Trailer Angle Detection Using Neural Networks and Camera Data

The patent replaces traditional mechanical or geometric trailer angle measurement systems with a neural network-based image processing system. The controller uses an imaging device to capture images of the trailer and surrounding environment, then processes these images through a neural network to estimate the trailer angle. This substitution of mechanical measurement with intelligent image processing resolves the contradiction by achieving high measurement precision through software-based angle estimation that is insensitive to physical variations in hitch types, weather conditions, and environmental factors.

The patent implements a self-service approach where the neural network automatically learns and adapts to different trailer configurations, hitch types, and environmental conditions during normal operation. The system uses the imaging device to capture images and the neural network to automatically estimate angles without requiring users to manually add markers, configure parameters, or perform setup procedures. This eliminates the need for additional markers or cues while maintaining high measurement precision, thereby resolving the contradiction between measurement accuracy and ease of operation.

The patent implements a multi-functional neural network system that performs multiple tasks using a single integrated architecture. The same neural network processes images to estimate trailer angles, adapts to different hitch types, compensates for environmental variations, and provides robust angle measurements across diverse operating conditions. This universal approach achieves high reliability without requiring multiple separate sensors or processing systems, as the single neural network handles all detection and adaptation functions, thereby resolving the contradiction between reliability and device complexity.