D-ACC: Dynamic Adaptive Cruise Control for Highways with Ramps Based on Deep Q-Learning

TL;DR

This paper introduces D-ACC, a deep reinforcement learning-based adaptive cruise control system that dynamically adjusts headway distances to improve highway traffic flow, especially around ramps, outperforming existing ACC systems.

Contribution

The paper presents a novel D-ACC system using deep Q-learning that effectively adapts to traffic dynamics on highways with ramps, addressing limitations of model-based approaches.

Findings

D-ACC improves traffic flow by up to 70% compared to existing ACC systems.

Simulation results show D-ACC's effectiveness across various traffic scenarios.

D-ACC adapts to both main road and ramp traffic conditions dynamically.

Abstract

An Adaptive Cruise Control (ACC) system allows vehicles to maintain a desired headway distance to a preceding vehicle automatically. It is increasingly adopted by commercial vehicles. Recent research demonstrates that the effective use of ACC can improve the traffic flow through the adaptation of the headway distance in response to the current traffic conditions. In this paper, we demonstrate that a state-of-the-art intelligent ACC system performs poorly on highways with ramps due to the limitation of the model-based approaches that do not take into account appropriately the traffic dynamics on ramps in determining the optimal headway distance. We then propose a dynamic adaptive cruise control system (D-ACC) based on deep reinforcement learning that adapts the headway distance effectively according to dynamically changing traffic conditions for both the main road and ramp to optimize the traffic flow. Extensive simulations are performed with a combination of a traffic simulator (SUMO) and vehicle-to-everything communication (V2X) network simulator (Veins) under numerous traffic scenarios. We demonstrate that D-ACC improves the traffic flow by up to 70% compared with a state-of-the-art intelligent ACC system in a highway segment with a ramp.