Lane Detection Using Computer Vision Techniques in ADAS Systems

Abstract

Lane detection is a critical component of Advanced Driver Assistance Systems (ADAS) aimed at improving road safety. This paper introduces a real-time lane detection system using computer vision techniques implemented in Python with OpenCV. The system efficiently identifies lane markers in video feeds, classifies them as continuous or dashed, and determines the vehicle's position relative to the lane. Additionally, an audible warning system alerts the driver when the vehicle veers outside the lane. Extensive testing demonstrates the system's effectiveness in real-time operation. Potential future improvements include handling more complex road scenarios and environmental conditions through machine learning integration.