Intelligent Lighting Research Articles

Cooperative link scheduling for RSU-assisted dissemination of basic safety messages

The reliable and real-time dissemination of Basic Safety Message (BSM) among smart vehicles has attracted much attention in both academia and industry, where a group of adjacent vehicles form a Vehicular Ad-hoc NETwork (VANET) to exchange their local-observed traffic information for driving awareness and road safety. However, buildings and trucks may degrade the quality of wireless links between vehicles and cause frequent packet losses in BSM dissemination. To address this issue, road side units (RSU) such as intelligent traffic light could be involved to cooperate with nearby vehicles for BSM forwarding, aiming to help other vehicles recover their lost packets. Based on this idea, many schemes have been proposed but few studies the efficiency of BSM dissemination. For instance, they always assume the scheduling of broacast links are fixed, which may fail to coincide with the sequence of message forwarding, yielding a long time delay and even data expiration when recovering lost BSMs. In this paper, we propose a decentralized Forwarding-Aware Cooperative Scheduling (FACS) scheme for reliable and real-time BSM dissemination, where broadcast links are delicatedly scheduled to coincide with the local resource of neaby vehicles and RSUs, e.g., receiving status of BSM, the allocated task in BSM recovery. Thereby, the time cost in disseminating BSMs could be cut down and other vehicles have more chances to further forward BSMs for reliable and real-time dissemination. We evaluate FACS with real-world Wi-Fi traces in a VANET with/without RSU, respectively. Simulation results show that 84.12% wireless links are scheduld averagely, which yields a message reception ratio of 96.33% (with RSU) and 91.56% (without RSU) and remains stable even when vehicle density increases to 0.35 vehicle/lane/meter. The scheduling of wireless links also helps to cut down the time delay from 100 to 46.12 milliseconds without triggering extra data colloisions (around 3.7%), and the task of message diessmination is offloaded to vehicles and RSUs evenly with a high network balnace of 0.96.

Realization of IoT Integration System of LED Based on Particle Swarm Optimization

The purpose of this research is to design an intelligent LED lighting control system with wireless remote control. Since the designed system is mainly based on Android applications, it controls wireless lighting modules through a sever by means of a smartphone, tablet computer, or another handheld mobile device, and the values displayed on the handheld device are used to monitor the establishment and design of the electricity state and multiple-LED light control scenarios in real time. The server is connected with a touch human-machine interface, digital meter, wireless lighting controller, infrared learning module, and other devices through RS-232 and RS-485 serial communication ports, and the controller and receiver of the wireless lighting module use ZigBee as a communication protocol. The touch human-machine interface can read the measured value from the server and receive user input commands to control the wireless light control module and infrared learning module. Through the Wi-Fi, the user can operate the proposed system by using a handheld mobile device. This paper also analyzes the measured data using the particle swarm optimization (PSO) algorithm, analyzing the electricity consumption based on the concepts of PSO speed and location updating and judging how to save electricity when using a light source in order to establish an intelligent LED lighting control system, facilitate mastering of electricity consumption, and realize control of other related electronic equipment during the operation process. The proposed intelligent LED lighting control system can be used in integrating applications of digital life and green energy saving.

Improvement of Multi-agent Routing Guidance with an Intelligent Traffic Light Scheduling and the Ability to Select Intermediate Destinations

Traffic congestion and route guidance are integral parts of urban development in large cities. How cars are routed and the traffic flowing have a direct impact on each other. Therefore, the first step is to determine a criterion for assessing the traffic situation. The type of vehicles should also be considered in routing. Emergency vehicles must arrive at their mission site as soon as possible. Public transportations must also travel according to their plans. Ordinary vehicle drivers can either choose a road as an intermediate destination (out of interest, pick up someone, etc.). In this paper, two new algorithms are proposed to 1) route with intermediate destination selection for ordinary vehicles, and 2) schedule traffic lights to decrease traffic density and routing delay. The first algorithm proposed an agent-based route guidance model that in addition to finding the least expected travel time (LET) routes, drivers could select a part of the route as intermediate destinations according to their interests, to raise their satisfaction level. The second algorithm considers the density of traffic flow and the presence of emergency vehicles. This algorithm evaluates the status of the traffic flow by fuzzy logic. The evaluation is conducted by considering traffic flow speed and density. The output of fuzzy logic is used by the Gradational Search Algorithm (GSA). GSA regards the status of the flow, the priority of the traffic flow, and the distance of the emergency vehicles to the traffic light. The simulation results prove that the proposed algorithms have better performances.

