Lane Detection and Tracking Using a New Lane Model and a Distance Transform

Abstract

Lane detection is an important component of driver assistance systems (DAS), and highway-based lane departure solutions have been in the market since the mid 1990s. However, improving and generalizing vision-based lane detection solutions remains to be a challenging task. Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filter-based approach. Furthermore, a modified version of an Euclidean distance transform is applied on an edge map to provide information for boundary point detection. In comparison to the edge map, properties of the distance transform support improved lane detection including a novel initialization method. Two lane tracking methods are also discussed while focusing on efficiency and robustness, respectively. Finally, the paper reports about experiments on lane detection and tracking.