Autonomous Vision Sensor Misalignment Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing lane sensing systems rely on manual correction of vision sensor misalignment, which is inefficient and lacks autonomous detection and correction capabilities, leading to inaccurate lane positioning and vehicle control issues.
Innovation Solution
An autonomous system that detects and corrects angle misalignment in vision sensors using vehicle and road parameters like yaw rate, lateral offset, heading, speed, and lane curvature, generating histograms to determine alignment probabilities and actuate warnings or corrections within predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual correction by service personnel is used, then misalignment can be corrected, but the process is inefficient and requires external service intervention
Solution Approach 1:
The vision sensor system performs self-diagnosis and self-correction by autonomously detecting misalignment through histogram analysis of alignment parameters and automatically adjusting its mounting angle, eliminating the need for external service personnel and manual intervention while maintaining system reliability
Solution Approach 2:
The system changes operational parameters by analyzing histograms of alignment parameters (such as lateral offset, heading angle, and curvature) to detect misalignment conditions, and adjusts the sensor's mounting angle parameters to correct the misalignment, enabling autonomous adaptation without external service
2Measurement precision
If vision sensor misalignment is not corrected, then the system remains simple, but lane positioning accuracy deteriorates
Solution Approach 1:
The patent replaces complex mechanical alignment adjustment mechanisms with a software-based histogram analysis system that processes alignment parameters and automatically controls sensor orientation, achieving high measurement precision through computational methods rather than mechanical systems
Solution Approach 2:
The system implements feedback by continuously monitoring alignment parameters, comparing them against expected distributions through histogram analysis, detecting deviations that indicate misalignment, and automatically adjusting the sensor orientation to maintain accurate lane positioning
Data Source
AI summary
A method of diagnosing a state of health of a vision-based lane sensing system. A first misalignment factor is calculated as a function of a vehicle lateral offset and a vehicle heading. A second misalignment factor is calculated as a function of a vehicle speed, an estimated curvature of an expected path of travel, a lane curvature, and the vehicle heading. Histograms are generated for the first and second misalignment factors. A probability of a state of health is determined. A determination is made whether the probability of the state of health is within a predetermined threshold. An angle misalignment of the vision system is estimated. The angle misalignment of the vision system is corrected in response to the determination that the probability of the state of health is within the predetermined threshold; otherwise a warning of a faulty lane sensing system is actuated.