Intelligent Traffic Light Control System Based On Traffic Environment Using Deep Learning

At present, the traffic control frameworks in India, need insight and go about as an open-loop control framework, with no input or detecting system. Present technologies use Inductive loops and sensors to detect the number of vehicles passing by. It is a very inefficient and expensive way to make traffic lights adaptive. Using a simple CCTV camera can improve the conditions. The visual tracking of objects is amongst the most critical areas of computer vision and deep learning. The objective of this work was to develop the traffic control framework by presenting a detecting system, which gives an input to the current system, with the goal that it can adjust the changing traffic density patterns and provides a vital sign to the controller in a continuous activity. Using this method, improvement of the traffic signal switching expands the street limit, saves time for voyaging, and prevents traffic congestion. The framework additionally goes for consolidating exceptional arrangements for clearing the path for emergency vehicles. In this paper, we will detect and track vehicles on a video stream and count those going through a defined line and to ultimately give an idea of what the real-time on-street situation is across the road network. Our real target is to advance the deferral in the travel of vehicles in odd hours of the day. It uses the YOLO (“You Only Look Once”) object detection technique to detect objects on each of the video frames And SORT (Simple Online and Real-time Tracking algorithm) to track those objects over different frames. Once the objects are detected and tracked over different frames, a simple mathematical calculation is applied to count the intersections between the vehicle’s previous and current frame positions with a defined line.However, accuracy drops when vehicles are either close together or have large shadows, dark vehicles do not always meet the detection criteria, and night scenes are challenging to resolve as headlight beams can create large areas that meet threshold criteria. We are focusing on the Indian roads where the traffic conditions are harsh and abrupt. We have already tested it on a live feed from various traffic prone roads and have found satisfactory results. Other Adaptive Traffic Light Systems were not able to work on the Indian traffic conditions. Our model has the edge over the other by performing at par on Indian roads. We’ll increase or decrease the timer according to the conditions of the roads. This will tremendously improve the traffic conditions at a very low cost. Inductive loops are a feasible but expensive method. So, this system reduces costs and provides quick results.

Экономия топлива и снижение выбросов вредных веществ в атмосферу путем регулирования работы светофоров

Traffic control and coordination is an important feature on today's busy roads. Typical traffic lights switch from a fixed time to those based on a variety of sensors. Some weaknesses were identified when considering these various approaches. In this article, we offer intelligent dynamic traffic lights that can adapt their signaling time (for example, changes from green to red and vice versa) according to traffic density, using information about the arrival direction and leading time transmitted from mobile phones transmitted by car drivers, smart antennas installed on cellular base stations. Analysis of the simulation shows that the proposed system can reduce the queue delay by 22%. Therefore, this approach will reduce fuel consumption and environmental pollution by 22%, avoiding the queue at the traffic lights. On this architecture, we define and evaluate an adaptive light control algorithm using simulation. Based on two main objectives, this algorithm dynamically determines the sequence of green lights by selecting the movements that make up each phase and its duration. The simulation results show that this algorithm, if properly configured, is able to reduce the average waiting time at the intersection. The development of modern digital technologies, information exchange and the use of intelligent control systems in the automotive industry has led to a significant change in scientific, technological and practical approaches to the organization of optimal road traffic in densely populated cities.

Knowledge transfer and engineering methods for smart-circular product service systems

Smart Product Service Systems (Smart PSS) have the potential to contribute to a Circular Economy (CE), but in the design of these systems engineering teams often lack information and knowledge on CE strategies and processes. Against this background, the authors propose a knowledge transfer system for the design of Smart-circular PSS. The system can be used in the concept phase of the Smart PSS development process and delivers information on CE strategies via a Smart-circular PSS Lifecycle Flowchart (SCPLF) and service archetypes. These strategies can be selected, supplemented and extended according to individual user requirements. The approach was validated using an intelligent street lighting system. This paper summarizes findings from the application and provides insights into potentials and limitations for the design of Smart-circular Smart PSS.

