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 hybrid 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 network throughput, as well as local information, such as vehicle positions and velocities. Obtaining this information requires updating existing road infrastructure with vast sensor networks and communication to potentially unwilling human drivers. We consider image observations as the alternative for hybrid traffic control via RL: 1) images are readily available 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 network to network; and 3) images only require communication to equipment. In this work, we show that robot vehicles using image observations can achieve similar performance to using precise information on networks, including ring, figure eight, merge, bottleneck, and intersections. We also demonstrate increased performance (up to 26%) in certain cases on tested networks, 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长期问题。现有交通控制方法在缓解当前拥堵水平方面效果有限，这促使研究者探索利用机器人车辆的想法，因为道路上不同自动化水平的车辆数量不断增加。这催生了混合交通控制，即通过强化学习让机器人车辆调节人类驾驶车辆。然而，大多数现有研究使用涉及全局信息（如网络吞吐量）以及局部信息（如车辆位置和速度）的精确观测值。获取这些信息需要更新现有道路基础设施，部署大规模传感器网络，并向可能不愿配合的人类驾驶员传输数据。我们考虑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）图像可通过卫星图像、车载摄像头系统和交通监控系统轻松获取；2）图像无需在不同网络间完全重新设计观测空间；3）图像仅需与设备进行通信。在本研究中，我们展示了使用图像观测的机器人车辆在环形路、八字形路、合流路、瓶颈路和交叉口等网络上能达到与使用精确信息相似的性能。我们还证明了在仅使用局部交通信息而非全局交通信息的情况下，在部分测试网络上性能提升最高可达26%。