Abstract
The issue of collision avoidance in road vehicles has been investigated from many different points of view. An interesting approach for Road Vehicle Collision Assistance Support Systems (RVCASS) is based on the creation of a scene of the vehicles involved in a potentially conflictive traffic situation. This paper proposes a neuro-fuzzy approach for dynamic classification of the vehicles roles in a scene. For that purpose, different maneuver state models for longitudinal movements of road vehicles have been defined, and a prototype has been equipped with INS (Inertial Navigation Systems) and GPS (Global Positioning System) sensors. Trials with real data show the suitability of the proposed neuro-fuzzy approach for solving support to the problem under consideration.
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Zamora-Izquierdo, M.A., Toledo-Moreo, R., Valdés-Vela, M., Gil-Galván, D. (2007). Neuro-fuzzy Based Maneuver Detection for Collision Avoidance in Road Vehicles. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_45
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DOI: https://doi.org/10.1007/978-3-540-73055-2_45
Publisher Name: Springer, Berlin, Heidelberg
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