Adaptive ADAS Parameter Estimation for Trailer Towing Stability
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Solution Overview
Problem
Modern vehicle control systems face challenges in accurately performing advanced driver-assistance systems (ADAS) functions, such as lane centering control, when towing trailers due to variations in payload and trailer configurations, leading to inaccurate estimates of understeer coefficients and potential instability.
Innovation Solution
The system includes a vehicle parameter estimation module that generates estimates of tire cornering stiffness and understeer coefficients using recursive least squares to adaptively adjust for changes in vehicle and trailer masses, operating conditions, and road bank angles, allowing for improved prediction of vehicle states and control of actuators to maintain stability and lane centering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle control systems use fixed vehicle parameters for ADAS functions, then the system complexity is low, but the measurement precision of vehicle state prediction deteriorates when payload or trailer configurations vary
Solution Approach 1:
The system dynamically adapts vehicle parameters (understeer coefficient, tire cornering stiffness) based on real-time operating conditions rather than using fixed parameters. The vehicle parameter estimation module continuously updates parameters according to actual vehicle state, payload conditions, and trailer configurations, making the system flexible and adaptive to varying masses and configurations.
Solution Approach 2:
The system uses readily available sensor data from the vehicle (accelerometers, gyroscopes, steering angle sensors) to self-determine vehicle parameters without requiring additional external sensors or manual input. The parameter estimation is performed autonomously by the control system using existing operational data, eliminating the need for separate measurement devices.
2Adaptability or versatility
If the system accounts for varying payload and trailer configurations, then the adaptability improves, but the device complexity increases due to additional sensing and computation requirements
Solution Approach 1:
The system uses a universal set of sensors already present in modern vehicles (accelerometers, gyroscopes, steering angle sensors) to serve multiple functions: both basic vehicle control and advanced parameter estimation for various ADAS functions. This multi-functional approach allows the system to adapt to different payload and trailer configurations without requiring additional specialized sensors.
Solution Approach 2:
The system autonomously determines vehicle parameters and adapts to different configurations using existing sensor data, eliminating the need for manual configuration input or additional external sensors. The parameter estimation module automatically adjusts vehicle models based on real-time operating conditions, making the system self-adapting without increasing hardware complexity.
3Measurement precision
If recursive least squares estimation is used to adaptively adjust vehicle parameters, then the measurement precision improves, but the loss of computation increases
Solution Approach 1:
The system continuously performs parameter estimation using recursive least squares methodology, maintaining up-to-date vehicle parameters throughout operation. Rather than periodic updates, the estimation runs continuously using incoming sensor data, ensuring accurate parameter values are always available for control decisions without requiring intensive batch processing.
Solution Approach 2:
The system implements feedback-based parameter estimation where the recursive least squares algorithm continuously refines parameter values based on the difference between predicted and actual vehicle behavior. This feedback mechanism allows the system to converge to accurate parameter values efficiently, reducing the computational burden compared to open-loop estimation methods.
Data Source
AI summary
A vehicle parameter estimation module is configured to generate a first estimate of a vehicle parameter based on operating conditions of a vehicle measured or estimated at a first time. The vehicle parameter includes at least one of a tire cornering stiffness of the vehicle and an understeer coefficient of the vehicle. The vehicle parameter estimation module is also configured to determine an error value based on the first estimate of the vehicle parameter and values of the vehicle operating conditions measured or estimated at a second time that is later than the first time. The vehicle parameter estimation module is further configured to generate a second estimate of the vehicle parameter based on the first estimate of the vehicle parameter and the error value. A vehicle actuator control module is configured to control an actuator of the vehicle based on the second estimate of the vehicle parameter.