This paper introduces an integrated approach to address challenges in traffic monitoring and control, alongside traffic simulation, by leveraging Visible Light Communication (VLC) technology. The proposed method optimizes traffic light signals and vehicle and pedestrians trajectories at urban intersections, incorporating Vehicle-to-Vehicle (V2V), Vehicle-to- Infrastructure (V2I), Infrastructures-to-Vehicles (I2V), and Pedestrians-to-Infrastructures (P2I) VLC communication. Experimental results demonstrate the feasibility of implementing these VLC modes in adaptive traffic control systems. Through modulated light, information exchange occurs between connected vehicles (CVs) and infrastructure elements like streetlamps and traffic light signals. Cooperative CVs share position and speed data via V2V communication within control zones, enabling adaptability to various traffic movements during signal phases. By utilizing Reinforcement Learning and the Simulation of Urban Mobility (SUMO) agent-based simulator, optimal traffic light control policies are determined. Unlike conventional methods focused solely on maximizing traffic capacity, this approach integrates traffic efficiency and safety considerations, including pedestrian concerns at intersections. Simulation scenarios adapted from real-world environments, such as Lisbon, feature interconnected intersections with traffic flow impact. A deep reinforcement learning algorithm dynamically manages traffic flows during peak hours via V2V and V/P2I communications, while prioritizing pedestrian and vehicle waiting times. VLC mechanisms facilitate queue/request/response interactions. A comparative analysis highlight the proposed approach's benefits in throughput, delay reduction, and minimizing vehicle stops, revealing improved patterns for signal and trajectory optimization. Evaluation on separate training and test sets ensures model reliability and effectiveness.
This paper presents a method for supporting wayfinding in crowded buildings using Visible Light Communication (VLC). Luminaires are repurposed to transmit encoded messages, providing location-based information to users. Tetra chromatic LEDs and OOK modulation efficiently transmit data, while error detection techniques ensure reliable transmission. Users carry receivers that interpret the light signals and perform localization calculations. Wayfinding algorithms guide users with turn-by-turn directions, landmarks, and alerts. The system integrates VLC into an edge/fog architecture, utilizing existing lighting infrastructure for efficient data processing and communication. It enables indoor navigation without GPS, demonstrating self-localization and optimizing routes. This method enhances accessibility and convenience in unfamiliar buildings
This paper introduces Visible Light Communication (VLC) as an integrated approach to improving traffic signal efficiency and vehicle trajectory management at urban intersections. By combining VLC localization services with learning-based traffic signal control, a multi-intersection traffic control system is proposed. VLC utilizes light communication between connected vehicles and infrastructure, enabling joint transmission and data collection via mobile optical receivers. Atmospheric conditions affecting communication quality are considered, with an analysis of outdoor coverage maps. The system aims to reduce waiting times for pedestrians and vehicles while enhancing overall traffic safety. Flexible and adaptive, it accommodates diverse traffic movements during multiple signal phases. Cooperative mechanisms, transmission ranges, and queue/response interactions balance traffic flow between intersections, improving road network performance. Evaluated using the SUMO urban mobility simulator, the multi-intersection scenario demonstrates reduced waiting and travel times for both vehicles and pedestrians. A reinforcement learning scheme, based on VLC queuing/response behaviors, optimally schedules traffic signals. Agents at each intersection control traffic lights using VLC-ready vehicles' communication, calculating strategies to enhance flow and communicate with each other for overall optimization. The decentralized and scalable nature of the proposed approach, particularly for multi-intersection scenarios, is discussed, showcasing its potential applicability in real-world traffic scenarios.
This paper analyses the mobility of Autonomous Guided Vehicles (AGV) in dense industrial environments. VLC technology is exploited to ensure data transmission among the infrastructures and the vehicles, providing X2X links. The VLC technology uses tetrachromatic qhite LEDs for simultaneous lighting and data transmission and dedicated pinpin photodiodes as receivers. Indoors positioning based on VLC methodology provides necessary information to enable guidance services. Coding schemes are designed for each VLC link.
The regulation of the AGVs flow along the lanes inside the warehouse is analyzed as the urban traffic flow using a SUMO urban mobility simulator. This tool is used to generate data related to the AGV movement, and a reinforcement learning scheme, combining agent-based modeling and VLC queuing/request/response behaviors, effectively schedules routes. This provides efficient travel and avoids crowded regions. The demonstration of this proof-of-concept is supported on the evaluation of travel time and traffic flows.
This study addresses the challenges and research gaps in traffic monitoring and control, as well as traffic simulation, by proposing an integrated approach that utilizes Visible Light Communication (VLC) to optimize traffic signals and vehicle trajectory at urban intersections. The feasibility of implementing Vehicle-to-Vehicle (V2V) VLC in adaptive traffic control systems is examined through experimental results. Environmental conditions and their impact on real-world implementation are discussed. The system utilizes modulated light to transmit information between connected vehicles (CVs) and infrastructure, such as street lamps and traffic signals. Cooperative CVs exchange position and speed information via V2V communication within the control zone, enabling flexibility and adaptation to different traffic movements during signal phases. A Reinforcement Learning, coupled with the Simulation of Urban Mobility (SUMO) agent-based simulator, is employed to find the best policies to control traffic lights. The simulation scenario was adapted from a real-world environment in Lisbon, and it considers the presence of roads that impact the traffic flow at two connected intersections. A deep reinforcement learning algorithm dynamically control traffic flows by minimizing bottlenecks during rush hour through V2V and Vehicle-to-Infrastructure (V2I) communications. Queue/request/response interactions are facilitated using VLC mechanisms and relative pose concepts. The system is integrated into an edge-cloud architecture, enabling daily analysis of collected information in upper layers for a fast and adaptive response to local traffic conditions. Comparative analysis reveals the benefits of the proposed approach in terms of throughput, delay, and vehicle stops, uncovering optimal patterns for signals and trajectory optimization. Separate training and test sets allow monitoring and evaluating our model.
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