Probabilistic lane detection and lane tracking for autonomous vehicles using a cascade particle filter

Abstract

This paper proposes a robust lane detection algorithm with a cascade particle filter that incorporates a model decomposition approach. Despite the sophisticated tracking mechanism of a particle filter, the conventional particle-filter-based lane detection system suffers from an estimation accuracy problem and a high computational load. In order to improve the robustness and the computation time for lane detection systems, the proposed cascade particle filter decomposes a lane model into two submodels: a straight model and a curve model. By dividing the lane model, not only can the computation time be decreased, but also the accuracy of the lane state estimation system can be increased. The proposed lane detection algorithm and the cascade particle filter were evaluated on various roads and environmental conditions with the autonomous vehicle A1, which was the winner of the 2010 and 2012 Autonomous Vehicle Competition in the Republic of Korea organized by the Hyundai motor group. The proposed algorithm proved to be sufficiently robust and fast to be applied to autonomous vehicles as well as to intelligent vehicles for improving the vehicle safety.