Design of Traffic Light Based on Field Programmable Gate Array

The use of fixed-time traffic lights for road traffic control has the disadvantage of low traffic efficiency. In order to optimize the vehicle traffic at the intersection, this paper proposes a design scheme of a real-time control system for road intelligent traffic lights based on FPGA. The system adopts the polling control model, the vehicle detector detects the arrival rate of vehicles, and obtains the corresponding traffic light green time length according to the traffic rules and polling model theory. Using Altera’s Cyclone IV series EP4CE15E22C8 chip as the development platform, a specific design plan is given. The circuit mainly includes program-controlled amplifier module, AD acquisition module, cross-correlation calculation module, serial port transmission and Lab-VIEW module. The system can realize the intelligent adjustment of traffic lights. Different vehicle arrival rates are detected at different times, so that the corresponding traffic light configuration time length changes accordingly. This intelligent adjustment controls road traffic and makes the main and branch roads coordinate and cooperate, thereby improving the traffic efficiency of the intersection.

Use of Receiver Operating Characteristic Curve to Evaluate a Street Lighting Control System

Intelligent control of public lighting is nowadays one of the most challenging issues in smart city deployment. Lighting optimization entails a compromise between comfort, safety, and power consumption, affecting both vehicles and pedestrians. Smart solutions must estimate their characteristics to trade-off users’ needs and energy requirements. This paper proposes an intelligent street lighting control system and the Receiver Operating Characteristic (ROC) curve method to evaluate the best number of street lamps to achieve a balance between public road user comfort and system power consumption. The control system is based on the detection of users, mainly pedestrians, using presence sensors. From the detection of a pedestrian by two or more consecutive street lamps it is possible to determine their speed. Knowing the pedestrian speed, allows the system to anticipate and adjust the light intensity of the remaining street lamps, and provide a comfortable view of the street. Using the ROC curve, we evaluate the control algorithm in terms of the number of previous street lamps used. We have tested the system and the method in a model of pedestrians walking down a street. The obtained results show that ROC analysis used to control street lighting allows measuring the whole control system’s efficiency by providing a concrete number of previous street lamps.

A Bidirectional Buck-Boost Converter-Based Switching Ripple Communication Strategy for Intelligent Street Lighting Systems

Abstract The light-emitting diode (LED) is an essential component of intelligent street lighting (ISL) systems. An efficient ISL system can not only reduce power consumption by planning LED illuminating time but also reduce maintenance costs through a high degree of automation. In this paper, a buck-boost converter is used to realise composite transmission of power and signals for an ISL system. The power is modulated by the pulse width modulation (PWM) approach, and the switching ripple generated in the PWM process is utilised as the carrier of the signals transmitted between the remote-control centre and the slave nodes. Moreover, the proposed model involves a ‘request to send (RTS)/confirm to send (CTS)’ mechanism to avoid signal conflicts. Compared with the conventional power line communication (PLC) approach, the proposed transmission scheme has the advantages of simple circuit structure and simple system wiring. Additionally, a simulation model built in MATLAB/Simulink proves the designed transmission method has strong anti-noise ability.

Development of a Face Recognition System and Its Intelligent Lighting Compensation Method for Dark-Field Application

A face recognition system that uses 3-D lighting estimation and optimal lighting compensation for dark-field application is proposed. To develop the proposed system, which can realize people identification in a near scene dark-field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used. First, 68 facial landmarks are detected, and their coordinates in both image and camera coordinate systems are computed. Second, a 3-D morphable model (3DMM) is developed after considering facial shadows, and a transformation matrix between the 3DMM and camera coordinate systems is estimated. Third, to assess lighting uniformity, 30 evaluation points are selected from the face. Sequencing computations of LED radiation intensity, ray reflection luminance, camera response, and face lighting uniformity are then carried out. Ray occlusion is processed using a simplified 3-D face model. Fourth, an optimal lighting compensation is realized: the overhead light is used for flood lighting, and the wall lights are employed as meticulous lighting. A genetic algorithm then is used to identify the optimal lighting of the wall lights. Finally, an Eigenface method is used for face recognition. The results show that our system and method can improve face recognition accuracy by >10% compared to traditional recognition methods.

