Supervised dFasArt: A Neuro-fuzzy Dynamic Architecture for Maneuver Detection in Road Vehicle Collision Avoidance Support Systems

Authors:
Rafael Toledo;Miguel Pinzolas;Jose Manuel Cano-Izquierdo
Affiliations:
Dept. of Electronics, Computer Technology and Projects,;Dept. of Systems Engineering and Automation, Technical University of Cartagena,;Dept. of Systems Engineering and Automation, Technical University of Cartagena,
Venue:
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
Year:
2007

Citing 1
Cited 2

Multisensor Data Fusion

Multisensor Data Fusion

A New Metric for Supervised dFasArt Based on Size-Dependent Scatter Matrices That Enhances Maneuver Prediction in Road Vehicles

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Maneuver prediction for road vehicles based on a novel neuro-fuzzy dynamic architecture

Robotics and Autonomous Systems

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Abstract

A supervised version of dFasArt, a neuronal architecture based method that employs dynamic activation functions determined by fuzzy sets is used for solving support of the problem of inter-vehicles collisions in roads. The dynamic character of dFasArt minimizes problems caused by noise in the sensors and provides stability on the predicted maneuvers. To test the proposed algorithm, several experiments with real data have been carried out, with good results.