Constrained stochastic hybrid system modeling to road map - GPS integration for vehicle positioning

Abstract

This paper considers the vehicle positioning problem of an automobile on-board navigation system which is mainly supported by Global Positioning System (GPS). To complement GPS, the existing navigation techniques incorporate additional vehicle sensors, together with the map data to match the positioning solution with the road map. We propose an advanced map-matching algorithm that integrates the additional map data with GPS and vehicle sensor measurements. Specifically, the detailed road map data, where individual road segments are subdivided into lanes, can impose further restriction on the vehicle as it is likely to move along the center of each lane and is rarely at boundary. Such a tendency can be mathematically interpreted as a statistical constraint in our map-matching algorithm. In addition, the lane change behavior of the vehicle can be accounted for by the discrete modes assigned to the individual road lanes. Then, the overall positioning process can be posed as a constrained stochastic hybrid system framework. The proposed map-matching algorithm provides more reliable vehicle positioning (continuous state estimate) and lane discrimination (discrete mode estimate) without needing costly sensor resources.