Lane Controller Self-Diagnosis via Virtual Dynamics
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Solution Overview
Problem
Lane departure warning and centering systems lack self-diagnostic capabilities to detect malfunctions, which can lead to unsafe driving conditions due to unawareness of defective components, potentially causing asymmetric wear or severe malfunctions.
Innovation Solution
A lane controller system with a diagnostic module that compares actual steering corrections with predicted ones, using a virtual dynamics module and vehicle state estimation, to diagnose discrepancies and identify root causes such as wheel imbalance, sensor misalignment, or mechanical malfunctions, providing warnings and control signals for assisted or automated steering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lane centering systems automatically correct vehicle movement, then lane keeping performance is improved, but driver awareness of malfunctions deteriorates
Solution Approach 1:
The system continuously monitors actual vehicle response to steering corrections and compares it with predicted response from virtual dynamics model, providing feedback about system health status. This feedback mechanism enables detection of malfunctions while maintaining automatic lane centering operation.
Solution Approach 2:
The diagnostic system performs self-diagnosis by comparing actual steering corrections with predicted ones, allowing the system to detect its own malfunctions without external intervention. This self-monitoring capability maintains lane centering performance while identifying issues.
2Reliability
If diagnostic capabilities are added to detect malfunctions, then system reliability is improved, but device complexity increases
Solution Approach 1:
The system creates a virtual copy (virtual dynamics model) of the vehicle's steering behavior to compare against actual behavior. This virtual model serves as a reference for detecting malfunctions without requiring additional physical sensors or complex diagnostic hardware.
Solution Approach 2:
The virtual dynamics module serves multiple functions: it predicts vehicle response for diagnostic purposes, models steering behavior for control optimization, and provides a baseline for detecting anomalies. This multi-functionality reduces the need for separate dedicated diagnostic components.
3Measurement precision
If actual steering corrections are compared with predicted ones, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The virtual dynamics model pre-calculates expected vehicle response to steering corrections before actual corrections are applied. This preliminary prediction allows immediate comparison with actual behavior, enabling rapid malfunction detection without time-consuming analysis.
Solution Approach 2:
The comparison between actual and predicted steering corrections occurs continuously as the lane centering system operates, rather than requiring separate diagnostic cycles. This continuous monitoring maintains high detection precision while minimizing time loss through constant real-time analysis.
Data Source
AI summary
A lane controller system installed on a vehicle may include components for self-diagnosing malfunctions on the vehicle. The system may include a desired path generator for generating a desired path that keeps the vehicle within a road lane; a steering controller for providing steering a steering correction to keep the vehicle within the road lane; a vehicle state estimator for estimating the state of the vehicle; a lane marking detector for detecting position of road lane markings; a path predictor for predicting a path actively followed by the vehicle; a virtual dynamics module for modeling the anticipated path of the vehicle following input of the steering controller; a comparer that compares the results of actual steering corrections applied with those predicted by the virtual dynamics module, and a diagnostic system that determines a root cause of malfunctions, based on the comparison by the comparer.