Enhancing Lane Detection for Autonomous Vehicles Using Image Processing and Real-Time Analysis

Lane detection is an essential function for autonomous driving systems since it provides information about the vehicle's position and the road's geometry. However, there are problems with existing lane detection algorithms, such as shadows, uneven lighting, and traffic signs. In this work, a conventional image processing-based lane detection method based on the Canny edge detector and Hough transform is implemented. The algorithm is implemented and tested on two different platforms, the Raspberry Pi 4 B+ and the Nvidia Jetson Nano boards, utilizing both recorded and live-streaming videos. Our evaluation shows that the technique works effectively for lane detection, with Nvidia Jetson Nano outperforming Raspberry Pi 4 B+. The proposed system can be used for many autonomous driving tasks, such as lane changing, lane keeping, and collision avoidance.