine risk components that match different crosswind risk dimensions of the vehicle based on the road features ahead and the traffic environment perception features. The risk determination unit is used to combine risk wind volumes from different crosswind risk dimensions to determine a crosswind risk level suitable for the vehicle in the vehicle's driving area; wherein, the risk wind volumes from different crosswind risk dimensions include wind condition components, vehicle speed and road curvature components, environmental abrupt change components, and sign components; the crosswind risk level is constructed by weighted summation. in, Indicates wind condition component; This represents the vehicle speed and the road curvature component; Indicates the environmental mutation component; Indicates the flag component; , , , For calibrable parameters, they are determined through real vehicle testing or simulation to meet the requirements. The wind component is determined as follows: based on the left and right lane boundary lines in the traffic environment perception features, the absolute road orientation angle is determined; based on the absolute value of the sine of the angle between the current wind direction in the traffic environment perception features and the absolute road orientation angle, the crosswind weight is determined; the current wind speed in the traffic environment perception features and the crosswind weight are multiplied to determine the wind component. W represents the current wind speed. The angle between the current wind direction and the road orientation. The crosswind weight is used; the absolute road orientation angle is determined as follows: based on the left and right boundary lines of the lane in the traffic environment perception features, the lane centerline in the vehicle coordinate system is determined; the tangent direction between the vehicle at a specified position and the lane centerline is determined as the local road orientation; the angle between the local road orientation and the longitudinal axis direction of the vehicle is determined, and the angle is determined as the relative road yaw angle; the absolute vehicle yaw angle is obtained, and the absolute vehicle yaw angle and the relative road yaw angle are added together to obtain the absolute road orientation angle; The warning level determination unit is used to determine the crosswind risk warning level corresponding to the vehicle driving area based on the comparison result between the crosswind risk level and the preset risk level threshold; and to gradually adjust the torque requested by the driver according to the crosswind risk warning level.

7. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the vehicle crosswind risk warning method according to any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the vehicle crosswind risk warning method according to any one of claims 1 to 5.

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