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

TL;DR

This paper introduces a macroscopic PDE-ODE model for vehicle platooning in mixed-autonomy traffic, enhancing Dyna-style learning to reduce fuel consumption by over 10% through virtual experiences.

Contribution

It develops a novel coupled PDE-ODE model for platooning and integrates it into a Dyna-style learning framework to improve data efficiency and fuel savings.

Findings

Achieved a 10.11% reduction in fuel consumption.

Validated the macroscopic model's effectiveness in mixed-autonomy traffic.

Enhanced Dyna-style learning with virtual experiences for platoon control.

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.