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

2024/05/03 by Yichuan Zou, Jin Li, Zou, Yichuan +3 · 1 citation
Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2405.02062

openalex publication_date 2024/05/03 · openalex created_date 2024/05/10 · openalex updated_date 2026/07/28

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.

Cited by

• Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach

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