Intelligent Lighting Control System Research Articles

Intelligent Traffic Light Control System

Abstract: The "Intelligent traffic light control system " project introduces an innovative approach to traffic management, aiming to expedite emergency vehicle passage. Utilizing Zigbee and Radio-Frequency Identification (RFID) technology, this system assigns a unique RFID tag to each vehicle, enabling precise tracking and control at traffic junctions. The scenarios involving emergency vehicle clearance, the system's responsiveness shines. When an emergency vehicle clearance approaches, the system rapidly receives the signal from zigbee transmitter associated with it and promptly triggers a green signal at the traffic junction. This prioritization ensures that emergency vehicles can navigate the traffic with minimal delays, potentially saving lives. Another remarkable feature of the system is stolen vehicle detection capability. In the event that a vehicle with a specific RFID tag is reported as stolen, the system is programmed to take action. It sends immediate SMS notifications to relevant authorities, such as law enforcement or the vehicle owner. This proactive response enhances the chances of recovering stolen vehicles and contributes to overall security. The hardware components of this system include Arduino Uno. Arduino Uno serves as the central control unit, processing RFID data and executing signal adjustments, while the GSM module facilitates real-time communication, enabling SMS alerts and notifications. The " Intelligent traffic light control system " project exemplifies how RFID technology and zigbee technology can revolutionize traffic management. By incorporating data analysis and automation, this system offers a multifaceted solution to common traffic-related challenges.

Multi-Agent Attention Double Actor-Critic Framework for Intelligent Traffic Light Control in Urban Scenarios With Hybrid Traffic

In real-world urban environments, hybrid and disorder traffic brings new challenges for the intelligent traffic light control system (ITLCS). Apart from coordinating traffic flows around intersections, the ITLCS is responsive to ensuring high priority vehicles pass through intersections quickly. To this end, we formulate the multiple intersections' decision-making problem as a Semi-Markov game and propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-agent attention double actor-critic (MAADAC)</i> framework to solve this game, integrating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">options framework</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph attention networks (GATs)</i> . Specifically, the options framework empowers agents to learn to make a long sequence of satisfactory decisions, such as keeping a reasonable phase for a short period to ensure high priority vehicles pass through intersections quickly. Besides, we adopt GATs to capture graph-structure mutual influences among agents. We set up a simulator based on real-world city road networks and conduct extensive experiments to evaluate the performance of MAADAC. The experimental results show that MAADAC can reduce high priority vehicles' waiting time in the interval of 18.16%-38.14% versus the density of vehicles in real-world urban scenarios over several state-of-the-art approaches. Also, our framework can guarantee the passing efficiency of high priority vehicles under various traffic conditions with the change in the proportion of high priority vehicles.

Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model

Lighting has emerged as a central concern in the domain of city planning and design in recent decades. Better lighting does more than just make cities safer and more secure; it also makes them more aesthetically pleasing and easier to live in. A single type of optical sensor is no longer sufficient to meet the needs of intelligent lighting for urban roads, and as such, there is a growing demand for cutting-edge control systems that can adapt to the dynamic lighting needs in urban environments. This paper’s goal is to create an intelligent urban lighting control system by integrating optical multisensor technology and the gray model (GM model). Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. Each road streetlight is equipped with a motion sensor detection device that is tasked with identifying the presence of vehicles and pedestrians within its field of view. Data fusion technology is utilized to process the environmental data gathered by optical multisensors, the collected data are then used to control and predict outcomes using the robust prediction capability of the GM model, and the result is a lighting control strategy that is both efficient and intelligent. In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system’s energy consumption. The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.

Design of Health Healing Lighting in a Medical Center Based on Intelligent Lighting Control System

The intelligent lighting control system will unify the management of lighting equipment in outpatient buildings, inpatient buildings, and other buildings of the hospital, thereby improving the service quality of the hospital, providing a comfortable and relaxed working environment for medical staff, and providing a warm and comfortable treatment environment for patients. This project aims to develop a health rehabilitation environment lighting automation control system based on combining Zigbee Wide Ad Hoc Network and RFID (Radio Frequency Identification) technology. It can be customized according to the comfort needs of patients and can achieve lighting adjustments before the patient arrives. A PSO-based intelligent lighting control method has been proposed. To overcome the shortcomings of PSO such as being prone to local minima and premature convergence, a new PSO optimization method is proposed based on the inertia weight PSO and combined with genetic optimization theory. This method can not only learn experience from individuals in the group but also avoid the possibility of parent particles falling into local extremum. Compared with the original particle swarm optimization algorithm, the new particle swarm optimization algorithm can quickly find the optimal combination of lighting devices, improve computational efficiency by 6.208%, and also reduce the energy consumption of the calculated lighting devices, indicating the advantages of the new particle swarm optimization algorithm.

