Probabilistic Localization Method for Trains

Abstract

The localization of trains in a railway network is necessary for train control or applications like autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach is demanding a robust, precise and track selective localization. Satellite navigation systems may be useful for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety-of-life system. In order to cope with position related uncertainties and sensor errors, a probabilistic approach with a Bayesian filter is proposed. In this paper we present a train localization approach by a particle filter fusing multiple on-board train sensors measurements with a track map. The particle filter is estimating a topologic position directly from the track map.