  This paper presents a multi-sensor fusion strategy for a novel road-matching
method designed to support real-time navigational features within advanced
driving-assistance systems. Managing multihypotheses is a useful strategy for
the road-matching problem. The multi-sensor fusion and multi-modal estimation
are realized using Dynamical Bayesian Network. Experimental results, using data
from Antilock Braking System (ABS) sensors, a differential Global Positioning
System (GPS) receiver and an accurate digital roadmap, illustrate the
performances of this approach, especially in ambiguous situations.
