This paper addresses the issues related to the Visible Light Communication (VLC) usage in vehicular communication applications. We propose a Visible Light Communication system based on Vehicle-to-Vehicle, Vehicleto-Infrastructure and Infrastructure-to-Vehicle communications able to safely manage vehicles crossing through an intersection. By using the streetlamps, street lights and traffic signaling to broadcast information, the connected vehicles interact with one another and with the infrastructure. Using joint transmission, mobile optical receivers collect data, calculate their location for positioning and, concomitantly, read the transmitted data from each transmitter. As receivers and decoders, optical sensors with light filtering properties, are used. Bidirectional communication between the infrastructure and the vehicles is tested. To command the passage of vehicles safely queue/request/response mechanisms and temporal/space relative pose concepts are used. The results show that the innovative solutions for congested intersections are related to the introduction of split intersections. The results indicate that the V-VLC system increases safety by directly monitoring critical points such as queue formation and dissipation, relative speed thresholds, as well as inter-vehicle spacing.

Monitoring the network traffic status of urban roads in real-time can provide rich and highquality basic data and allow the assessment of traffic control effects. Our work focuses on the use of Visible Light Communication (VLC) as a support for transmission of information providing guidance to drivers, as well as specific information to them. Connected vehicles communicate with one another and with the infrastructure using street lights, street lamps, and traffic signals. As a result of joint transmission, optical mobile receivers collect data, calculate their location for positioning, and, correspondingly, read transmitted data from each transmitter. As receivers and decoders, optical sensors with light filtering properties, are used. To command the passage of vehicles safely queue/request/response mechanisms and temporal/space relative pose concepts are used. The results indicate that the V-VLC system increases safety by directly monitoring critical points such as queue formation and dissipation, relative speed thresholds, as well as inter-vehicle spacing.

Urban traffic management faces growing challenges due to increasing volumes of vehicles and pedestrians, resulting in congestion, delays, and safety concerns. This study introduces an innovative traffic signal control framework that integrates Multi-Agent Reinforcement Learning (MARL) with Visible Light Communication (VLC) and a Self-Adaptive Phase Adjustment (SAPA) module to enhance coordination across urban intersections. In the proposed architecture, each intersection is governed by an independent Deep Reinforcement Learning (DRL) agent capable of making real-time, decentralized decisions for both vehicular and pedestrian flows. Cooperative behavior emerges through the MARL framework, allowing agents to account for the dynamic states of neighboring intersections and improve network-wide efficiency. VLC provides high-resolution, low-latency data exchange between vehicles, pedestrians, and infrastructure, enabling precise sensing of position, speed, queue length, and stop duration. The introduction of the SAPA module further enhances adaptability by dynamically adjusting phase durations based on real-time queue/request/response patterns, resolving conflicts and prioritizing urgent demands. Extensive simulations and field tests demonstrate that the MARL–VLC–SAPA system significantly outperforms centralized and conventional agent-based approaches, reducing waiting and travel times while improving overall safety and responsiveness in complex urban environments.

Modern airports are complex Cyber-Physical Systems (CPS), where effective coordination between physical entities – like pedestrians and Autonomous Guided Vehicles (AGVs) – and computational components is essential for safety and efficiency. This study introduces a novel CPS architecture that integrates Artificial Intelligence (AI) and Visible Light Communication (VLC) to optimize mobility and enhance real-time responsiveness. Using tetrachromatic LED luminaires modulated via On-Off Keying (OOK) and amorphous SiC optical receivers in a mesh-based hybrid topology, the system creates a VLC infrastructure that delivers real-time, location-aware navigation. A custom protocol ensures low-latency, reliable data exchange between agents and the digital core. VLC receivers capture continuous data on agent positions and movements, which is processed by Deep Reinforcement Learning (DRL) agents trained via Q-learning. These agents adaptively manage traffic flow, minimize congestion, and improve throughput. Simulations and experiments confirm the system’s advantages over traditional methods, enabling GPS-independent indoor navigation, efficient mixed traffic coordination, and scalable deployment within smart airport environments.

