An Urban Map Matching Algorithm Using Rough Sensor Data

Map matching is an important task in vehicle navigation applications. In this paper, we present a map matching algorithm for complex urban environment, using only low cost sensors including GPS, speedometer and gyroscope. To deal with the complexity and inaccurate information, a novel weighting method is proposed to describe likelihoods of candidate roads, using a distance forgetting factor for history accumulation. Based on this, a finite state machine (FSM) matching engine and a tracing back procedure are included in the algorithm, which are designed especially for the rough data. Simulation results show that the algorithm can deal with the high uncertainties and achieve a high success rate in matching to the road.