Research on Smart Street Lamp Planning Based on Information Utility

Compared with traditional street lamps, LED smart street lamps have strong functions and high cost. In the reconstruction of existing urban road lighting, it is not necessary to realize intelligent reconstruction of all street lamps. Therefore, how to realize the optimal layout of LED smart street lamps will be particularly important. In this paper, by analyzing the influence of traffic incident rate, traffic flow rate, information availability and information acquisition rate on road lighting, a relatively general model of urban intelligent lighting with the maximum information utility as the objective function under the same construction cost is constructed.

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.

A lighting control system of art museum based on image recognition and STM32

Intelligent lighting control system plays a vital role in the lighting regulation of art gallery. It is necessary to design an automatic control system of light regulation. This paper proposes a la...

Automatic System of Illumination of the Territory of the Enterprise

The work is devoted to the development of an intelligent lighting control system for adjacent territories.Every day more and more popular is the use of led lamps, they are more economical from outdated gas-discharge lamps or incandescent lamps, which are now commonly used. For comparison, a 40W incandescent lamp has a luminous flux of 400 lumens, the same luminous flux has a 5W led lamp. There are many outdoor lighting network management systems that have a wide range of management capabilities. The most common ones are LonWorks, DMX-512, DALI, DCI. They usually use motion sensors, light sensors, time relays, and other automation elements, each of which has its own advantages and disadvantages. These advantages and disadvantages have been analyzed in terms of application in the system being developed. The main requirements for the system were economic and ease of use. Taking into account these requirements, the existing methods of street lighting management and systems based on them were considered.A block diagram of the lighting control system for the adjacent territories of the enterprise was developed, which is shown in figure Fig. 1. The system is a network whose nodes are connected by a simple digital data bus DB. The digital data bus can be wired, wireless, or optical. Each network node consists of: an MSi motion sensor, an LLi led lamp, a microcontroller with a built-in MCi real-time clock, a light-sensitive LSI sensor, and IUI interface blocks. The light-sensitive sensor can be installed in one place, provided that the level of natural light in the coverage area of each local lighting system is approximately the same. When the level of external natural light decreases below a certain critical level, the light-sensitive sensor sends a signal to the microcontroller to turn on the local lighting system, after which each local zone is illuminated with individual specified parameters of the glow power. When the motion sensor is activated in the local area of the lighting system, the lamp switches to high power mode. Thanks to a real-time microcontroller, the moment when an object appears in this zone is recorded. As the object moves, it enters the coverage area of another local lighting system where the network uses a built-in real-time clock to record the moment when the object appears in another zone, this information is transmitted via a digital bus to the system, from which we can determine the direction of movement of the object.Based on the developed block diagram and based on the requirements for the system being developed, a microcontroller from the STM family was selected. This family of microcontrollers, due to their performance, architecture and low power consumption, is best suited for working with the hardware of the system being developed.

Intelligent control and energy saving evaluation of highway tunnel lighting: Based on three-dimensional simulation and long short-term memory optimization algorithm

