Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models

作者：Wei, Yuguang; Avci, Cafer; Liu, Jiangtao; Belezamo, Baloka; Aydin, Nizamettin; Li, Pengfei; Zhou, Xuesong^*

来源：Transportation Research Part B: Methodological , 2017, 106: 102-129.

DOI：10.1016/j.trb.2017.10.012

• Traffic flow management
• Autonomous vehicle
• Vehicle trajectory optimization
• Car-following model

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.

• 出版日期2017-12
• 单位北京交通大学