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to optimize traffic signal control, reduce congestion, and enhance safety. Utilizing existing road infrastructure, VLC technology transmits real-time data on vehicle and pedestrian positions, speeds, and queues. AI agents, powered by Deep Reinforcement Learning (DRL), process these data to manage traffic flows dynamically, applying anti-bottlenecking and rerouting techniques. A global agent coordinates local agents, enabling indirect communication and a unified DRL model that adjusts traffic light phases in real time using a queue/request/response system. A key focus of this work is the design of reward functions for standard and rerouting scenarios. In standard scenarios, the reward function prioritizes wide green bands for vehicles while penalizing pedestrian rule violations, balancing efficiency and safety. In rerouting scenarios, it dynamically prevents queuing spillovers at neighboring intersections, mitigating cascading congestion and ensuring safe, timely pedestrian crossings. Simulation experiments in the SUMO urban mobility simulator and real-world trials validate the system across diverse intersection types, including four-way crossings, T-intersections, and roundabouts. Results show significant reductions in vehicle and pedestrian waiting times, particularly in rerouting scenarios, demonstrating the system’s scalability and adaptability. By integrating VLC technology and AI-driven adaptive control, this approach achieves efficient, safe, and flexible traffic management. The proposed system addresses urban mobility challenges effectively, offering a robust solution to modern traffic demands while improving the travel experience for all road users.

Urban traffic management is increasingly challenged by rising vehicle and pedestrian flows, resulting in congestion, delays, and safety riskW. This article proposes an innovative traffic signal control framework that integrates Deep Reinforcement Learning (DRL) with Visible Light Communication (VLC) to optimize operations at intersections, which are critical bottlenecks in urban networks. A decentralized DRL agent is deployed at each intersection and trained on local traffic states, enabling real-time decision-making for both vehicular and pedestrian movements. VLC is used to support low-latency, infrastructure-to-user communication, providing accurate data on positions, speeds, queue lengths, and stop durations. The system employs a queue/request/response mechanism to adapt signal phases dynamically, resolve conflicts, and prioritize urgent flows. The proposed approach is validated through simulations and real-world trials, demonstrating superior performance over centralized and traditional agent-based methods by substantially reducing waiting and travel times while enhancing safety. The solution is scalable and adaptable to a wide range of intersection configurations, with SUMO-based experiments confirming its potential for more efficient and intelligent urban traffic control.

The main goal of this paper is a Visible Light Communication (VLC) based guidance system to be used by mobile users inside large buildings. This system is composed of several transmitters (ceiling luminaries), which transmit map information and path guidance messages. Mobile devices, with VLC support, decode the information. A mesh cellular hybrid structure is proposed. The luminaires, via VLC, deliver their geographic position and specific information to the users, making them available for whatever use they request. The communication protocol, coding/decoding techniques, and error control are examined. Bidirectional communication is implemented and the best route to navigate through venue calculated. We propose several guidance services and multi-person cooperative localization. By analyzing the results, it became clear that the system not only provides self-location, but also the capability to determine the direction of travel and to interact with information received in order to optimize the route towards a static or dynamic destination.

Communications within personal working/living spaces are highly demanded. To support people’s wayfinding activities, we propose a Visible Light Communication (VLC) cooperative system that supports guidance services and uses an edge/fog-based architecture for wayfinding services. A mesh cellular hybrid structure is proposed. The dynamic navigation system is composed of several transmitters (ceiling luminaries), which send the map information and path messages required to wayfinding. The luminaires are equipped with one of two types of nodes: a “mesh” controller that connects with other nodes in its vicinity and can forward messages to other devices in the mesh, effectively acting like routers nodes in the network and a “mesh/cellular” hybrid controller, that is also equipped with a modem providing IP base connectivity to the central manager services. These nodes act as border-router and can be used for edge computing. Mobile optical receivers, using joint transmission, collect the data at high frame rates, extracts their location to perform positioning and, concomitantly, the transmitted data from each transmitter. Each luminaire, through VLC, reports its geographic position and specific information to the users, making it available for whatever use. Bidirectional communication is implemented and the best route to navigate through venue calculated. The results show that the system makes possible not only the self-localization, but also to infer the travel direction and to interact with information received optimizing the route towards a static or dynamic destination