Trailer Hitch Ball Position Estimation Using Marker Light Tracking
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Solution Overview
Problem
Current driver-assisted systems for maneuvering vehicles with trailers face challenges in accurately estimating the location of the hitch ball, which is crucial for trailer angle detection and other assistive features, as existing methods lack precision and reliability.
Innovation Solution
A system utilizing a convolutional neural network to process camera images from a vehicle-mounted camera, calibrating for distortion, and employing nonlinear least-squares optimization to accurately determine the hitch ball location and hitch angle by tracking marker lights on the trailer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to estimate hitch ball location, then the system is simpler, but the measurement precision is insufficient
Solution Approach 1:
The patent introduces marker lights as intermediary objects attached to the trailer, which serve as detectable reference points. These markers act as mediators between the camera system and the hitch ball, enabling indirect but precise measurement of the hitch ball location through geometric relationships and optimization algorithms.
Solution Approach 2:
The patent replaces traditional mechanical or manual methods of determining hitch ball location with a computer vision-based optical system. By using camera images, convolutional neural networks, and nonlinear optimization, the system substitutes mechanical measurement approaches with optical-field-based detection, achieving higher precision without physical contact.
2Adaptability or versatility
If the hitch ball is not visible, then the system can operate in more conditions, but the detection reliability decreases
Solution Approach 1:
The patent creates a virtual model or copy of the hitch ball location through image processing and geometric calculations. Instead of directly observing the hitch ball, the system generates a computed representation of its position based on marker light positions and camera parameters, allowing reliable estimation even when the hitch ball itself is not visible.
Solution Approach 2:
The system performs preliminary calibration by establishing the geometric relationships between marker lights, camera position, and hitch ball location before actual operation. This pre-established model enables the system to reliably estimate hitch ball position under varying conditions without requiring direct visibility of the hitch ball during operation.
Data Source
AI summary
The disclosure is generally directed to systems and methods for trailer hitch ball position location including receiving a plurality of image frames from a camera directed at a front of a trailer coupled to a vehicle at a coupling point, modeling the image frames in a convolutional neural network to form an initial estimate of a pivot point position as a location, optimizing the model using a nonlinear equation to identify the pivot point position, and locating the coupling point as the optimized pivot point position. The convolutional neural network includes a plurality of bounding boxes centered as locations of predetermined markers on the front of the trailer, the modeling via a reverse pinhole projection of a pixel onto a three-dimensional coordinate frame projected on a trailer plane to enable computed marker positions to determine relative dimensions of the trailer and locate the coupling point. The optimizing includes solving a nonlinear least-squares (NLLSQ) optimization formulation for multiple markers on the trailer. The method include the use of geometrical means to determine the angle of articulation between the trailer and tow-vehicle (hitch angle) by tracking the plurality of bounding boxes centered as locations of predetermined markers on the front of the trailer, as the vehicle and trailer move in a circular arc with respect to each other.