Lane detection and tracking are very crucial treatments in lane departure warning systems as they help the vehicle-mounted system to keep its lane. In this context, the authors’ work aims to develop vision-based lane detection and tracking method to detect and track lane limits in highways and main roads. The authors’ contribution focuses on the detection step. By exploiting the fact that, in an image, the road can be formed by linear and curvilinear portions, the authors propose two types of appropriate treatments to detect the lane limits. The authors’ method offers high precision rates independently of the painted lane marking’s characteristics, of the time of acquisition and in different weather conditions. Besides the challenges it overcomes, the authors’ method has the advantage of operating with a timing complexity that is reasonable for real-time applications. As shown experimentally, compared to three leading methods from the literature, the authors’ method has a higher efficiency.
Data fusion based lane departure warning framework using fuzzy logic