Light Control Algorithm Research Articles

Dynamic Traffic Light Algorithm Incorporating Post-Intersection Space Allocation

This research presents a novel dynamic traffic light algorithm designed to optimize traffic flow and reduce traffic congestion by dynamically allocating green time based on post-intersection space availability. The algorithm employs a three-stage process: input generation, processing, and output. The input stage involves capturing traffic images using cameras strategically placed at intersections, which are then processed using background subtraction, edge detection, and object counting techniques. The processing phase includes vehicle counting using the YOLOv8 algorithm and open space calculation based on the maximum capacity of each road section. The output phase involves dynamically allocating green time to roads based on available post-intersection space and occupancy rates. The algorithm is designed to adapt to changing traffic conditions by continuously monitoring the post- intersection space and adjusting green times accordingly. It also incorporates a reset timer to ensure the algorithm loops back to the initial stage of gathering and processing traffic images. Simulation experiments using a physical model with toy vehicles and a camera setup demonstrated the benefits of this approach. Compared to the density-based approaches[1], this algorithm reduced average vehicle delay by 20-30%, increased overall intersection throughput by 15-25%, and decreased maximum queue lengths in each lane by 25-35%. It also adapted more effectively to fluctuations in traffic conditions, improving performance metrics by 20-30%. These results highlight the potential of incorporating downstream space considerations into traffic light control algorithms to enhance intersection efficiency, reduce traffic congestion, and enable more adaptive and fair traffic management.

A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas.

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing.

Direct Illuminance-Contribution-Based Lighting Control for IoT-Based Lighting Systems in Smart Buildings

With the advent of low-voltage light-emitting diodes (LEDs) and advances in Internet of Things (IoT) technologies, smart buildings have recently become more energy efficient. Nevertheless, the lighting-control system is one of the major sources of electrical energy consumption in commercial buildings. This study proposes a direct illuminance-contribution-based lighting-control framework to reduce the energy of LED luminaires and ensure illuminance for user requirements in smart buildings. Specifically, we designed a direct illuminance-contribution-based lighting-control algorithm (DIC-LCA) using luminaires that are ideally axisymmetric with all light emitted below the horizontal plane and developed a WiFi lighting controller for the IoT-based lighting-control systems in smart buildings. The DIC-LCA can adjust the dimming level by calculating the illuminance based on the line of sight (LOS) distance for energy saving and user satisfaction. After simulation analysis, we prove that energy savings can be achieved by controlling the dimming levels of LED luminaires with high light contribution.

A Survey on Challenges and Solutions of Traffic Surveillance Systems in IoT-Enabled Eco System

A Survey on Challenges and Solutions of Traffic Surveillance Systems in IoT-Enabled Eco System

Traffic light control with reinforcement learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

<p>In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.</p> <p> </p>

Biochemical and Preclinical Evaluation with Synthesis and Docking Study of Pyridopyrimidines and Selenium Nanoparticle Drugs for Cancer Targeting

Abstract: The coding method of spatial light modulator is the core key of spatial light field modulation technology, and the needs of the modulation algorithm are different under the specified mode and application requirements. This paper first reviews the progress made in recent years in light field control algorithms for digital micromirror devices (DMDs) and liquid crystal spatial light modulators (LC-SLM). Based on existing algorithms, the impact of optimization methods is analyzed. Then, the application areas of the different algorithms are summarized, and the characteristics of the control algorithms are analyzed. In addition, this review highlights innovative breakthroughs achieved by using various coding schemes and spatial light modulators (SLM) to manipulate the light field. Finally, critical future challenges facing emerging control algorithm technologies are outlined, while prospects for developing SLM control algorithms are proposed.

Light Field Modulation Algorithms for Spatial Light Modulators: A Review

Abstract: The coding method of spatial light modulator is the core key of spatial light field modulation technology, and the needs of the modulation algorithm are different under the specified mode and application requirements. This paper first reviews the progress made in recent years in light field control algorithms for digital micromirror devices (DMDs) and liquid crystal spatial light modulators (LC-SLM). Based on existing algorithms, the impact of optimization methods is analyzed. Then, the application areas of the different algorithms are summarized, and the characteristics of the control algorithms are analyzed. In addition, this review highlights innovative breakthroughs achieved by using various coding schemes and spatial light modulators (SLM) to manipulate the light field. Finally, critical future challenges facing emerging control algorithm technologies are outlined, while prospects for developing SLM control algorithms are proposed.

Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish.

