Rapid tracking for autonomous driving with monocular video
We present a novel tracking algorithm for an autonomous vehicle equipped with a single camera. Given only monocular visual data, our algorithm utilizes projective geometry to compute concise features of the environment. Using these features, road markings are identified by a multi-class classifier. The classification results are then used with a Rao-Blackwellized particle filter to track the vehicle as it moves back and forth across the road. The resulting position tracker is part of a complete, simulated autonomous driving system. The realistic driving video game Need for Speed: Hot Pursuit was used as a vehicle simulation platform, and the autonomous system is shown to perform competitively against the game's automated opponents. © 2013 IEEE.
