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Energy-efficient trajectory design of connected automated vehicles platoon: A unified modeling approach using space-time-speed grid networks

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  • Jiang, Yangsheng
  • Huangfu, Junjie
  • Xiao, Guosheng
  • Zhang, Yongxiang
  • Yao, Zhihong

Abstract

This paper proposes an energy-efficient trajectory design method for a platoon of autonomous modular vehicles (TPAMV) on highway transportation. First, a high-dimensional space-time-velocity network model is adopted to describe the trajectories of vehicle platooning with coupled constraints. The security constraint has been dualized using two dual decomposition methods: Lagrangian relaxation and the Alternating Direction Method of Multipliers (ADMM). Then, we employ an iterative primal and dual optimization framework to develop a sub-problem version of dynamic programming. Finally, we compare the numerical performance of the proposed method with that of the standard optimization solver Gurobi. Numerical experiments show that: (1) The proposed ADMM algorithm demonstrates efficient performance in the small-scale illustrative examples, exhibiting superior solution time compared to Gurobi. (2) In a larger-scale problem, ADMM can significantly reduce solving time by at least threefold and decrease the number of iterations by half or more. (3) The proposed algorithm demonstrates enhanced efficiency by achieving a superior solution within approximately 1 s, even when confronted with ultra-large-scale complex platooning scenarios. Therefore, the research findings can offer valuable theoretical support for optimizing the intricate platoon trajectories of AMVs.

Suggested Citation

  • Jiang, Yangsheng & Huangfu, Junjie & Xiao, Guosheng & Zhang, Yongxiang & Yao, Zhihong, 2025. "Energy-efficient trajectory design of connected automated vehicles platoon: A unified modeling approach using space-time-speed grid networks," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224040702
    DOI: 10.1016/j.energy.2024.134292
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    References listed on IDEAS

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