CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learning

TL;DR

This paper introduces a novel CAV-AHDV-CAV car-following framework using deep reinforcement learning to reduce traffic oscillations and improve flow in mixed traffic scenarios with both CAVs and HDVs.

Contribution

The paper proposes a new car-following structure that treats multiple HDVs as a single entity and employs deep reinforcement learning to mitigate traffic oscillations.

Findings

Outperforms baselines in collision avoidance.

Maintains equilibrium with preceding and leading vehicles.

Achieves lowest standard deviation of time headway.

Abstract

Connected and Automated Vehicles (CAVs) offer a promising solution to the challenges of mixed traffic with both CAVs and Human-Driven Vehicles (HDVs). A significant hurdle in such scenarios is traffic oscillation, or the "stop-and-go" pattern, during car-following situations. While HDVs rely on limited information, CAVs can leverage data from other CAVs for better decision-making. This allows CAVs to anticipate and mitigate the spread of deceleration waves that worsen traffic flow. We propose a novel "CAV-AHDV-CAV" car-following framework that treats the sequence of HDVs between two CAVs as a single entity, eliminating noise from individual driver behaviors. This deep reinforcement learning approach analyzes vehicle equilibrium states and employs a state fusion strategy. Trained and tested on diverse datasets (HighD, NGSIM, SPMD, Waymo, Lyft) encompassing over 70,000 car-following instances, our model outperforms baselines in collision avoidance, maintaining equilibrium with both preceding and leading vehicles and achieving the lowest standard deviation of time headway. These results demonstrate the effectiveness of our approach in developing robust CAV control strategies for mixed traffic. Our model has the potential to mitigate traffic oscillation, improve traffic flow efficiency, and enhance overall safety.