Lane identification and ego-vehicle accurate global positioning in intersections

This paper proposes a method for achieving accurate ego-vehicle global localization with respect to an approaching intersection; the method is based on the data alignment of the information from two input systems: a Sensorial Perception system, on-board of the ego-vehicle, and an a priori digital map. For this purpose an Extended Digital Map is proposed that contains the detailed information about the intersection infrastructure: detailed landmarks accurately measured and positioned on the map. The data alignment mechanism is thus based on superimposing the sensorial detected landmarks with the corresponding, correctly positioned map landmarks stored in the new Extended Digital Map. The data Alignment Algorithm requires as input, beside the information from the two input systems, the ego-vehicle driving lane. This information is inferred by using a probabilistic approach in the form of a Bayesian Network; the uncertain and noisy character of the sensorial data require such a probabilistic approach in the quest of the ego-lane.