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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Intelligent Transportation Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
