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

Yuguang Wei, Cafer Avcı, Jiangtao Liu, Baloka Belezamo, Nizamettin Aydın, Li, Pengfei(Taylor) and Xuesong Zhou

Transportation Research Part B: Methodological, 2017, vol. 106, issue C, 102-129

Abstract: 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.

Keywords: Traffic flow management; Autonomous vehicle; Vehicle trajectory optimization; Car-following model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.trb.2017.10.012

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