A Vision-based Lane Detecting and Tracking Algorithm

Abstract:

This paper presents a novel lane detection algorithm, which is suitable for automatic drive system. The algorithm chooses the common curve lane parameter model, which can describe both straight and curved lanes. The most prominent contribution of this paper is: instead of using one single method to calculate all the parameters in the lane model, both the Adaptive Random Hough Transformation (ARHT) algorithm and the Tabu Search algorithm are used to calculate the different parameters in the lane model, according to the different demands of accuracy for different parameters. Furthermore, in order to reduce the time-consume of the whole system, the strategy of multi-resolution is proposed. At last, this paper also presents a tracking algorithm based on the particle filter, which can make the system more stable. The algorithms presented in this paper are proved to be both robust and fast by a large amount of experiments in variable occasions, besides, the algorithms can extract the lanes accurately even in some bad illumination occasions.