Trailer Hitch Alignment via Vision-Based Coupler Detection
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Solution Overview
Problem
The process of connecting a trailer to a towing vehicle is challenging due to the need for high accuracy in aligning the trailer coupler with the tow-ball, which is difficult and time-consuming, even for experienced drivers, and there is a lack of automated systems to assist in this process and prevent trailer jackknife situations.
Innovation Solution
A system that uses a camera positioned at the rear of the vehicle to capture images of the tow-ball and trailer, employing a trained learning model and tracking algorithm to detect and track the trailer, tow-ball, and coupler, and generate a trajectory for the vehicle to align with the trailer, while also monitoring for potential jackknife situations and preventing them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trailer hitching is performed by maneuvering the vehicle in reverse, then the trailer coupler and tow-ball can be aligned and connected, but the process is very difficult and time-consuming even for experienced drivers
Solution Approach 1:
The patent replaces manual mechanical maneuvering with an automated vision-based system. A camera captures images of the trailer coupler and tow-ball, and a trained learning model automatically detects their locations and generates alignment trajectories, eliminating the need for drivers to manually maneuver the vehicle in reverse while significantly reducing hitching time
Solution Approach 2:
The system enables the vehicle to perform self-alignment and self-hitching by using its own camera and processing units to detect the trailer position, calculate the optimal trajectory, and guide the driver or control the vehicle automatically, making the hitching process independent of driver experience
2Measurement precision
If a trained learning model processes multiple scaled versions of images to detect trailers and tow-balls, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image processing task by dividing it into multiple stages: first processing the original image, then processing scaled versions of the image with different resolutions. This segmentation allows the system to handle both distant and close-range detections effectively while managing computational complexity through progressive refinement
Solution Approach 2:
The system adds the dimension of image scaling by processing the same image at multiple different scales (resolutions). This multi-scale approach enables the learning model to detect objects of varying sizes and distances, improving detection accuracy without requiring multiple physical cameras
3Reliability
If the system continuously detects locations of trailer, tow-ball and coupler in each image sequence, then tracking accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary detection of the trailer in earlier images to establish initial tracking parameters and area of interest regions. This preliminary action allows subsequent continuous detection to focus on refined regions, improving tracking reliability while reducing the computational load of processing entire images at every frame
Solution Approach 2:
The patent applies local quality by estimating and focusing computational resources on specific areas of interest within each image frame. Instead of uniformly processing entire images, the system concentrates detection efforts on regions where the trailer, tow-ball, and coupler are most likely to appear based on previous detections, improving tracking efficiency
Data Source
AI summary
Techniques are described for tracking a trailer, including obtaining a sequence of images from a camera positioned in rear of a vehicle, detecting the trailer and a tow-ball in the first image of the sequence of images using a trained learning model based on the first image and at least one scaled version of the first image, estimating an area of interest using a tracking algorithm and locations of the detected trailer and the tow-ball in the first image, estimating a distance between the vehicle and the trailer based on the detected location of the trailer in the first image, and upon determining that the distance between the vehicle and the trailer is less than a second threshold, continuously detecting locations of the trailer, tow-ball and a coupler in each image of the sequence of images using the learning model and the estimated area of interest, and updating the estimated area of interest to correspond to an area of interest in the following image, and actuating an action of the vehicle based on the detected locations of the trailer, tow-ball and the coupler.