Empirical evaluation of vehicular models for ego motion estimation

Abstract

Estimating the motion of a vehicle is a crucial requirement for intelligent vehicles. In order to solve this problem using a Bayes filter, an appropriate model of vehicular motions is required. This paper systematically reviews typical vehicular motion models and evaluates their suitability in different scenarios. For that, the results of extensive experiments using accurate reference sensors are presented and discussed in order to provide guidelines for the choice of an optimal model.