Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in Mixed-Autonomy Traffic

Yichuan Zou, Li Jin, Xi Xiong
5/3/2024
cs.LG

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Abstract

Platooning of connected and autonomous vehicles (CAVs) plays a vital role in modernizing highways, ushering in enhanced efficiency and safety. This paper explores the significance of platooning in smart highways, employing a coupled partial differential equation (PDE) and ordinary differential equation (ODE) model to elucidate the complex interaction between bulk traffic flow and CAV platoons. Our study focuses on developing a Dyna-style planning and learning framework tailored for platoon control, with a specific goal of reducing fuel consumption. By harnessing the coupled PDE-ODE model, we improve data efficiency in Dyna-style learning through virtual experiences. Simulation results validate the effectiveness of our macroscopic model in modeling platoons within mixed-autonomy settings, demonstrating a notable $10.11\%$ reduction in vehicular fuel consumption compared to conventional approaches.

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Cite this paper

@article{zou2024dynastyle,
  title  = {Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in Mixed-Autonomy Traffic},
  author = {Yichuan Zou and Li Jin and Xi Xiong},
  year   = {2024},
  eprint = {2405.02062},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.02062v1}
}

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