LAUSR

Connected autonomous vehicles can significantly improve the safety and mobility of urban transportation systems. However, these systems are vulnerable to model uncertainties, wireless communication impairments, and external disturbances. In this article, we propose a new autonomous intersection management (AIM) system, called hierarchical model predictive control (HMPC). In HMPC, the intersection coordination unit (ICU) in a global centralized layer is responsible for assigning a safe speed to each vehicle while minimizing the system's cost. In the Local decentralized layer, each vehicle is responsible for tracking the reference speed assigned by the ICU, while avoiding collisions. In our method, each vehicle can use its own sensors to monitor its close surroundings, and take its own decisions on its movements, independent on the control decisions sent from the ICU. We investigate the safety, scalability and robustness of HMPC compared with two well-known AIM methods based on centralized and decentralized control strategies. For the evaluation, we use simulation of urban mobility (SUMO). Further, we study the scalability and performance of the algorithms in the presence of communication impairments associated with wireless channels. Our simulation results show that HMPC can safely handle high traffic flow rates. Also, HMPC is robust to uncertainties caused by the wireless communication.