A Modern Vehicle Driver Assistance Interface Using Computer Vision and AI Techniques

This paper proposes an advanced auto vision algorithms to provide a fresh approach to vehicle lane tracing. The proposed approach reliably detects and tracks lane boundaries in real-time by combining machine learning models with powerful image processing methods. The suggested system successfully manages changing road conditions and difficult illumination conditions by combining noise reduction, dynamic thresholding, edge recognition, and Hough transformbased feature extraction. When compared to conventional techniques, experimental evaluations show that our technology not only increases detection accuracy but also computing efficiency, opening the door for safer and more dependable autonomous navigation systems. By supporting adaptive cruise control, lane-keeping, and lane departure alarms, efficient lane detecting improves vehicle safety. The proposed research work examines various lane detecting methods, their difficulties, and developments in real-time application for intelligent transportation systems.