Hitch Angle Estimation for Trailer Backup System—An Object Detection and Tracking Approach

This article proposes a novel technique for trailer angle detection (TAD) for use in an advanced trailer backup assistance system (TBAS) to accomplish a semiautonomous or full-autonomous backup maneuver. TBAS incorporates a combined trailer-tow vehicle kinematic model that requires an accurate estimate of the hitch angle. The proposed computer vision (CV) and machine learning (ML) TAD model process the image frames, acquired from the rear-facing camera, to detect and track the trailer and estimate its orientation in relation to the tow vehicle. The technique is based on a deep learning object detection and CV tracking model to detect and track one or more identifiable objects on the trailer (the marker-lights on the front edges of the trailer in this work). The estimated positions of the detected marker-lights are used to perform the hitch angle estimation. The model detects the hitch angle within the specified limit with an acceptance rate of 98%. The model is implemented in real time with a processing rate of more than 30 frames/s.