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This paper presents a road selection strategy for novel road-matching methods that are designed to support real-time navigational features within Advanced Driving-Assistance Systems (ADAS). Selecting the most likely segment(s) is a crucial issue for the road-matching problem. The selection strategy merges several criteria using Belief theory. Particular attention is given to the development of belief functions from measurements and estimations of relative distances, headings, and velocities. Experimental results using data from antilock brake system sensors, the differential Global Positioning System receiver, and the accurate digital roadmap illustrate the performances of this approach, particularly in ambiguous situations.
Najjar et al. (2007) studied this question.