Technical gazette, Vol. 25 No. 3, 2018.
Preliminary communication
https://doi.org/10.17559/TV-20170308230100
Hybrid Automaton Based Vehicle Platoon Modelling and Cooperation Behaviour Profile Prediction
Lejla Banjanovic-Mehmedovic
orcid.org/0000-0002-3810-8645
; Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina
Ivana Butigan
; Leftor, Tuzla, Bosnia and Herzegovina
Fahrudin Mehmedovic
; Office Manager and Strategic Account Manager, ABB Representation for Bosnia and Herzegovina, Tuzla, Bosnia and Herzegovina
Mehmed Kantardzic
orcid.org/0000-0002-6861-4434
; Director of Data Mining Lab, Speed School of Engineering, University of Louisville, Louisville, USA
Abstract
Autonomous cooperative driving systems require the integration of research activities in the field of embedded systems, robotics, communication, control and artificial intelligence in order to create a secure and intelligent autonomous drivers behaviour patterns in the traffic. Beside autonomous vehicle management, an important research focus is on the cooperation behaviour management. In this paper, we propose hybrid automaton modelling to emulate flexible vehicle Platoon and vehicles cooperation interactions. We introduce novel coding function for Platoon cooperation behaviour profile generation in time, which depends of vehicles number in Platoon and behaviour types. As the behaviour prediction of transportation systems, one of the primarily used methods of artificial intelligence in Intelligent Transport Systems, we propose an approach towards NARX neural network prediction of Platoon cooperation behaviour profile. With incorporation of Platoon manoeuvres dynamic prediction, which is capable of analysing traffic behaviour, this approach would be useful for secure implementation of real autonomous vehicles cooperation.
Keywords
autonomous vehicles; cyber-physical systems; cooperation behaviour profile; hybrid automaton; Platoon; prediction; system modelling
Hrčak ID:
202645
URI
Publication date:
28.6.2018.
Visits: 2.311 *