Robust road boundary estimation for intelligent vehicles in challenging scenarios based on a semantic graph

This paper presents a stereovision-based detection and tracking approach of the drivable road boundary, designed for navigating an intelligent vehicle through challenging traffic scenarios, and increment road safety in such scenarios with advanced driver assistance systems (ADAS). It is based on a formulation of stereo with homography associated with a semantic graph constructed from the traffic scene. Under this formulation, we employ the Viterbi algorithm and propose a sophisticated measure of the probability of the state sequence in the semantic graph to find the most likely boundary between the road and non-road regions. The results are then refined by a post-processing step with the RANdom Sample Consensus (RANSAC) algorithm to obtain the locations and curvatures of the lateral road boundaries. Experimental results on a wide variety of typical but challenging real road scenes have substantiated the effectiveness as well as robustness of the proposed approach.