A Vision Based Lane Detection and Tracking Algorithm in Automatic Drive

This paper presents a novel lane detection algorithm for automatic drive system. The algorithm chooses a common curved 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) 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 particle filter, which can make the system more stable. The algorithm presented in this paper is proved to be both robust and fast by a large amount of experiments in variable occasions, besides, the algorithm can extract the lanes accurately even in some bad illumination occasions.