Dual Forgetting Factor Crosswind Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Crosswind conditions pose challenges for drivers as they require complex steering inputs and increased workload, leading to discomfort and potential driver distraction, as existing technologies struggle to accurately estimate and manage crosswind disturbances effectively.
Innovation Solution
A method utilizing a recursive-least-squares heuristic with multiple forgetting factors to estimate vehicle uncertainty and crosswind disturbances from driver steering and sensor inputs, improving sensitivity to fast-changing crosswinds and reducing driver workload through enhanced crosswind estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single forgetting factor is used in crosswind estimation, then the system complexity is low, but the estimation accuracy for fast-changing crosswinds deteriorates
Solution Approach 1:
The patent divides the single forgetting factor into multiple forgetting factors (first forgetting factor for vehicle uncertainty, second forgetting factor for crosswind estimation). This segmentation allows different parts of the estimation process to use different forgetting factors optimized for their specific purposes, improving overall estimation accuracy for fast-changing crosswinds while maintaining manageable system complexity through modular design.
2Ease of operation
If crosswind estimation is improved to reduce driver workload, then driver comfort improves, but the computational requirements and system complexity increase
Solution Approach 1:
By segmenting the estimation process into two parallel RLS estimators with different forgetting factors, the system achieves improved crosswind estimation accuracy that can reduce driver workload through better information provision, while the modular segmented architecture keeps computational complexity manageable compared to a single complex estimator.
Solution Approach 2:
The dual-estimator system serves multiple purposes: the first estimator provides vehicle uncertainty characterization while the second provides crosswind estimation. This self-service approach allows the system to derive multiple benefits from the enhanced estimation capability, justifying the increased complexity through multiple functional returns that ultimately reduce driver workload.
Data Source
AI summary
A device may identify a first forgetting factor accounting for a rate of change of vehicle uncertainty and a second forgetting factor accounting for a rate of change of crosswind estimation. The device may utilize a recursive-least-squares heuristic executed by a crosswind and vehicle uncertainty estimator and specialized with the first and second forgetting factors to determine vehicle uncertainty and crosswind estimation from driver steering inputs and crosswind disturbance inputs, the first and second forgetting factors accounting for relatively slower-changing vehicle uncertainty and relatively faster-changing crosswinds.