Optimized IoT Based Decision Making For Autonomous Vehicles In Intersections

Applications that use communication networks and distributed systems to control traffic have high latency, especially in critical situations. The performance of these applications largely depends on the computational delay of algorithms that run on local or central processors. Therefore, providing an optimized solution to minimize this delay to a tolerable range is highly needed. This article studies a method in which autonomous vehicles around an intersection try to control the intersection traffic efficiently by communicating and interacting with each other and road-side smart devices. This problem can be addressed in the form of a network utility maximization problem. To achieve a solution that is close to an optimal solution, a gradient descent algorithm with a fixed step size can be utilized. It is necessary to find a balance between latency and accuracy, which leads to finding a velocity close to the optimal velocity. The number of loop repetitions in the scheduling algorithm, determines the latency in preparation for making the proper schedule for autonomous vehicles. In this work, we propose an approach to provide an optimized schedule for autonomous vehicles in intersections considering pedestrian traffic. Autonomous vehicles are able to communicate with each other and road side unites. However, surveillance cameras are required to observe pedestrians passing the intersection. Hence, we utilize cameras, smart sensors, processors, and communication equipment embedded in autonomous vehicles and road side unites, to collect the required data, process it, and distribute the calculated optimal decision to autonomous vehicles. To simulate the traffic behaviors resulting from applying the proposed solution, Simulation of Urban Mobility software is used.