论文标题
D-ACC：基于深Q学习的坡道的高速公路动态自适应巡航控制
D-ACC: Dynamic Adaptive Cruise Control for Highways with Ramps Based on Deep Q-Learning
论文作者
论文摘要
自适应巡航控制系统（ACC）系统允许车辆自动保持所需的前进距离。它越来越被商用车采用。最近的研究表明，有效使用ACC可以通过适应当前的交通状况来改善前进距离的交通流量。在本文中，我们证明了最先进的智能ACC系统在高速公路上的坡道上的性能较差，这是由于基于模型的方法的限制，这些方法未正确考虑到确定最佳前进距离的坡道上的交通动态。然后，我们根据深入的强化学习提出了一个动态自适应巡航控制系统（D-ACC），该学习根据动态变化的主要道路和坡道有效地适应了前进距离，以优化交通流量。在许多交通情况下，通过交通模拟器（SUMO）和车辆到所有通信（V2X）网络模拟器（静脉）的结合进行了广泛的模拟。我们证明，与带有坡道的高速公路领域中最先进的智能ACC系统相比，D-ACC的交通流量最多可提高70％。
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.
