Camera Monitor Wheel Estimation for Hidden Trailer Wheels
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Solution Overview
Problem
Existing camera monitor systems fail to accurately track the position of trailer wheels when they are hidden from view, such as at low trailer angles or obstructed, which hinders semi-automated driver assist systems and electronic stability programs.
Innovation Solution
A method using a camera monitor system (CMS) to identify wheel locations, filter out false positives, apply quadratic regression to establish a parabola curve relating image positions to trailer angles, and estimate wheel positions even when hidden, providing continuous wheel position data to vehicle systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a camera monitor system uses conventional wheel tracking methods, then wheel position can be accurately tracked when visible, but wheel position becomes unknown when the wheel is hidden (low trailer angle or obstructed view)
Solution Approach 1:
The system pre-establishes a parabola curve model relating wheel image positions to trailer angles during periods when wheels are visible. This preliminary modeling enables the system to predict and estimate wheel positions during subsequent periods when wheels become hidden, thus maintaining continuous tracking capability across all operating conditions
Solution Approach 2:
The parabola curve serves as an intermediary mathematical model that bridges the gap between visible and hidden wheel states. By establishing a predictive relationship between trailer angle and wheel position through this curve, the system can infer wheel positions during obstruction without direct visual observation
2Productivity
If the system continuously tracks wheel positions using camera data, then real-time wheel location is available, but false positive detections occur reducing tracking reliability
Solution Approach 1:
The system applies feedback mechanisms by continuously comparing detected wheel locations against the established parabola curve model. Detected positions that deviate significantly from the expected curve are identified as false positives and filtered out, while positions consistent with the curve are accepted, thereby maintaining high reliability in wheel position data
Solution Approach 2:
The system extracts and removes false positive detections from the set of detected wheel locations by comparing them against the parabola curve expectations. This extraction process separates valid wheel position data from spurious detections, ensuring only reliable positions are used for tracking
Data Source
AI summary
A method for estimating a trailer wheel position includes identifying a first set of wheel locations in at least a first image. Each of the wheel locations in the first set of wheel locations is associated with a corresponding trailer angle. A subset of wheel locations is identified in the first set of wheel locations as being false positives and the false positives are removed from the first set of wheel locations. A quadratic regression is applied to the first set of wheel locations and a parabola curve is determined relating a y position in the at least the first image to an x position in the at least the first image. A trailer angle is determined. A current wheel location is estimated by applying the determined trailer angle to the parabola curve.