Maneuver prediction for road vehicles based on a neuro-fuzzy architecture with a low-cost navigation unit

Authors:
Rafael Toledo-Moreo;Miguel Pinzolas-Prado;Jose Manuel Cano-Izquierdo
Affiliations:
Department of Electronics, Computer Technology and Projects, Technical University of Cartagena, Cartagena, Spain and Research Group of Intelligent Systems, University of Murcia, Murcia, Spain;Department of Systems Engineering and Automation, Technical University of Cartagena, Cartagena, Spain;Department of Systems Engineering and Automation, Technical University of Cartagena, Cartagena, Spain
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2010

Citing 6
Cited 3

Learning from noisy information in FasArt and FasBack neuro-fuzzy systems

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Reliable road vehicle collision prediction with constrained filtering

Signal Processing - Special section: Distributed source coding
dFasArt: Dynamic neural processing in FasArt model

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Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

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High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

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Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis

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Brief paper: Applying neuro-fuzzy model dFasArt in control systems

Engineering Applications of Artificial Intelligence
Maneuver prediction for road vehicles based on a novel neuro-fuzzy dynamic architecture

Robotics and Autonomous Systems
An application of a fuzzy classifier extracted from data for collision avoidance support in road vehicles

Engineering Applications of Artificial Intelligence

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Abstract

Collision avoidance is currently one of the main research areas in road intelligent transportation systems. Among the different possibilities available in the literature, the prediction of abrupt maneuvers has been shown to be useful in reducing the possibility of collisions. A supervised version of dynamic Fuzzy Adaptive System ART-based (dFasArt), which is a neuronal-architecture-based method that employs dynamic activation functions determined by fuzzy sets, is used for maneuver predicting and solving the problem of intervehicle collisions on roads. In this paper, it is shown how the dynamic character of dFasArt minimizes problems caused by noise in the sensors and provides stability on the predicted maneuvers. Several experiments with real data were carried out, and the SdFasArt results were compared with those achieved by an implementation of the Incremental Hierarchical Discriminant Regression (IHDR)-based method, showing the suitability of SdFasArt for maneuver prediction of road vehicles.