Traditional tunnel lighting luminance is calculated based on the designed hourly traffic volume and designed speed. This approach first determined the entrance section reduction factor and middle section’s luminance through the look-up table method, then calculated each section’s luminance in proportion. Since the designed value is often higher than the actual traffic flow value, and the calculated brightness value is so high which might lead to tunnel energy consumption. What’s more, the designed value in this method is the piecewise discrete value, which does not meet the requirement of continuous dimming for LED lighting. In this case, under the premise of satisfying operation safety, how to carry out continuous intelligent energy saving control research and energy saving evaluation based on actual traffic value is an urgent problem to be solved in tunnel lighting. This paper first proposed a luminance calculation model for each lighting section which can realize continuous dimming as needed. Using the least squares method fitting the reduction coefficient and the luminance of the middle section to solve the luminance of the entrance and middle sections under the condition of any speed and traffic flow, and realizing the continuous optimization of the luminance calculation. Second, in terms of the shortcomings of the traditional graded dimmer control, this paper offered a highway tunnel lighting intelligent control algorithm with traffic flow, speed and luminance out of the tunnel as the input matrix, which meets the requirement of LSTM (Long Short-Term Memory) neural networks, tunnel lighting characteristics and tunnel lighting section. Considering the objective function of optimization problem in LSTM, three gradient descent methods were introduced to optimize the model respectively. Third, this paper adopted equal proportion to arrange lamps and light distribution, and developed a tunnel lighting simulation environment based on DIALux to meet the energy conservation assessment requirements in operating tunnels. Roadway luminance and its total uniformity are selected as energy saving evaluation indexes. Finally, we established a three-dimensional tunnel model with the real environment of Jinding Lake 2# tunnel, then conducted simulations and experimental verifications of the intelligent control algorithm under sunny and cloudy weather conditions, and verified the performance of the intelligent lighting control algorithm under the three optimizers. The result of energy conservation evaluations shows that compared with traditional lighting design, the tunnel lighting energy conservation algorithm can save 23.61% of energy in sunny days and 31.40% in cloudy days.

Research on intelligent lighting distributed control algorithm based on sensor network technology

In order to flexibly and effectively complete the intelligent lighting distributed control, this paper studies the intelligent lighting distributed control algorithm based on sensor network technology. In this method, a complete sensor network was constructed by the establishment of sensor network and the successful access of nodes. The characteristics of data structure information in sensor networks were sampled to optimize the channel equalization configuration in sensor networks, thus achieving the data acquisition. Then, the adaptive weighting algorithm was utilized to fuse the collected sensing information to improve the accuracy and stability of the data, obtaining high-precision acquisition data. The mapping relationship was established to realize the distributed control of intelligent lighting after the implementation of high-precision data learning and training. The results show that the method has high precision data acquisition, and can complete the distributed control of intelligent lighting according to the changes of the target illumination area, human physiological information and environmental information.

Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network

Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.

Improved Multi-domain Convolutional Neural Networks Method for Vehicle Tracking

In the field of intelligent transportation, background complexity, lighting changes, occlusion, and scale transformation affect the tracking results of moving vehicles in the video. We propose an improved vehicle object tracking algorithm based on Multi-Domain Convolutional Neural Networks (MDNet), combining the instance segmentation method with the MDNet algorithm, adding two attention mechanisms to the algorithm. The module extracts better features, ensures that the vehicle object adapts to changes in appearance, and greatly improves tracking performance. Our improved algorithm has a tracking precision rate of 91.8% and a success rate of 67.8%. The Vehicle Tracking algorithm is evaluated on the Object Tracking Benchmark (OTB) data set. The tracking results are compared with eight mainstream object tracking algorithms, and the results show that our improved algorithm has excellent performance. The object tracking precision rate and tracking success rate of this algorithm have achieved excellent results in many cases.

A low-cost web-based smart street lighting geographical information system for smaller towns and villages

AbstractThe use of LED lights in smart street lighting to save energy is efficient and very common in many developed cities. However, such a system results in higher initial costs, which may discourage smaller towns and villages from its purchasing. This paper therefore deals with the design and development of a smart street lighting control system for smaller cities and villages. Such a system is required to be economical from the perspective of development and production. Considering other requirements such as platform independence, high availability and broad support, we proposed to build a system as a web application using a custom light control service component. The result is a web application that consists of a user interface served by a web server and an application server used to communicate with light control service. In addition, the system is designed as a geographical information system to be easy to use for managing street lights in groups or individually, while displaying them on the map. The system allows automatic sensor-based light intensity modulation by default with the possibility of manual adjustment or override of the illumination. It also includes a device error notification system with a tool to navigate faulty devices. The presented system is a low-cost solution for intelligent street lighting control designed for smaller cities or villages. They can apply the designed architecture of the system and the specific technologies suitable because of their low-cost implementation.

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights.

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on , (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.