Enhancing Urban Traffic Management with Visible Light Communication and Reinforcement Learning

Abstract

This paper introduces Visible Light Communication (VLC) to enhance traffic signal efficiency and vehicle trajectory management at urban intersections. A multi-intersection traffic control system is proposed, integrating VLC localization services with learning-based traffic signal control. VLC facilitates communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. By leveraging vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) interactions, joint transmission and data collection are achieved via mobile optical receivers. The system aims to reduce waiting times for pedestrians and vehicles while improving overall traffic safety. It is designed to be flexible and adaptive, accommodating diverse traffic movements during multiple signal phases. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow between intersections, enhancing the overall road network performance.