In order to guarantee the safety of autonomous vehicles (AV), improve passenger comfort, and increase traffic efficiency, we aim to develop innovative learning-based control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic by a combined use of reinforcement learning and optimal control techniques.
Collaborative driving results are available for vehicle-level controls and mostly focused on architecture and human-in-the-loop approaches. We aim at a macroscopic and network-level approach to exploit the potential impact of collaborative driving.
Trucks have been an integral part of the freight movement network in distributing goods and services to various communities; however, a percentage of these trucks are often overloaded beyond legal load limits. A more practical and efficient OW enforcement scheme would be needed to discourage the trucking industry from overloading their fleets.
Cities like NYC and Seattle need to deal with significant growth of urban deliveries as a result of increasing e-commerce compounded by increased stay-at-home behavior due to COVID-19. We propose to develop a citywide model of truck network flows, one that relates changes to truck routes to changes in truck tours or to time-of-day congestion pricing policies.