Vision-Based Lane Detection for Autonomous Artificial Intelligent Vehicles

Intelligent vehicles are one of the enlightening ideas that will shape our future by providing enhanced safety and improved mobility. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. However, lane detection is a challenging task because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle. A developed preprocessing phase including a grayscale conversion, noise removal, edge detection with automatic thresholding, lines extraction using Hough transform, lines or boundaries of the road fitted by hyperbolas are simulated. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.