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：暂无翻译