Ego-lane estimation for downtown lane-level navigation

Abstract

We present an ego-lane estimation algorithm for downtown lane-level navigation. It is capable of determining the currently used lane reliably, using sensors available in a modern production vehicle, such as odometry, GPS, visual lane-marking detection, and radar-based object detection. The method employs a particle filter with a novel step that combines the importance weight update and sampling. This step avoids performance deterioration in case of sparse particle sets even when the likelihood is very tight compared to the predicted particle set. Preprocessed odometry data allow for a further performance increase. In an extensive test in downtown scenarios on real roads with up to seven lanes, it achieves error probabilities below 1% in the 95th percentile at availabilities above 95%.