Vehicle Dynamics Control for Crosswind Detection and Lane Centering
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Solution Overview
Problem
Strong crosswinds can cause lateral drift and instability in vehicles, particularly high profile vehicles, which existing Advanced Driver Assistance Systems (ADAS) struggle to mitigate effectively without additional hardware.
Innovation Solution
A method and system that utilize a vehicle dynamics model to detect crosswind impacts by analyzing frequency responses and phase shifts, adapting steering control through gain scheduling to resist crosswind effects, enhancing lane centering performance without additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing ADAS lane keeping assistance features are used, then basic lane centering is provided, but the system cannot effectively mitigate crosswind-induced lateral drift and instability
Solution Approach 1:
The system continuously monitors vehicle lateral dynamics variables (lateral acceleration, yaw rate, steering angle) and uses this feedback to detect crosswind conditions by comparing actual vehicle response against expected model predictions. This closed-loop feedback mechanism enables real-time detection and adaptation to crosswind disturbances, improving lane centering reliability under adverse conditions
Solution Approach 2:
The system dynamically adjusts control parameters (steering gains, control effort) based on detected crosswind conditions. When crosswind is detected, the controller modifies steering intervention parameters to counteract the lateral disturbance, changing system behavior adaptively rather than using fixed parameters, thereby maintaining lane centering performance despite crosswind effects
2Measurement precision
If additional sensors are added to detect crosswind, then crosswind detection capability is improved, but hardware complexity and cost increase
Solution Approach 1:
The system uses the vehicle's existing sensor suite (lateral acceleration sensors, yaw rate sensors, steering angle sensors) to detect crosswind conditions by analyzing the vehicle's dynamic response to lateral disturbances. Rather than requiring dedicated crosswind sensors, the system makes the existing sensor network serve the additional function of crosswind detection through model-based analysis, avoiding hardware complexity increases
Solution Approach 2:
The system introduces a vehicle dynamics model as an intermediary between the existing sensors and the lane keeping controller. This model predicts expected vehicle behavior under normal conditions, and the deviation between predicted and actual behavior serves as an indirect measurement of crosswind effects, enabling crosswind detection without direct sensing
3Measurement precision
If the vehicle dynamics model is continuously updated, then detection accuracy is improved, but computational load increases
Solution Approach 1:
The system performs model identification and parameter updates only when necessary - specifically when crosswind conditions are detected or when driving conditions warrant re-calibration. Rather than continuously updating the vehicle dynamics model, the system applies partial updates triggered by specific events, reducing computational energy consumption while maintaining detection accuracy when needed
Data Source
AI summary
Systems and methods for controlling a vehicle are provided. The systems and methods provide a vehicle dynamics model that relates at least one input vehicle dynamics variable to at least one output vehicle dynamics variable. The systems and methods detect a crosswind impacting the vehicle by detecting a disturbance associated with the vehicle dynamics model caused by the crosswind and adapt control of the vehicle based on the detecting the crosswind impacting the vehicle.