Camera Trailer Angle Tracking Without Target Markers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle vision systems face challenges in accurately determining the trailer angle relative to the vehicle, especially when reversing with a trailer attached, due to limitations in visibility caused by weather, shadows, and glare, and require a target on the trailer body for accurate positioning.
Innovation Solution
A camera-based system that detects the trailer angle without a target, using feature vectors and motion patterns to track the trailer's position and angle relative to the vehicle, independent of climatic and ambient lighting conditions, and calculates the trailer angle using vehicle parameters like speed and steering wheel angle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a target is placed on the trailer body for accurate positioning, then measurement precision is improved, but device complexity and ease of operation are worsened
Solution Approach 1:
The patent extracts the target requirement from the system by detecting natural features of the trailer body itself (edges, corners, contours) rather than requiring an externally attached target. This eliminates the need for additional target components while maintaining detection accuracy through feature vector extraction from the trailer's inherent geometric characteristics.
Solution Approach 2:
The trailer serves itself as the detection target by using its own structural features (body contours, edges, corners) for angle detection. The system leverages the trailer's inherent geometric properties rather than requiring external targets, making the trailer self-sufficient for its own positioning and angle measurement.
2Measurement precision
If imaging sensors are used to detect trailer position, then measurement capability is improved, but reliability is worsened due to weather, shadows, and glare
Solution Approach 1:
The system adapts to varying lighting and weather conditions by dynamically adjusting detection parameters and using multiple feature types (edges, corners, contours) that can be detected under different conditions. The feature vector extraction process identifies reliable geometric parameters that remain detectable despite changes in illumination, shadows, or glare.
Solution Approach 2:
The detection system uses a composite approach by combining multiple feature detection methods (edge detection, corner detection, contour analysis) rather than relying on a single imaging method. This multi-feature composite strategy ensures that at least some features remain detectable under varying weather and lighting conditions, improving overall system reliability.
3Ease of operation
If feature vectors and motion patterns are used for tracking, then ease of operation is improved by eliminating targets, but measurement precision may be worsened
Solution Approach 1:
The system segments the trailer detection task into multiple independent feature vector extractions (edges, corners, contours) rather than relying on a single target. This segmentation allows the system to use multiple geometric features simultaneously, maintaining measurement precision while eliminating the need for artificial targets and simplifying operation.
Data Source
AI summary
A vehicular trailer angle detection system includes a camera disposed at a rear portion of a vehicle. The system determines, via processing of frames of image data captured by the camera, features of a trailer present rearward of the vehicle and hitched to the vehicle by determining features that have similar position changes between a current frame of image data captured by the camera and a previous frame of image data captured by the camera. Responsive to movement of the trailer relative to the vehicle, and via processing of captured frames of image data, the system tracks determined features over multiple captured frames of image data for different positions of the trailer relative to the vehicle. The system determines angle of the trailer relative to the vehicle based at least in part on tracking of determined features of the trailer over multiple captured frames of image data.