Multi agent intersection management for autonomous vehicles

Traditional transportation systems cause traffic congestion especially at the intersections as the number of vehicles keeps increasing. This is also the main reason of air pollution and time wasted. Most of the people lose their time and money because of traffic congestion. Thanks to recent research on autonomous vehicles, intelligent transportation and wireless communication systems, efficient traffic management at the intersections with multi-agent scheduling methods will be possible. The main objective of this thesis is intersection coordination for multi-agent systems by using time-based optimization and Model Predictive Control (MPC) methods while considering fuel economy at the intersections. Existing results show that these methods are efficient in comparison to the traditional methods when all the vehicles are autonomous. However, better trajectory planning can improve the total delay of the system. Besides, including fuel economy in the optimization function can also decrease fuel consumption which would be good for both humanity and nature. In this thesis, the effect of trajectory planning and different communication ranges on time-based optimization method is studied. It is shown that a wider communication range and better trajectory planning provide less time delay. Another contribution of this thesis is to propose centralized and decentralized MPC algorithms by including fuel consumption related costs in the objective function. As a result, fuel consumption is decreased at the expense of an increase in the time delay. In simulations, it is also observed that centralized MPC performs better than decentralized MPC.