AI-based YOLO V4 Intelligent Traffic Light Control System

With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, the effective result of wait times for the Commuters at the traffic signal point is not reduced. The proposed methodology employs Artificial Intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in lower vehicle waiting times.

Research on a fuzzy algorithm for intelligent power-saving control of urban street lamps

Urbanisation has increased the demand for energy-efficient and environmentally friendly street lighting systems. However, traditional urban lighting uses time interval control and cannot be intelligently controlled according to the environment. Therefore, an intelligent urban street lighting control system based on advanced fuzzy control technology was developed. The system uses a fuzzy control algorithm based on ZigBee technology to study the street lighting system. The fuzzy control system determines input and output variables and obtains demand instructions for intelligent control of street lights through fuzzy operation. The number of node visits and node degree were optimised to complete the construction of the street lamp intelligent control system. Experimental simulations showed that the proposed scheme could reasonably control the brightness of the street lighting according to ambient light. At 5.00 p.m., the proposed scheme adjusts the street lighting to 80 W. The system is illuminated at the rated power, and the current power consumption is 100 W. A test was carried out under a street lighting system, and the proposed scheme dynamically adjusted the lighting brightness reasonably considering various factors such as light intensity, noise intensity and vehicle speed. The proposed street lamp fuzzy control algorithm has good control performance and significantly reduces the system energy consumption. The research has important reference value for energy saving and intelligent control of urban lighting.

A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning

With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance.

The Analysis of the Artistic Innovation of LED Lighting in Gymnasiums Based on Intelligent Lighting Control Systems

With people's attention to and participation in sports, large-scale and comprehensive gymnasiums have sprung up nationwide. Unlike previous small gymnasiums, large modern gymnasiums have more robust functions, more intelligent control, and humanization. However, some shortcomings remain, such as too centralized control and inflexible control. Therefore, intelligent control and energy saving of lighting will become the development direction of lighting systems. To ensure the normal progress of sports events and the quality of TV broadcast, the requirements of gym lighting are increasingly stringent, so the lighting control system of gyms has higher requirements accordingly. To solve these problems, this paper designs a gym intelligent control lighting system based on LED bus, which can adapt to the corresponding lighting scenes of different venues. The experimental results show that the intelligent lighting control system designed in this paper can run normally and stably, and can complete the detection of crucial working parameters. This system can automatically control the light and dark and switch off according to the indoor lighting brightness, achieving a sound energy saving effect, improving the lighting environment, and achieving the desired goal.

The Design of Intelligent Building Lighting Control System Based on CNN in Embedded Microprocessor

A convolutional neural network (CNN) was designed and built on an embedded building lighting control system to determine whether the application of CNN could increase the accuracy of image recognition and reduce energy consumption. Currently, lighting control systems rely mainly on information technology, with sensors to detect people’s existence or absence in an environment. However, due to the deviation of this perception, the accuracy of image detection is not high. In order to validate the effectiveness of the new system based on CNN, an experiment was designed and operated. The importance of the research lies in the fact that high image detection would bring in less energy consumption. The result of the experiment indicated that, when comparing the actual position with the positioning position, the difference was between 0.01 to 0.20 m, indicating that the image recognition accuracy of the CNN-based embedded control system was very high. Moreover, comparing the luminous flux of the designed system with natural light and the designed system without natural light with the system without intelligent control, the energy savings is about 40%.

IMPROVED TRAFFIC CONTROL SYSTEM THROUGH DETECTION AND CLASSIFICATION OF VEHICLES USING CAMERA BASED CROWD SENSING WITH DEEP LEARNING APPROACH.

Abstract - Intelligent traffic light control system can be considered as one of the main aspects of a smart city. There is significant increase in the number of People that own vehicles and this leads to traffic congestion in urban cities of developing countries. Traffic congestion is one of the highly challenging problems people face in their daily lives which affect their working productivity, causes accident, unnecessary delays and possibly loss of lives. The control of traffics based on the densities and classes of the vehicles using deep learning approach proved to be a better approach in handling traffic jams according to the current state of the studies. However, the current state of the studies did not have special consideration for the class of special vehicles (VIPs) which need special consideration due to their importance and tight schedules. Also, it did not consider allocating time based on the classification. Thus, a model to improve the detection and classification of vehicles on side of intersection as well as special consideration for VIPs was proposed. The proposed model was built using Faster R-CNN based on Tensorflow machine learning framework on a Google Colab environment. The model was able to detect and classify vehicles as “special vehicles (i.e. VIP), Emergency (EV) and Others” classes of vehicles, a time allocation simulator was proposed based on the specified classes with special timing for VIPs. Results show that using the proposed method, 94.43% accuracy was achieved.

