Trailer Wheel Position Estimation When Camera View Is Obstructed
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Solution Overview
Problem
Existing camera systems in commercial vehicles fail to accurately track the position of trailer wheels when they are hidden due to low trailer angles, which is crucial for semi-automated driver assist systems and electronic stability programs.
Innovation Solution
A method for estimating trailer wheel position by identifying wheel locations in images, clustering these locations, generating a best fit curve based on trailer angles, and extending this curve to predict wheel positions when they are hidden, using techniques like Kalman or least-square filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the trailer angle is low, then the field of view coverage is improved, but the wheel visibility is lost
Solution Approach 1:
The system pre-establishes a best fit curve model during periods when wheels are visible (normal trailer angles). This preliminary modeling allows the system to predict wheel positions even when wheels become hidden during low trailer angle operations, thus maintaining continuous wheel position data without requiring real-time visibility
Solution Approach 2:
The patent introduces an intermediary mathematical model (best fit curve) that mediates between visible wheel positions and hidden wheel positions. The curve serves as a predictor that translates trailer angle information into estimated wheel positions, allowing the system to maintain wheel position data during low trailer angles without direct camera observation
2Reliability
If wheel tracking is maintained during low angles, then the system reliability is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or sensor-based wheel tracking systems with a computational approach using best fit curve modeling. Instead of adding physical sensors or complex mechanical tracking devices, the system uses mathematical modeling and image analysis to estimate wheel positions, significantly reducing device complexity while maintaining reliability
Solution Approach 2:
The system creates a mathematical copy (best fit curve) of the wheel position trajectory based on previously observed wheel positions at various trailer angles. This virtual copy allows the system to predict wheel positions during low angles without physically tracking the actual wheels, maintaining reliability while avoiding complex tracking hardware
Data Source
AI summary
A method for estimating a trailer wheel position includes identifying a first set of wheel locations in a first image. Each of the wheel locations in the first set of wheel locations is associated with a corresponding trailer angle. The first set of wheel locations is clustered and a primary cluster in the first set of wheel locations is identified. A best fit curve is applied to the primary cluster. The best fit curve is a curve associating wheel position to trailer angle. An estimated wheel position is determined by applying a determined trailer angle to the best fit curve in response to the wheel being hidden in the first image. The estimated wheel position is output to at least one additional vehicle system.