This article presents a multisensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driver assistance systems. In road navigation, context, integrity, reliability and accuracy are essential qualities for road-matching methods. Particularly, managing multihypotheses is a useful strategy to treat ambiguous situations in the road-matching task. In this study, multisensor fusion and multimodal estimation are realized using a hybrid Bayesian network. To manage multihypothesis, multimodal estimation is proposed. Experimental results, using data from antilock braking system sensors, a differential global positioning system receiver, and an accurate digital roadmap illustrate the performance of the proposed approach, especially in ambiguous situations.