Path Planning for Autonomous Driving Based on Stereoscopic and Monoscopic Vision Cues

This paper presents a system for real-time feature detection and subsequent path planning for autonomous driving. Special focus lies on visual features for autonomous navigation: stereoscopic and monoscopic cues are employed to distinguish between trafficable road and obstacles of any kind; temporal and stereoscopic point correspondences are used to determine the ego motion of the vehicle. A probabilistic framework for path planning is formulated that models the a priori knowledge about a path and the likelihoods of the visual features. Our path planning algorithm employs a Bayes filter approach that allows recursive integration of new measurements. A slightly modified version of the system was successfully used at the qualifications and final race of the DARPA Grand Challenge 2005 within the Desert Buckeyes´ autonomous vehicle. However, the algorithm is, in principle, suitable for arbitrary environments and not limited to unstructured terrain