Sustainable tunnel lighting system based on mechanical-regulated and soft-contact hybridized nanogenerator

Tunnel lighting consumes nearly 30 % of the energy in the whole infrastructure, so optimizing the lighting control strategy could significantly increase the energy utilization efficiency. However, there is still lack of an intelligent and cost-effective lighting control system. Here, we report a low-cost, intelligent, and self-powered tunnel lighting control system based on the mechanical-regulated and soft-contact hybridized nanogenerator (MS-HNG) as both the power supply and distributed traffic monitoring sensor. Benefited from the mechanical regulation and frequency up-conversion design, the MS-HNG can effectively convert the energy into electricity from intermittent stimulation of the moving vehicle and thus offers a maximum output power of 108 mW to transmit the traffic information wirelessly. The introducing of soft fur also endows HNG with excellent stability and durability, showing a negligible decrease in the output over 100,000 cycles. Moreover, an “illumination traveling with the vehicle” sustainable tunnel lighting system which could save energy by 72 % at a traffic volume of 20 veh/h compared with the traditional one is successfully demonstrated. This work may pave the way for industrial applications of nanogenerators and shows great potential in energy-efficient and intelligent transportation systems (ITS).

Intelligent lighting control system based on Internet of things technology

With the rapid development of the economy[1], the number of cars in our country continues to increase, underground parking garages have become commercial centers, supporting facilities in large residential areas, the number of underground garages is increasing, and the energy consumption of underground garage lighting systems is also rising. This question proposes an intelligent lighting control system based on the Internet of Things technology, studies the wifi protocol, wireless mesh networking technology, the Internet of Things cloud platform and other technologies. Based on the ESP32 chip, the terminal lighting control node is designed and developed, which cooperates with the sensor to realize the detection of personnel movement and light intensity information. The terminal lighting node controls the brightness and switch of the lamps according to this information, and realizes the effect of turning on the lights when the car arrives and turning off the lights when the car leaves, so as to achieve energy-saving control purpose.

An intelligent lighting control system for individual visual comfort and energy savings in buildings

An intelligent lighting control system for individual visual comfort and energy savings in buildings

Traffic Safety Improvement via Optimizing Light Environment in Highway Tunnels

Driving in tunnel areas depends more heavily on light conditions than that on open roadways. Traditional lighting systems in highway tunnels adjust lighting parameters only caring about outside light luminance, and focus is usually on energy conservation; however, little concern is about drivers’ actual physical and psychological needs. How to leverage the enormous research progress of traffic safety, light environment, human factors engineering, and modern lighting sources to create an ideal tunnel light environment that aids with ensuring driving safety and lower interference effects caused by the change of light environment will greatly improve safety level and reduce adverse influence on drivers’ visual health in a tunnel area. An intelligent lighting control system designed with multiple influence factors are systematically considered. Based on sensor data from outside natural light conditions, target lighting parameters are determined per each lighting zone requires; then, lighting commands will be transferred and parsed by adaptive lighting controllers and modules, eventually LED lighting properties are altered step by step. This system helps a lot with optimizing tunnel lighting quality and improving drivers’ visual performance; as a result, it contributes to lower the fluctuation of drivers’ workload and get a smooth traffic flow, and ultimately this technically ensures physical and mental health of drivers in a tunnel area.

Intelligent Traffic Light Control System with Priority Lane Intervention

The current traffic light system uses a lot of fixed time signals. Fixed time signals cause traffic jams in one or several lanes due to traffic congestion. On the other hand, emergency vehicles cannot get their rights to the maximum due to the density of these vehicles. This study aims to build an intelligent traffic light with a vehicle density sensor device and a smartphone application to increase the accuracy of light timing and parse traffic density. Furthermore, in an emergency, they can still overcome and accelerate the speed of passing vehicles by manually intervening with the traffic light through a smartphone application connected wirelessly to the traffic light. The intelligent traffic light is made with Arduino Bluetooth Control (ABC), Bluetooth HC-05, Arduino Mega 2560, and infrared (IR) sensor E18-D80NK. The results show that the green light setting is based on the input from the IR sensor both near sensor (NS) and far sensor (FS) work well. When NS detects a vehicle with a green light, it increases Y seconds from the default X seconds. If FS detects a vehicle with a green light, it increases by Z seconds. Settings with the ABC application can randomly turn on the green light in four lanes according to the will of the smartphone operator.