This study aims to present a novel flocking algorithm for robotic fish that will aid the study of fish in their natural environment. The algorithm, fish-inspired robotic algorithm (FIRA), amalgamates the standard flocking behaviors of attraction, alignment, and repulsion, together with predator avoidance, foraging, general obstacle avoidance, and wandering. The novelty of the FIRA algorithm is the combination of predictive elements to counteract processing delays from sensors and the addition of memory. Furthermore, FIRA is specifically designed to work with an indirect communication method that leads to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. By leveraging a high-latency, non-guaranteed communication methodology inspired by stigmergy methods inherent in nature, FIRA successfully addresses some of the obstacles associated with underwater communication. This breakthrough enables the realization of inexpensive, multi-agent swarms while concurrently harnessing the advantages of tetherless communication. FIRA provides a computational light control algorithm for further research with low-cost, low-computing agents. Eventually, FIRA will be used to assimilate robots into a school of biological fish, to study or influence the school. This study endeavors to demonstrate the effectiveness of FIRA by simulating it using a digital twin of a bio-inspired robotic fish. The simulation incorporates the robot's motion and sensors in a realistic, real-time environment with the algorithm used to direct the movements of individual agents. The performance of FIRA was tested against other collective flocking algorithms to determine its effectiveness. From the experiments, it was determined that FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.

A new street lighting control algorithm based on forecasted traffic data for electricity consumption reduction

Buildings, urban mobility and public lighting systems are promising areas for cities to reduce energy consumption. In this work, a smart city located in a central region of Italy was investigated and a new street lighting control algorithm was developed. The method is applied to manage the public lighting system of a real case study, where efficient technologies, such as LED lamps and control systems, are already installed. Preliminary on-site measurements of traffic and lighting parameters were carried out in specific streets chosen for the case studies. The analysis was supported with lighting simulations. Based on the results, a Python-based algorithm was developed to apply switching and dimming schedules to decrease the electricity consumption of the streets. A cost/benefit study was carried out and a preliminary analysis of the environmental impact of the proposed strategy is also included. The developed algorithm could be further improved based on other boundary conditions, such as daylighting and weather data.

A traffic light control method based on multi-agent deep reinforcement learning algorithm

Intelligent traffic light control (ITLC) algorithms are very efficient for relieving traffic congestion. Recently, many decentralized multi-agent traffic light control algorithms are proposed. These researches mainly focus on improving reinforcement learning method and coordination method. But, as all the agents need to communicate while coordinating with each other, the communication details should be improved as well. To guarantee communication effectiveness, two aspect should be considered. Firstly, a traffic condition description method need to be designed. By using this method, traffic condition can be described simply and clearly. Secondly, synchronization should be considered. As different intersections have different cycle lengths and message sending event happens at the end of each traffic signal cycle, every agent will receive messages of other agents at different time. So it is hard for an agent to decide which message is the latest one and the most valuable. Apart from communication details, reinforcement learning algorithm used for traffic signal timing should also be improved. In the traditional reinforcement learning based ITLC algorithms, either queue length of congested cars or waiting time of these cars is considered while calculating reward value. But, both of them are very important. So a new reward calculation method is needed. To solve all these problems, in this paper, a new ITLC algorithm is proposed. To improve communication efficiency, this algorithm adopts a new message sending and processing method. Besides, to measure traffic congestion in a more reasonable way, a new reward calculation method is proposed and used. This method takes both waiting time and queue length into consideration.

Multi-agent deep reinforcement learning with actor-attention-critic for traffic light control

In recent years, with the increase of urbanization and car ownership, urban traffic congestion have become increasingly prominent. Traffic light control can effectively reduce urban traffic congestion. In the research of controlling traffic lights of multiple intersections, most methods introduced theories related to deep reinforcement learning, but few methods considered the information interaction between intersections or the way of information interaction is unreasonable. Inspired by this, this paper proposes a multi-agent deep reinforcement learning with actor-attention-critic network for traffic light control (MAAC-TLC) algorithm. In MAAC-TLC, each agent introduces the attention mechanism in the process of learning, so that it will not pay attention to all the information of other agents indiscriminately, but only focus on the important information of the agents that plays an important role in it, so as to ensure that all intersections can learn the optimal policy. Finally, the traffic lights at each intersection in the MAAC-TLC algorithm are controlled according to its own policy, thereby improving the traffic efficiency of the traffic network. The experimental results on light and heavy traffic flow scenarios have demonstrated that MAAC-TLC can improve traffic congestion at multiple intersections effectively.

