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This paper presents a multi-sensor fusion strategy for a novel road-matchingmethod designed to support real-time navigational features within advanceddriving-assistance systems. Managing multihypotheses is a useful strategy forthe road-matching problem. The multi-sensor fusion and multi-modal estimationare realized using Dynamical Bayesian Network. Experimental results, using datafrom Antilock Braking System (ABS) sensors, a differential Global PositioningSystem (GPS) receiver and an accurate digital roadmap, illustrate theperformances of this approach, especially in ambiguous situations.

Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching