CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles Using Deep Reinforcement Learning

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction in fuel consumption and emissions, improvements in traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Most existing research in sustainable urban traffic control adjusts either traffic light signals or vehicle speed. Adaptive traffic light signal control can increase the intersection throughput and reduce travel time as well as energy consumption and emissions. Connected Autonomous Vehicles (CAVs) can proactively control vehicle acceleration to achieve more stable traffic nearby with relatively higher driving velocity (i.e., lower fuel consumption and CO 2 emissions) and maintain a safe distance from the surrounding traffic (i.e., longer time-to-collision).