Steering toward Safety: Real-Time Lane Detection for Autonomous Navigation

Abstract

The advancement of autonomous driving technology has led to the development of various systems aimed at improving vehicle safety and navigation. This paper presents a lane detection system designed to enhance road safety using edge detection, lane departure warnings, road condition assessment, and optical flow estimation. By leveraging traditional computer vision techniques, including Canny edge detection, Hough transform, and Kalman filtering for temporal smoothing, the system is capable of detecting lane markings and assessing the condition of road surfaces in real-time. Additional features, such as the calculation of lane curvature and optical flow, allow for motion estimation and the detection of vehicles. The methodology also incorporates lane departure warning functionality and a robust approach to detecting lane divergence. The system’s performance is evaluated using video datasets, demonstrating the potential of these techniques for practical deployment in autonomous driving applications.