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The target of reducing travel time only is insufficient to support thedevelopment of future smart transportation systems. To align with the UnitedNations Sustainable Development Goals (UN-SDG), a further reduction of fuel andemissions, improvements of traffic safety, and the ease of infrastructuredeployment and maintenance should also be considered. Different from existingwork focusing on the optimization of the control in either traffic light signal(to improve the intersection throughput), or vehicle speed (to stabilize thetraffic), this paper presents a multi-agent Deep Reinforcement Learning (DRL)system called CoTV, which Cooperatively controls both Traffic light signals andConnected Autonomous Vehicles (CAV). Therefore, our CoTV can well balance theachievement of the reduction of travel time, fuel, and emissions. In themeantime, CoTV can also be easy to deploy by cooperating with only one CAV thatis the nearest to the traffic light controller on each incoming road. Thisenables more efficient coordination between traffic light controllers and CAV,thus leading to the convergence of training CoTV under the large-scalemulti-agent scenario that is traditionally difficult to converge. We give thedetailed system design of CoTV and demonstrate its effectiveness in asimulation study using SUMO under various grid maps and realistic urbanscenarios with mixed-autonomy traffic.

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