CLARK: a heterogeneous sensor fusion method for finding lanes and obstacles
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 sensorsvision 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.
bayesian detection intelligent vehicles deformable templates global shape matching