## 2 Background and Related Work
### Innovative Solutions: Integrating Connected Vehicles with VLC  
Advancements in wireless communication technologies and the development of V2V and V2I systems present new opportunities to optimize urban traffic networks by coordinating traffic signal control with driving behaviors [19]. Addressing these challenges, this paper proposes a novel approach that integrates VLC-based localization with learning-driven traffic signal control. The goal is to provide comprehensive management of pedestrian and vehicular traffic, prioritizing reduced waiting times and improved safety in multi-intersection environments.  
CV technology is revolutionizing urban traffic management by enabling V2V command V2I communications. This real-time exchange of traffic and safety data, such as speed, location, and road condition, improves traffic flow, enhances safety, and reduces accidents. Simultaneously, Visible Light Communication (VLC) is emerging as a groundbreaking solution for traffic management. By modulating the intensity of LED lights, commonly used in streetlights, traffic signals, and vehicle headlights, VLC transmits data while maintaining its primary function of illumination. This dual-purpose capability allows VLC to integrate seamlessly into existing infrastructure, transforming ordinary light sources into effective communication tools for smart urban environments. With its ability to deliver high-speed, localized communication, VLC is a key enabler in the development of smarter and more adaptive traffic management solutions.  
To develop an intelligent control system for safe vehicle management at intersections utilizing V2V, V/P2I, and I2V/P communications, RL concepts are applied. RL trains agents by rewarding desired behaviors and penalizing undesired ones [20]. The effectiveness of the proposed V-VLC system in multi-intersection scenarios is evaluated using the agent-based Simulation of Urban Mobility (SUMO) platform [21]. SUMO is an open-source traffic simulation software developed to model complex traffic scenarios. It allows for the simulation of various traffic networks, vehicle types, and mobility patterns, making it widely applicable for urban traffic analysis and studies involving autonomous vehicles. We utilized different levels of simulation models within SUMO to represent the traffic scenario. These levels include the following:  
* _Microscopic models_, which simulate individual vehicle behaviors, including lane-changing, car-following, and interactions with traffic signals.
* _Macroscopic models_, which model traffic flow using averaged variables, such as vehicle density and speed, over larger sections of a network.  
Through experience, the agent learns to avoid negative outcomes and focus on positive actions. Traffic lights in SUMO are managed by the learning agent, which adjusts traffic signal phases based on its decisions to optimize traffic flow. The agent aims to explore new states while maximizing cumulative rewards to devise an optimal policy. A dynamic phasing diagram and a state matrix, based on total accumulated time, illustrate the approach.  
By combining CV's real-time data sharing with VLC's high-precision communication, urban traffic systems can achieve synchronized control, improving traffic efficiency and safety at intersections and across multi-intersection networks. Together, these technologies form the backbone of an intelligent, adaptive traffic management ecosystem.