IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Neuro-fuzzy Based Maneuver Detection for Collision Avoidance in Road Vehicles
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
dFasArt: Dynamic neural processing in FasArt model
Neural Networks
IMM-based lane-change prediction in highways with low-cost GPS/INS
IEEE Transactions on Intelligent Transportation Systems
New likelihood updating for the IMM approach application to outdoor vehicles localization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IEEE Transactions on Intelligent Transportation Systems
Incremental Hierarchical Discriminant Regression
IEEE Transactions on Neural Networks
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Collision avoidance systems for road vehicles may benefit from timely predictions of vehicle maneuvers. This article presents a novel approach for the prediction of maneuvers that copes with noisy measurements and is based on a supervised version of a dynamic FasArt method (SdFasArt). Additionally, the use of size-dependent scatter matrices to compute the activation of the neurons makes the algorithm more adaptable to different data distributions. The results obtained in real tests confirm the goodness of the method.