Intelligent Flow Control of Connected Driverless Vehicles in Smart City Intersections

Date
2021
Authors
Sahba, Amin
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Abstract

A Smart city as a complex system includes many different subsystems interacting together. Smart transportation is one of the subsystems in a smart city which consist of roads, vehicles, intersections, roadside units, traffic control systems, and so on. Many of these components needs to be connected to provide a smooth safe traffic in a smart city. Therefore, applications that use communication networks and distributed systems to control traffic need low latency, especially in critical situations. The performance of these programs largely depends on the computational latency of algorithms running on local or central processors. Hence, providing an optimized solution to minimize this delay within a tolerable range is much needed. This work studies the way in which self-driving vehicles around a road junction try to effectively manage and control the flow of traffic through the crossing by communicating and interacting with each other and smart devices along the road. It is essential to find a balance between delay and accuracy that leads to have a smooth and safe traffic flow at the intersection. In this work, we propose a way to properly manage the flow of self-driving vehicle traffic at road junctions, taking into account human driven vehicles. Self-driving vehicles are able to communicate with each other and smart devices along the road. However, surveillance cameras are needed to observe human driven traffic at the intersection. Therefore, we use cameras, smart sensors, processors, and communication equipment embedded in self-driving vehicles and roadside smart devices to collect data, process it, and generate proper instructions to manage self-driving vehicles traffic flow at intersections. In this research we have used Simulation of Urban Mobility software to simulate traffic behaviors resulting from the use of the proposed solution. The simulation shows a smooth flow of traffic in a simple junction. A deep reinforcement learning approach is then proposed to manage traffic flow in multiple lane junctions. The simulation results illustrate this method is able to manage the traffic flow of mixed traffic in multiple lane junctions.

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Electrical and Computer Engineering