Traffic congestion is a persistent problem in our society. Existing methods for traffic control have proven futile in alleviating current congestion levels leading researchers to explore ideas with robot vehicles given the increased emergence of vehicles with different levels of autonomy on our roads. This gives rise to mixed traffic control, where robot vehicles regulate human-driven vehicles through reinforcement learning (RL). However, most existing studies use precise observations that involve global information, such as environment outflow, and local information, i.e., vehicle positions and velocities. Obtaining this information requires updating existing road infrastructure with vast sensor environments and communication to potentially unwilling human drivers. We consider image observations as the alternative for mixed traffic control via RL: 1) images are ubiquitous through satellite imagery, in-car camera systems, and traffic monitoring systems; 2) images do not require a complete re-imagination of the observation space from environment to environment; and 3) images only require communication to equipment. In this work, we show robot vehicles using image observations can achieve similar performance to using precise information on environments, including ring, figure eight, intersection, merge, and bottleneck. In certain scenarios, our approach even outperforms using precision observations, e.g., up to 26% increase in average vehicle velocity in the merge environment and a 6% increase in outflow in the bottleneck environment, despite only using local traffic information as opposed to global traffic information.

翻译：交通拥堵是我们社会中的一个持久性问题。现有交通控制方法在缓解当前拥堵水平方面已被证明效果有限，这促使研究人员探索利用自动驾驶车辆（robot vehicles）的方案——鉴于道路上不同自动驾驶水平的车辆日益增多。这催生了混合交通控制（mixed traffic control），即通过强化学习（reinforcement learning, RL）让自动驾驶车辆调节人类驾驶的车辆。然而，现有大多数研究依赖于精确观测，涉及全局信息（如环境流量）和局部信息（即车辆位置与速度）。获取此类信息需要更新现有道路基础设施，配备大量传感器环境，并与可能不情愿的人类驾驶员进行通信。我们提出将图像观测作为基于强化学习的混合交通控制的替代方案：1) 图像通过卫星影像、车载摄像头系统和交通监控系统已无处不在；2) 图像无需在不同环境之间完全重新设计观测空间；3) 图像仅需与设备进行通信。本研究中，我们展示使用图像观测的自动驾驶车辆在环形、八字形、交叉口、汇入和瓶颈等环境中能够达到与使用精确信息相似的性能。在某些场景下，我们的方法甚至优于精确观测方法——例如，在汇入环境中平均车速提升高达26%，在瓶颈环境中流量提升6%，尽管仅使用了局部交通信息而非全局交通信息。