: 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.

To accurately predict traffic information is of great importance in a large number of applications in connection with Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. The most important research is done in the domain of cooperative intelligent transport system (C-ITS). Only minor attention has been given to coordinated maneuvering, since testing with real vehicles which can drive autonomously requires a large-scale infrastructure with important security measures. In this paper, we propose hybrid automaton modelling in Matlab/Simulink/ Stateflow to emulate flexible platooning conditions, analysing how cooperation interactions can be accomplished using inter-vehicle communication and certain control of the vehicles. Such analysis reveals to be necessary in order to establish the improvement of traffic mobility in Intelligent Transportation Systems through cooperation behaviour profile prediction. This study presents an approach towards NARX neural network prediction of flexible Platooning maneuvers profile. In order to estimate prediction, MSE and R were utilized. The study results suggest that in the case of noise in test data, NARX neural network would be an efficient prediction tool, and useful for the prediction mobility in Intelligent Transport systems.

Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.