Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments

TL;DR

This paper introduces a two-layer control strategy for autonomous vehicles that improves traffic flow and reduces energy consumption in mixed traffic environments, demonstrated through simulations and real-world experiments.

Contribution

The paper presents a novel two-layer control method for AVs that predicts downstream traffic states and maintains safe headways, tested in large-scale real-world experiments.

Findings

Over 15% energy savings in simulations

Effective congestion mitigation with only 4% AV penetration

Successful deployment on 100 AVs in real traffic

Abstract

Autonomous driving systems present promising methods for congestion mitigation in mixed autonomy traffic control settings. In particular, when coupled with even modest traffic state estimates, such systems can plan and coordinate the behaviors of automated vehicles (AVs) in response to observed downstream events, thereby inhibiting the continued propagation of congestion. In this paper, we present a two-layer control strategy in which the upper layer proposes the desired speeds that predictively react to the downstream state of traffic, and the lower layer maintains safe and reasonable headways with leading vehicles. This method is demonstrated to achieve an average of over 15% energy savings within simulations of congested events observed in Interstate 24 with only 4% AV penetration, while restricting negative externalities imposed on traveling times and mobility. The proposed strategy that served as part of the "speed planner" was deployed on 100 AVs in a massive traffic experiment conducted on Nashville's I-24 in November 2022.