Abstract
The performance of all navigation and tracking algorithms for road-bound vehicles can be improved by utilizing the trajectory constraint imposed from the road network. We refer to this approach as road-assisted navigation and tracking. Further, we refer to the process of incorporating the road constraint into the standard filter algorithms by dynamic map matching. Basically, dynamic map matching can be done in three different ways: (1) as a virtual measurement, (2) as a state noise constraint, or (3) as a manifold estimation problem where the state space is reduced. Besides this basic choice of approach, we survey the field from various perspectives: which filter that is applied, which dynamic model that is used to describe the motion of the vehicle, and which sensors that are used and their corresponding sensor models. Various applications using real data are presented as illustrations.
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Acknowledgments
This work has been supported by the Swedish Research Council (VR) under the Linnaeus Center CADICS, the VR project grant Extended Target Tracking, the SSF excellence center MOVIII, the Vinnova excellence center FOCUS and the strategic research environments ELLITT in the ICT area and Security Link in the security area.
The authors would also like to thank NIRA Dynamics, SAAB EDS, and FOI for initiating a series of stimulating master thesis in this area. Special thanks to Mussa Bshara for collecting the WiMAX data in Sect. 6.2.
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Gustafsson, F., Orguner, U., Schön, T.B., Skoglar, P., Karlsson, R. (2012). Navigation and Tracking of Road-Bound Vehicles Using Map Support. In: Eskandarian, A. (eds) Handbook of Intelligent Vehicles. Springer, London. https://doi.org/10.1007/978-0-85729-085-4_16
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DOI: https://doi.org/10.1007/978-0-85729-085-4_16
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