Intersection Management System for Autonomous Vehicles using a Fuzzy Inference System

Abstract

An intersection management system (IMS) is a critical element in a transport system. Primarily intersection points are controlled with a time-based traffic signal. With the rapid growth in autonomous vehicles (AVs) and intelligent transport systems (ITSs), intersection management is an important area of research and development. The primary direction of this study is designing a management system that can communicate with vehicles, respond to them according to the traffic situation, and significantly reduce and avoid fatal situations like accidents. In this paper, a two-layered fuzzy logic-based controller is proposed to automate a cross-sectional intersection. Vehicles convey their particular information to the intersection unit, and the controller decides the specific action for the vehicles. This intersection controller is an IMS. After that, all roadside information is transferred to a centralized IMS (CIMS). The proposed CIMS reacts to a vehicle by giving an action command to leave the intersection. For prediction in a complex and dynamic environment, especially when many relevant factors overlap, fuzzy logic can be efficient. In this paper, fuzzy logic indicates the action for vehicles. Simulations were performed using MATLAB.