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 practical, safe, and scalable system that can positively impact numerous road users.

翻译：网联自动驾驶车辆的普及为提高驾驶效率和缓解交通拥堵带来了前所未有的机遇。然而，现有研究未能解决现实的多车道高速公路场景，且通常假设了当前车辆普遍不具备的网联、感知与控制能力。本文提出了一种新颖的人工智能系统，该系统首次在模拟的现实多车道场景中，相比类人交通流提升了高速公路交通效率，同时仅依赖现有的网联、感知与控制能力。我们方法的核心是一个基于强化学习的控制器，它根据实时交通状况，动态地向瓶颈区域附近的自动驾驶车辆通信期望的车头时距。这些期望车头时距随后被自适应巡航控制系统用于调整其跟车距离。通过（i）整合现有的交通估计技术与低带宽车路协同通信，（ii）利用经过安全认证的自适应巡航控制系统，以及（iii）针对可在不同地点独立处理的局部瓶颈难题，我们提出了一种实用、安全且可扩展的系统，能够对众多道路使用者产生积极影响。