A robust Bayesian multisensor fusion algorithm for joint lane and pavement boundary detection

In this paper we propose to simultaneously detect lane and pavement boundaries by fusing information from both optical and radar images. The boundaries are described with concentric circular models, whose parameters are compatible and will result in better conditioned estimation problems than previous parabolic models. The optical and radar imaging processes are represented with Gaussian and log-normal probability densities, with which we successfully avoid the ad-hoc weighting scheme carried on the two likelihood functions. The multisensor fusion boundary detection problem is posed in a Bayesian framework and a joint maximum a posteriori (MAP) estimate is employed to locate the lane and pavement boundaries. Experimental results are shown to demonstrate that the fusion algorithm outperforms single sensor based boundary detection algorithms in a variety of road scenarios. And it also yields better boundary detection results than the fusion algorithm that took advantage of existing prior and likelihood formulations.