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.Real-time positioning system is a very important module in modern navigation systems and transportation safety applications. Digital road maps became very important sources of information for these kinds of applications. As a matter of fact, real-time positioning on digital road maps allows the driving assistance module to accurately localize the vehicle on the map, facilitate operations (such as route calculation), and support Advanced Driver Assistance System Applications (ADAS), such as Adaptive Cruise Control (ACC), adaptive lighting control, collision warning, lane departure warning, etc. In this context, roadmatching is the problem of determining the location of a vehicle with respect to a roadmap. The problem of road-matching becomes complicated when one seeks to obtain a reliable, precise, and robust vehicle location on the road network.Outdoor positioning systems often rely on Global Positioning Systems (GPS) because of its affordability and convenience; however, GPS suffers from satellite masks occurring in urban environments, under bridges and tunnels, and in forests. GPS appears, then, as an intermittently-available positioning systemThe authors wish to acknowledge the HEUDIASYC laboratory for support. We thank Mr. Philippe Bonnifait for his contribution.