Intelligent Light Control System Based on Zigbee.

This system uses Freescale i.MX283 chip as the main control core of the control panel and LPC824 as the main control core of the light control point. ZigBee communication local area network is built. The ZigBee network is used to search all the nodes and display them on the control interface stably. The controller can selectively control the node and communicate with ZigBee. It supports simple and convenient human-computer interaction interface. The control panel can control RGB lamps of lamp control nodes as follows: control the light on and off, adjust the brightness of the light, toggle the color of the light, control the gradient of the light, control the flashing of RGB lamp, control timing switch of RGB lamp, realize the scene effect of the light, etc. After testing, the system is stable and applied to many lighting control occasions.

Intelligent Street Light Control System Based on Fuzzy Control Technology

Aiming at the problem of low intelligence and single control mode of traditional street lights in the city, which causes serious power waste, we design an intelligent street light control system based on fuzzy control technology, use fuzzy control algorithm to control the street light system, design the light fuzzy controller and vehicle speed fuzzy controller respectively, and conduct simulation experiments by Matlab software. The experiments show that the system has a good effect of saving electric energy, and to a certain extent, it improves the intelligence of street lights and saves management and maintenance costs.

HCL Control Strategy for an Adaptive Roadway Lighting Distribution

This study aims to develop a human-centric, intelligent lighting control system using adaptive LED lights in roadway lighting, integrated with an imaging luminance meter that uses an IoT sensor driver to detect the brightness of road surfaces. AI image data are collected for luminance and vehicle conditions analyses to adjust the output of the photometric curve. Type-A lenses are designed for R3 dry roads, while Type-B lenses are designed for W1 wet roads, to solve hazards caused by slippery roads, for optimizing safety and for visual clarity for road users. Data are collected for establishing formulae to optimize road lighting. First, the research uses zonal flux analysis to design secondary optical components of LED roadway lighting. Based on the distribution of LED lights and the target photometric curve, the freeform surface calculation model and formula are established, and control points of each curved surface are calculated using an iterative method. The reflection coefficient of a roadway is used to design optical lenses that take into account the illuminance and luminance uniformity to produce photometric curves accordingly. This system monitors roadway luminance in real time, which simulates drivers’ visual experiences and uses the ZigBee protocol to transmit control commands. This optimizes the output of light according to weather and produces quality roadway lighting, providing a safer driving environment.

Research on Digital Application of Lighting Design in Public Space Based on Cloud Computing and Data Mining

Light comes along with everything in the world, which makes the world full of vigor and vitality. The appearance and development of urban landscape art lighting have added a surprise to human beings and have also increased their desire and pursuit for life. This paper describes the characteristics and functions of intelligent lighting control system in landscape public space based on cloud computing and big data, and how to use remote measurement and control technology. The system is composed of intelligent sensing layer, network transport layer, and big data processing application layer, which can collect the location and running status information of urban lighting equipment in real time. The application mode, management mode, and service mode of traditional lighting were changed through a remote platform, and the sustainable development mode of energy saving and environmental protection in the future was realized. The importance of humanized lighting design in landscape public space and the scientificalness of humanized lighting design in landscape public space lighting design are further clarified. This paper hopes to have certain reference value and guiding significance in landscape public space lighting design.

Simulation and Research of Urban Intelligent Traffic Light Control System

Intelligent traffic light control system belongs to the field of transportation public safety control. So far, the traffic light control system on the market cannot change the working time of the traffic lights according to the actual traffic flow, which is not conducive to the easing of the traffic flow and easy to cause traffic jams. Therefore, it is necessary to solve the problem of the existing traffic light control system which cannot adjust the working hours of the traffic light according to the actual road conditions, so as to alleviate the traffic jam at the intersection to a certain extent. In this paper, the design of intelligent traffic light control system uses AT89C51 MCU as the core device. The system controls the traffic of different times and different conditions, and it can alleviate traffic jam to a certain extent. This paper analyzes the hardware and software designes of intelligent traffic light control system, and simulates traffic light control by simulation software. The system of this paper is simple in structure, economical and practical, reliable in operation and it can effectively dredge the traffic.