Road trajectory mining and autonomous steering control for vision-based unmanned vehicles

Autonomous steering control is the principal task in the development of an intelligent transportation system. This research paper proposes a novel approach for vision based intelligent control of unmanned vehicles. The paper addresses the problems of accurate and efficient intelligent vehicle control by incorporating a well known evolutionary algorithm cAnt-Miner. The uniqueness of the proposed algorithm lies in its ability to accurately steer the vehicle on structured and unstructured roads in real-time, without requiring meticulous calculation. The algorithm first uses the road edges to evaluate the direction of the path through classification via cAnt-Miner. Subsequently, it steers the vehicle according to the particular direction using fuzzy logic controller. The synergy of the evolutionary algorithm, cAnt-Miner, for trajectory mining with fuzzy logic control enables autonomous steering with high efficacy. Experiments were carried out on our university's Intelligent DRIving System (IDRIS). Experimental results show that the proposed algorithm can steer the car automatically in real-time.