CLARM: a heterogeneous sensor fusion method for finding lanes and obstacles

摘要

This paper describes Combined Likelihood Adding Radar Knowledge (CLARK), a new method for detecting lanes and obstacles by fusing information from two forward-looking vehicle mounted sensors--vision and radar. CLARK has three stages: (1) obstacle detection using a novel template matching approach; (2) lane detection using a modified version of the Likelihood Of Image Shape algorithm; (3) simultaneous estimation of both obstacle and lane positions by locally maximizing a combined likelihood function. Experimental results illustrating the efficacy of these components are presented. CLARK detects the position of lanes and obstacles accurately, even under significantly noisy conditions.