Cooperative Cruising: Reinforcement Learning-Based Time-Headway Control for Increased Traffic Efficiency

TL;DR

This paper introduces a reinforcement learning-based system for adaptive cruise control that dynamically manages time-headways in multi-lane highway scenarios, improving traffic flow without requiring advanced vehicle capabilities.

Contribution

It presents the first RL-based approach to optimize highway traffic efficiency using existing connectivity and perception technologies in realistic multi-lane simulations.

Findings

Improved traffic flow compared to human-like driving in simulations

Effective real-time communication of time-headways near bottlenecks

Scalable approach leveraging existing vehicle-to-infrastructure connectivity

Abstract

The proliferation of connected automated vehicles represents an unprecedented opportunity for improving driving efficiency and alleviating traffic congestion. However, existing research fails to address realistic multi-lane highway scenarios without assuming connectivity, perception, and control capabilities that are typically unavailable in current vehicles. This paper proposes a novel AI system that is the first to improve highway traffic efficiency compared with human-like traffic in realistic, simulated multi-lane scenarios, while relying on existing connectivity, perception, and control capabilities. At the core of our approach is a reinforcement learning based controller that dynamically communicates time-headways to automated vehicles near bottlenecks based on real-time traffic conditions. These desired time-headways are then used by adaptive cruise control (ACC) systems to adjust their following distance. By (i) integrating existing traffic estimation technology and low-bandwidth vehicle-to-infrastructure connectivity, (ii) leveraging safety-certified ACC systems, and (iii) targeting localized bottleneck challenges that can be addressed independently in different locations, we propose a potentially practical, safe, and scalable system that can positively impact numerous road users.