Model-Based Robust Lane Detection for Driver Assistance

Abstract

In this paper, we propose an efficient and robust lane detection method for detecting immediate leftand right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lanemodel and the reliable line segment clustering. The reliable line segment cluster is determined from themost probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environmentssuch as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, andthat the proposed method performs better compared with other lane detection methods on an CMU/VASClane dataset.