Abstract-Roundabout intersections promote a continuous flow of traffic. Roundabouts entry move traffic through an intersection more quickly, and with less congestion on approaching roads. With the introduction of smart vehicles and cooperative decision-making, roundabout management shortens the waiting time and leads to a more efficient traffic without breaking the traffic laws and earning penalties. This paper proposes a novel approach of cooperative behavior strategy in conflict situations between the autonomous vehicles in roundabout using game theory. The game theory presents a strategic decision-making technique between independent agents -players. Each individual player tends to achieve best payoff, by analyzing possible actions of other players and their influence on game outcome. The Prisoner's Dilemma game strategy is selected as approach to autonomous vehicle-to-vehicle (V2V) decision making at roundabout test-bed, because the commonly known traffic laws dictate certain rules of vehicle's behavior at roundabout. It is shown that, by integrating non-zero-sum game theory in autonomous vehicle-to-vehicle (V2V) decision making capabilities, the roundabout entry problem can be solved efficiently with shortened waiting times for individual autonomous vehicles.

Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and

Abstract. In this paper, a new fuzzy-behavior-based algorithm for roundabout intersection management is presented. The algorithm employs cooperative intelligence and includes intelligent vehicles and infrastructure to calculate speed profiles for different vehicles, in order to achieve more comfortable driving profiles, as well to reduce congestion and CO2 emissions. The algorithm uses adaptive spatio-temporal reservation technique and was tested in MATLAB/Simulink environment. The algorithm is designed to function in different scenarios with both cooperative and non-cooperative vehicles, as well as optional intersection infrastructure. Results have show that using the proposed algorithm different vehicle communication types can be successfully combined in order to increase traffic flow through roundabout intersections.

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Abstract. 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.