Probabilistic fusion of rural road course estimations

This paper presents an advanced road course prediction algorithm focusing on longer distances. It shows how to simply combine the different sensors available in modern cars for a road course estimation task. Concretely, a digital-map-based estimation is fused with an optical lane recognition system. Both sensors are evaluated on a representative subset of test sequences to characterize their measurement uncertainties. Then a Bayesian fusion system combines the advantages of the single sensors. Extensive evaluations with high precision ground truth data demonstrate the feasibility of this approach.