Traffic light detection and V2I communications of an autonomous vehicle with the traffic light for an effective intersection navigation using MAVS simulation

Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the system’s effectiveness in a multi-intersection scenario

IMPLEMENTASI SISTEM KLASIFIKASI MOBIL PADA SISTEM PENGATURAN LAMPU LALU LINTAS TERDISTRIBUSI BERBASISKAN JARINGAN SYARAF TIRUAN

Sistem Pengaturan Lampu Lalu Lintas Terdistribusi adalah sebuah sistem lampu lalu lintas yang ditujukan untuk memenuhi kebutuhan akan kinerja pengaturan lampu lalu lintas yang cerdas dengan pengambilan data secara real-time. Sistem ini dapat melakukan penjadwalan dan pengaturan jaringan banyakpersimpangan secarareal-time yang tidak bisa dilakukan oleh sistem pengaturan lampu lalu lintas konvensional. Penerapan klasifikasi di dalam sistem ini digunakan untuk meningkatkan akurasi dari pengenalan mobil. Proses klasifikasi diimplementasikan menggunakan tiga algoritma Jaringan Syaraf Tiruan, yakni Backpropagation, FLVQ, dan FLVQ-PSO. Berdasarkan hasil ujicoba, dapat ditunjukkan bahwa algoritma Backpropagationmemiliki performa akurasi yang lebih baik dibandingkan dua algoritma JST yang lainnya. Distributed Traffic Light Control System is a traffic light system intended to meet the need for setting the performance of intelligent traffic lights with real-time data capturing. The system can perform scheduling and network settings of multi-junction in real time that can not be done by a conventional traffic light settings system. Application of classification within this system is used to improve the accuracy of the car recognition. Classification process is implemented using three neural network algorithms, namely Backpropagation, FLVQ, and FLVQ-PSO. Based on the test results, it can be shown that the Backpropagation algorithm performs better accuracy than the other two algorithms

INTEGRATED LOW LIGHT IMAGE ENHANCEMENT IN TRANSPORTATION SYSTEM

Recent Intelligent Transportation System (ITS) focuses on both traffic management and Homeland Security. It involves advance detection systems of all kind but proper analysis of the image data is required for controlling and further processing. It becomes even more difficult when it comes to low light images due to limitation in the image sensor and heavy amount of noise. An ITS supports all levels like (Transport policy level, Traffic control tactical level, Traffic control measure level, Traffic control operation). For this it uses several split systems like Real time passenger information (RTPI), Automatic Number Plate Recognition (ANPR), Variable message signs (VMS), Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) system. While analyzing critical scenarios, mostly for the development of the application for Vehicle to Infrastructure (V2I) System several cases are taken into consideration. From these cases some are very difficult to analyze due to the visibility of the background as the detail structure is taken into consideration. Here Direct processing of low light images or video frames like day images leads to loss of required data, so an efficient enhancement method is required which gives allowable result for further transformation and analysis with minimal processing. So an Adaptive Enhancement Method is presented here which applies different enhancement methods for day light and low light images separately. For this purpose a combination of image fusion, edge detection filtering and Contourlet transformation is used for low light images; tone level adjustment and low level feature extraction for enhancement of day light images

Low-cost automatic number plate detection system

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2022The ability to detect vehicle license plates has been implemented to reduce traffic violations with varying success. Some of these applications have limited coverage or reduced functionality when used in real-time, as some were only tested with still images and datasets. In this paper, I propose a technique for implementing the Automatic Number Plate Recognition (ANPR) System using Python and Open Computer Vision Library integrated with a traffic light to capture and interpret the number plate of moving vehicles in different lighting conditions and at varying times speeds. The system is 100% successful at detecting vehicle number plates for cars traveling at 5kmph.Ashesi Universit

Managing mine road maintenance interventions using mine truck on-board data

The management of unpaved mine road networks on large surface mines rarely results in optimal road maintenance strategies and minimised total road-user costs. This is ascribed mostly to the complex and dynamic combination of variable road networks and loading and discharge points. In a dynamic mining environment - typically those mines in which production is managed by a centralised truck dispatch system - there is no guarantee that a particular road maintenance intervention will contribute significantly to reducing total road-user costs or increasing productivity. Most large surface mines operating ultra-heavy mine trucks rely on an integrated on-board diagnostic data collation, communication and GPS-asset location system as a real-time fleet management tool. By extending this system to incorporate a multi-sensor analytical procedure in which specific truck vital signs are monitored and filtered, a road defect can be recognised and a trigger level set to relay the location of defects on the mine haul road to the centralised truck data management system. Previous work established the feasibility of the multi-sensor approach to road defect recognition on large mine haul trucks, and outlined the defect recognition, analytical and modelling issues and system limitations. This paper presents the development of the analytical procedure used as a basis for evaluating the truck on-board data to establish maintenance priorities amongst a network of mine roads. Following an introduction to the system architecture, the results of system field trials are analysed and the results discussed in the light of defect density and traffic volume as the primary variables in an approach to prioritising road maintenance. The paper concludes that by supplementing the existing mine communication and asset management systems, road maintenance can be managed on a near real-time basis and maintenance equipment dispatched to where most immediate benefit will realised from a maintenance intervention on the network, thereby generating the maximum improvement in service and reduction in cost per ton hauled

HOG, LBP and SVM based Traffic Density Estimation at Intersection

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and can run efficiently on raspberry pi board. Code is released at https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201