Abstract
In previous investigations, a supervised version of a dynamic FasArt method (SdFasArt) proved its capability to supply good results to the problem of maneuver prediction in road vehicles. The dynamic character of dFasArt minimized problems caused by noise in the sensors and provided stability on the predicted maneuvers. This paper presents a new SdFasArt architecture enhanced by the inclusion of size-dependent scatter matrices (SDSM) to compute the activation of the neurons. In this novel approach, the receptive fields of the neurons are capable to rotate and scale in order to better respond to data distributions with a preferred orientation in the input space, what leads to a more efficient classification. The results achieved by both methods in a series of experiments in real scenarios with a probe vehicle show that SDSM-SdFasArt supplies better results in terms of maneuver prediction and number of nodes.
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Toledo, A., Toledo-Moreo, R., Cano-Izquierdo, J.M. (2009). A New Metric for Supervised dFasArt Based on Size-Dependent Scatter Matrices That Enhances Maneuver Prediction in Road Vehicles. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Bioinspired Applications in Artificial and Natural Computation. IWINAC 2009. Lecture Notes in Computer Science, vol 5602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02267-8_48
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DOI: https://doi.org/10.1007/978-3-642-02267-8_48
Publisher Name: Springer, Berlin, Heidelberg
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