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

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Wei Y., Avcı C., Liu J., Belezamo B., AYDIN N., Li P., ...More

Transportation Research Part B: Methodological, vol.106, pp.102-129, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 106
  • Publication Date: 2017
  • Doi Number: 10.1016/j.trb.2017.10.012
  • Journal Name: Transportation Research Part B: Methodological
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.102-129
  • 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
  • Yıldız Technical University Affiliated: Yes


Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation trans- portation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for schedul- ing 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 re- action 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.