Safe and Robust Autonomous Intersection Management Methods

Abstract: Connected Autonomous Vehicles (AV)s can transform urban transportation systems and have the potential to improve the safety and efficiency, since human errors and distractions are removed. However, these systems are vulnerable to model uncertainties, communication impairments associated with the wireless communication, and external disturbances. As a result, vehicles need to drive at low speed and have a large safety distance between vehicles in order to guarantee a safe traveling in the road network. In addition, intersections along the road network inherently slow down the speed of the traffic stream, which may result in congestion. However, when the traffic flow rate is high and approaches the maximum capacity of the intersection, vehicles need to fully stop for periods of time. This has a significant impact on the efficiency of the transportation system.In the work presented in this thesis, we explore Autonomous Intersection Management (AIM) methods based on different control strategies with the ultimate goal to develop control methods that can be deployed in operational systems. We have mainly investigated the feasibility and implementation challenges of control strategies in a fully autonomous system in the presence of communication impairments associated with wireless channels. We design a solution, a hierarchical control strategy, which is safe and robust against uncertainties, and also works for high traffic demands and speeds.We evaluated the robustness, scalability and performance of the investi- gated strategies in a realistic urban mobility simulator Simulation of Urban MObility (SUMO) in the presence of communication impairments associated with wireless channels.