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Deep Reinforcement Learning (DRL) uses diverse, unstructured data and makesRL capable of learning complex policies in high dimensional environments.Intelligent Transportation System (ITS) based on Autonomous Vehicles (AVs)offers an excellent playground for policy-based DRL. Deep learningarchitectures solve computational challenges of traditional algorithms whilehelping in real-world adoption and deployment of AVs. One of the mainchallenges in AVs implementation is that it can worsen traffic congestion onroads if not reliably and efficiently managed. Considering each vehicle'sholistic effect and using efficient and reliable techniques could genuinelyhelp optimise traffic flow management and congestion reduction. For thispurpose, we proposed a intelligent traffic control system that deals withcomplex traffic congestion scenarios at intersections and behind theintersections. We proposed a DRL-based signal control system that dynamicallyadjusts traffic signals according to the current congestion situation onintersections. To deal with the congestion on roads behind the intersection, weused re-routing technique to load balance the vehicles on road networks. Toachieve the actual benefits of the proposed approach, we break down the datasilos and use all the data coming from sensors, detectors, vehicles and roadsin combination to achieve sustainable results. We used SUMO micro-simulator forour simulations. The significance of our proposed approach is manifested fromthe results.

Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing