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 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 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.
Based on Vehicle-to-Vehicle, Vehicle-to-Infrastructure and Infrastructure-to-Vehicle communications, we propose a VLC system for managing vehicles crossing a light controlled intersection in a safe manner. Connected vehicles and infrastructure interact by broadcasting information using headlights, streetlights, and traffic signals. Transmitters emit light signals encoded, modulated and converted from data. Optical sensors with light filtering properties are used as receivers and decoders. A joint transmission allows mobile optical receivers to collect data, calculate their location for positioning, and read the transmitted data at the same time. A communication scenario is stablished. Parallel to this, an intersection manager coordinates traffic flow and interacts with vehicles through embedded Driver Agents. To command the passage of vehicles crossing the intersection safely queue/request/response mechanisms and temporal/space relative pose concepts are used. A dynamic phasing diagram and a matrix of states based on the total accumulated time are presented to illustrate the concept. On a Simulation of Urban MObility simulator (SUMO), deep reinforcement learning was used to control the cycle of traffic lights. Data shows that the adaptive traffic control system in the V2X environment can collect detailed data, including vehicle position, speed, queue length, and stopping time. Dynamic control of traffic flows at intersections is demonstrated using sequence state durations, phase diagrams, and average speed measurements. For the same traffic flow, static and dynamic cycle lengths were compared. According to the results, the dynamic system finishes the cycle first by adjusting the durations of the cycles as necessary. The better temporal management of phases results in better traffic flow and a higher average speed.
Toward supporting people's wayfinding activities, we propose a VLC-based guidance system for mobile users inside large buildings. A mesh cellular hybrid structure is chosen as the architecture, and the communication protocol is defined for a multi-level building scenario. The dynamic navigation system is made up of several transmitters (ceiling luminaries) that transmit map information and path messages for wayfinding. Each luminaire includes one of two types of controller: a "mesh" controller that communicates with other devices in its vicinity, effectively acting as a router for messages to other nodes in the network, or a "mesh/cellular" hybrid controller that communicates with the central manager via IP. Edge computing can be performed by these nodes, which act as border routers. Mobile optical receivers, using joint transmission, collect the data at high frame rates, extracts theirs 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. A bidirectional communication process is carried out and the optimal path through the venue is determined. Results show that the system offers not only self-localization, but also inferred travel direction and the ability to interact with received information optimizing the route towards a static or dynamic destination. According to global results, the location of a mobile receiver is found in conjunction with data transmission. The dynamic LEDaided guidance system provides accurate route guidance, allows navigation, and keeps track of the route. Localization tasks are automatically rescheduled in crowded regions by the cooperative localization system, which provides guidance information and alerts the user to reschedule.
An integrated approach that optimizes traffic signals and vehicle trajectory at urban intersections using Visible Light Communication (VLC) is proposed. A Connected Vehicle (CV) platoon approaches a signalized intersection, and downstream CVs queue before the stop lines. Light is used to communicate information between CVs and the infrastructure using streetlamps, intersection signals, and headlights. Interaction with traffic is coordinated by an intersection manager. Integrated control is flexible and adaptive to traffic demands since different traffic movements are incorporated during multiple signal phases. As part of the simulation process, an open-source urban mobility simulator SUMO creates the desired scenarios and generates different urban traffic flows. VLC queue/request/response mechanisms and temporal/space relative pose concepts are used. Using sequence state durations, phase diagrams, and average speed measurements, the system dynamically controls traffic flows at intersections using a Deep Reinforcement Learning (DRL) algorithm, minimizing rush hour bottlenecks, through joint Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. Comparisons with trajectory optimization and signal optimization demonstrate the benefits on throughput, delay, and vehicle stops, and reveal the optimal patterns for signals and trajectory.
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 acts as borderrouter and can be used for edge computing. Mobile optical receivers, using joint transmission, collect the data at high frame rates, extracts theirs 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.
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, Vehicle-to-Infrastructure and Infrastructure-to-Vehicle communications able to safely manage vehicles crossing through an intersection leveraging Edge of Things facilities. By using the streetlamps, street lights and traffic signaling to broadcast information, the connected vehicles interact with one another and with the infrastructure. By using joint transmission, mobile optical receivers collect data at high frame rates, calculate their location for positioning and, concomitantly, read the transmitted data from each transmitter. In parallel with this, an intersection manager coordinates traffic flow and interacts with the vehicles via Driver Agents embedded in them. A communication scenario is stablished and a “mesh/cellular” hybrid network configuration proposed. Data is encoded, modulated and converted into light signals emitted by the transmitters. 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 crossing the intersection safely queue/request/response mechanisms and temporal/space relative pose concepts are used. Results show that the shortrange mesh network ensures a secure communication from street lamp controllers to the edge computer through the neighbor traffic light controller with active cellular connection and enables peer-to-peer communication, to exchange information between V-VLC ready connected cars. The innovative treatments for the congested intersections are related with the introduction of the split intersection. In the split intersection a congested two-way-two-way traffic light controlled intersection was transformed into two lighter intersections which facilitate a smoother flow with less driver delay by reducing the number of vehicle signal phases. Based on the results, the V-VLC system provides direct monitoring of critical points including queue formation and dissipation, relative speed thresholds and inter-vehicle spacing, increasing safety.
Increasing interest in indoor navigation has recently been generated by devices with wireless communication capabilities
that enabled a wide range of applications and services. The rise of the Internet of Things (IoT) and the inherent end-to-end
connectivity of billions of devices is very attractive for indoor localization and proximity detection. Other fields,
such as, marketing and customer assistance, health services, asset management and tracking, can also benefit from
indoor localization. Different techniques and wireless technologies have been proposed for indoor location, as the
traditional Global Positioning System (GPS) has a very poor, unreliable performance in a closed space. The work
presented in this research proposes the use of an indoor localization system based on Visible Light Communication
(VLC) to support the navigation and operational tasks of Autonomous Guided Vehicles (AVG) in an automated
warehouse. The research is mainly focused on the development of the navigation VLC system, transmission of control
data information and decoding techniques.
As part of the communication system, trichromatic white LEDs are used as emitters and a-SiC:H/a-Si:H based
photodiodes with selective spectral sensitivity, are used as receivers. Through the modulation of the RGB LEDs, the
downlink channel establishes an infrastructure-to-vehicle link (I2V) and provides position information to the vehicle.
The decoding strategy is based on accurate calibration of the output signal. Characterization of the transmitters and
receivers, description of the coding schemes and decoding algorithms will be the focus of discussion in this paper.
Vehicle Communication Systems consist of vehicles and roadside units that communicate with one another in order to exchange information, such as traffic information and safety warnings. Split intersections are an innovative solution for congested urban areas. In this case, a congested two-way-two-way intersection is made into two lighter intersections. It facilitates a smoother flow with less driver delay, by reducing the number of conflict points and improving the travel time. Based on Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications, we propose a Visible Light Communication system that can safely manage vehicles crossing an intersection using Edge of Things facilities. The connected vehicles communicate with each other and with the infrastructure through visible light, by using headlights, street lamps, and traffic signals, In parallel, an intersection manager coordinates the traffic flow and interacts with the vehicles through internally installed Driver Agents. Request/response mechanisms and time/space relative pose concepts are used to control the flow of vehicles safely crossing the intersection. A communication scenario is established, and a “mesh/cellular” hybrid network configuration is proposed. Data is encoded, modulated and converted into light signals emitted by the transmitters. As receivers and decoders, optical sensors with light filtering properties, are used. Bidirectional communication between the infrastructure and the vehicles is tested, using the VLC request/response concept. Results show that the short-range mesh network ensures a secure communication from street lamp controllers to the edge computer through the neighbor traffic light controller with active cellular connection and enables peer-to-peer communication, to exchange information between VVLC ready connected cars.
Vehicular Communication Systems are a type of network in which vehicles and roadside units are the communicating nodes, providing each other with information, such as safety warnings and traffic information. In this paper, a traffic controlled intersection is analyzed by using microsimulation. A Vehicle-to-Everything (V2X) communication scenario is stablished and a mesh cellular hybrid network configuration is used. The concept of request/response and relative pose estimation for the management of the trajectory is used, in a two-way-two-way traffic lights controlled crossroad, using Vehicular Visible Light Communication (V-VLC). The connected vehicles receive information from the network and interact with each other and with the infrastructure. In parallel, an intersection manager (IM) coordinates the crossroad and interacts with the vehicles (I2V) using the response distance, the pose estimation and the temporal/space relative pose concepts. The communication between the infrastructures and the vehicles (I2V), between vehicles (V2V) and from the vehicles to the infrastructures (V2I) is performed through V-VLC using the street lamps, the traffic signaling and the headlamps to broadcast the information. Data is encoded, modulated and converted into light signals emitted by the transmitters. Tetra-chromatic white sources are used providing a different data channel for each chip. As receivers and decoders, SiC Wavelength Division Multiplexer (WDM) optical sensor, with light filtering properties, are used. Cooperative localization is realized in a distributed way with the incorporation of the indirect V2V relative pose estimation method. A phasing traffic flow is developed, as Proof of Concept (PoC), to control the arrival of vehicles to the intersection and schedule them to cross at times that minimize delays, A generic model of cooperative transmission based on the graphical representation of indirect relative poses estimation (simultaneous localization and mapping) is analysed. The block diagram expresses that the vehicle’s behavior (successive poses) is mainly influenced by the manoeuvre permission and presence of other vehicles. Results show that the cooperative I2V and V2V messages and the intersection redesigned layout are important issues on traffic control with least dependency on infrastructure.
Nowadays, Global Positioning Systems (GPS) are used everywhere for positioning and navigation. However, its use is not suitable in indoor environment, due to power budget constraints and the strong attenuation inside buildings. Therefore, indoors navigation takes advantage of other technologies to infer position. Recently, several Visible Light Positioning (VLP) systems have been reported. Among these technologies, Visible Light Communication (VLC) is one of the most promising, as its operation is based on the use of LED lights, currently widely used in the illumination solutions of most buildings. In this paper, we propose an indoor navigation system based on VLC in an industrial application for automated warehouses, where the navigation of autonomous vehicles (AVG) is supported by VLC. The proposed VLC system establishes bidirectional communication between the infrastructure and the guided vehicles. LED transmitters at the warehouse ceiling support downlink data transmission from the Infrastructure to Vehicle (I2V). This channel provides positioning and navigation of the vehicles, as well as transmission of dedicated messages related to the requested tasks of the management warehouse system to the autonomous vehicles. The uplink channel from the Vehicle to the Infrastructure (V2I) is used to acknowledge the requested tasks and transmit updates on the concluded tasks. Optical transmitters are tri-chromatic white LEDs with a wide angle beam. The characterization of the optical transmitter system is done through MatLab simulations for path loss and VLC channel gain prediction, using the Lambertian model for the LED light distribution. Dedicated receivers based on a-SiC:H/a-Si:H photodiodes with selective spectral sensitivity are used to record the transmitted signal. The decoding strategy is based on accurate calibration of the output signal.
To support people’s wayfinding activities in crowded buildings minimizing the risks of contamination this paper proposes a method able to generate landmark route and alert instructions using Visible Light Communication (VLC). The system is composed of several transmitters (ceiling luminaries) which send the map information, alerts and the path messages required to wayfinding. The system informs the users, in real time, not only of the best route to the desired destination, through a route without clusters of users, but also of crowded places. An architecture based on a mesh cellular hybrid structure was used. Data from the sender is encoded, modulated and converted into light signals emitted by the transmitters. Tetra-chromatic white sources are used providing a different data channel for each chip. The modulated light signal, containing the ID and the 3D geographical position of the transmitter and wayfinding information, is received by a SiC optical sensor with light filtering and demultiplexing properties. Each luminaire for downlink transmission is equipped with one two type of controllers: mesh controller and cellular controllers do forward messages to other devices in the vicinity or to the central manager services. The light signals emitted by the LEDs are interpreted directly by the receivers of the positioned users. Bidirectional communication is tested. The effect of the location of the Access Points (APs) is evaluated and a 3D model for the cellular network is analyzed. In order to convert the floorplan to a 3D geometry, a tandem of layers in an orthogonal topology is used, and a 3D localization design, demonstrated by a prototype implementation, is presented. Uplink transmission is implemented, and the 3D best route to navigate through venue is calculated. Buddy wayfinding services are also considered. The results showed that the dynamic VLC navigation system enables to determine the position of a mobile target inside the network, to infer the travel direction along the time, to interact with received information and to optimize the route towards a static or dynamic destination, avoiding the threat of contamination in crowded regions.
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