Reinforcement learning-based adaptive speed controllers in mixed autonomy condition

TL;DR

This paper develops an RL-based adaptive speed controller for AVs in mixed traffic, modeling traffic dynamics with PDE-ODE systems, and demonstrates its effectiveness through simulations in improving traffic flow.

Contribution

It introduces a novel RL-based control policy for AVs in mixed traffic using PDE-ODE modeling, advancing adaptive traffic management techniques.

Findings

Controller improves traffic flow in simulations

Reduces congestion with AV integration

Demonstrates effectiveness of PDE-ODE modeling

Abstract

The integration of Automated Vehicles (AVs) into traffic flow holds the potential to significantly improve traffic congestion by enabling AVs to function as actuators within the flow. This paper introduces an adaptive speed controller tailored for scenarios of mixed autonomy, where AVs interact with human-driven vehicles. We model the traffic dynamics using a system of strongly coupled Partial and Ordinary Differential Equations (PDE-ODE), with the PDE capturing the general flow of human-driven traffic and the ODE characterizing the trajectory of the AVs. A speed policy for AVs is derived using a Reinforcement Learning (RL) algorithm structured within an Actor-Critic (AC) framework. This algorithm interacts with the PDE-ODE model to optimize the AV control policy. Numerical simulations are presented to demonstrate the controller's impact on traffic patterns, showing the potential of AVs to improve traffic flow and reduce congestion.