Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models - Wei, Yuguang; Avci, Cafer; Liu, Jiangtao; Belezamo, Baloka; Aydin, Nizamettin; Li, Pengfei (Taylor); Zhou, Xuesong (PERGAMON-ELSEVIER SCIENCE LTD, 2017)
Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords: Traffic flow management; Autonomous vehicle; Vehicle trajectory optimization; Car-following model; PART I; COMMUNICATION-SYSTEMS; VEHICLE AUTOMATION; FLOW OPTIMIZATION; TRAFFIC FLOW; TIME; DESIGN; SAFETY; ASSIGNMENT; BEHAVIOR; Business & Economics; Engineering; Operations Research & Management Science; Transportation