Ego lane estimation using vehicle observations and map information

An ego vehicle localization algorithm must be able to estimate where the vehicle is on the road. This is typically performed with a positioning filter that operates in global coordinates. Herein, we take a different approach, by splitting the localization problem into two parts: in-lane localization and ego lane estimation. The paper addresses the latter problem. For this, we have developed theory and algorithms which, based on information about the positions of surrounding vehicles, give the probability of being in each of the current number of lanes. The object positions are provided by one or several low-cost on-board perception sensors. The derived Bayesian filter is evaluated on real data from a prototype self-driving car. Preliminary results show that when other vehicles are present, the proposed method is able to estimate the lane of travel with high probability.