Smart lighting control system based on fusion of monocular depth estimation and multi-object detection

Smart lighting systems can reduce energy consumption by controlling lights according to the distribution of personnel. Most of the current smart lighting systems using vision-based indoor personnel positioning method suffer from difficulties in calibration and deployment. A smart lighting control system based on fusion of monocular depth estimation and multi-object detection is proposed in this paper. The system automatically recognizes the relative positions of people and lights from the video and controls the lights, thereby reducing the complexity of calibration and deployment. In order detect the relative positions, a multi-object relative position detection algorithm (Deep-YOLO) is proposed. Deep-YOLO fuses and optimizes Scaled-YOLOv4 and AdaBins, and it detects the relative positions of people and lights by performing simultaneous multi-object detection and depth estimation on videos. A dedicated image dataset (PL) and deep transfer learning are utilized to improve the object detection accuracy of Deep-YOLO. In order to control lights according to the relative positions, a K-Means-based lighting control algorithm is proposed, and a smart lighting control system verification platform is developed. The experimental results show that the mAP of Deep-YOLO is 98.16% and the control average error of the system is less than 1 m.

Green Building Design Based on 5G Network and Internet of Things System

An information-based and intelligent comprehensive energy consumption management system should not only collect, manage, and analyze energy consumption data but also monitor the environmental data inside the building, to manage and display the real energy consumption and internal environmental quality more effectively and reliably. In order to solve this problem, the design and construction of the front-end hardware of the energy consumption management system are proposed, mainly including the design of data acquisition node and data centralization end. The design of data acquisition node includes the selection of DSP controller, wireless communication module, and data acquisition module and the design of main circuit and peripheral circuits. The design of data centralization end includes the selection of its high-performance processor and the transplantation of Linux operating system. This paper only introduces the design and research of lighting control algorithm and applies fuzzy control algorithm to intelligent lighting. The fuzzy control system is introduced, and a fuzzy controller based on intelligent lighting is created. The fuzzy control rule table and fuzzy control query table of the intelligent lighting system are obtained through the fuzzy control algorithm. According to the basic rules of light intensity control, when the indoor light intensity is greater than the set value of light intensity, the LED lamps are turned off, so when e < 0 , the LED lamp is off and the PWM output value is 0. Therefore, when designing the fuzzy controller of intelligent lighting system, the basic domain of illumination intensity error E is 0500 , excluding the case that the indoor illumination intensity is greater than the set value of illumination intensity.

A greenhouse daily light integral control algorithm that takes advantage of day ahead market electricity pricing

A greenhouse daily light integral control algorithm that takes advantage of day ahead market electricity pricing

Analytically Guided Reinforcement Learning for Green It and Fluent Traffic

This study investigates various methods for autonomous traffic signal control. We look into different types of control methods, including fixed time, adaptive, analytic, and reinforcement learning approaches. Machine learning approaches are compared with the “analytic” approach, which is used as “gold standard” for performance assessment. We find that conventional machine learning approaches are better than the analytic approach, but require a lot more computer power. We, therefore, introduce a novel hybrid method called “analytically guided reinforcement learning” or shorter “α-RL”. This approach is implemented in our “GuidedLight agent” and tends to outperform both, classical machine learning and the analytic approach, while largely improving convergence. This method is therefore suited as a “green IT” solution that improves environmental impact in a two-fold way: by reducing (i) traffic congestion and (ii) the processing power needed for the learning and operation of the traffic light control algorithm.

Development of New Radar and Pyroelectric Sensors for Road Safety Increase in Cloud-Based Multi-Agent Control Application

The paper concentrates on the design, architecture, and monitoring of smart LED street lighting control, with focus on traffic safety and safe road infrastructure. The use of a CMAS (Cloud-Based Multi-Agent System) as a possible framework is investigated. The work is based on previous developments by the authors in the production and design of close and long-range hybrid Pyroelectric Infrared (PIR) motion detection sensors. It also introduces the advances in radar-type sensors used in smart SLC (street lighting control) application systems. The proposed sensor solutions can detect the road user (vehicle or pedestrian) and determine its movement direction and approximate speed that can be used for dynamic lighting control algorithms, traffic intensity prediction, and increased safety for both driver and pedestrian traffic. Furthermore, the street lighting system infrastructure can monitor city environmental parameters, such as temperature, humidity, CO2 levels, thus increasing levels of safety and security for smart cities. Utilising other hybrid systems within intelligent street lighting applications represents a new specialisation area in both energy-saving, safety awareness, and intelligent management.

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.

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 Green Control Algorithm of Street Lamp

As an important facility of community lighting, street lights can not only provide convenience for pedestrians or vehicles, but also improve the safety of the community. The power consumption of street lamp lighting is one of the important parts of shared power in the community. In order to solve this problem, firstly, we study the lighting control algorithm and establish the energy consumption formula; secondly, we analyse the energy consumption under different lighting control strategies; finally, we design a feasible intelligent green control algorithm. Theoretical analysis shows that intelligent green light control can effectively save energy and prolong the life of lamps and lanterns.