Improving the Smart Cities Traffic Management Systems using VANETs and IoT Features

This paper discusses the creation of an integrated worldwide system based on integrating and linking automobiles with VANET, IoT, and AI technologies, which will have a substantial impact on the smart, safe transportation system. This paper aims to apply a proposed project to a specific area in Jordan to examine the projects viability and its impact on reducing the accident rate by controlling traffic with special traffic rules in the study area using a cloud database that stores all the private information for each car and receives information about the cars speed as it travels. When a driver exceeds the speed established by the Traffic Department, he receives warning messages informing him that he has over the speed limit, and if he does not respond to the warning messages, he gets a fine. The research focuses on optimizing the utilization of VANET network services, which is crucial for enhancing public safety applications involving data exchange between automobiles and RSUs. The simulation was conducted using OMNeT++ version 5.7 on Debian 11, Linux 5, and GNOME 3 operating systems. As a network simulator, it is a scientifically approved open-source too

Analogical study of Support Vector Machine (SVM) and Neural Network in Vehicleas Number Plate Detection

Formal grammars, studied by N. Chomsky for the definition of equivalence with languages and models of computing, have been a useful tool in the development of compilers, programming languages, natural language processing, automata theory, etc. The words or symbols of these formal languages can denote deduced actions that correspond to specific behaviors of a robotic entity or agent that interacts with an environment. The primary objective of this paper pretend to represent and generate simple behaviors of artificial agents. Reinforcement learning techniques, grammars, and languages, as defined based on the model of the proposed system were applied to the typical case of the ideal route on the problem of artificial ant. The application of such techniques proofs the viability of building robots that might learn through interaction with the environment

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

Next-gen traffic surveillance: AI-assisted mobile traffic violation detection system

Road traffic accidents pose a significant global public health concern, leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million people lose their lives daily due to traffic accidents [World Health Organization, 2022]. Addressing this issue requires accurate traffic law violation detection systems to ensure adherence to regulations. The integration of Artificial Intelligence algorithms, leveraging machine learning and computer vision, has facilitated the development of precise traffic rule enforcement. This paper illustrates how computer vision and machine learning enable the creation of robust algorithms for detecting various traffic violations. Our model, capable of identifying six common traffic infractions, detects red light violations, illegal use of breakdown lanes, violations of vehicle following distance, breaches of marked crosswalk laws, illegal parking, and parking on marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash camera, we apply the YOLOv5 algorithm's detection module to identify traffic agents such as cars, pedestrians, and traffic signs, and the strongSORT algorithm for continuous interframe tracking. Six discrete algorithms analyze agents' behavior and trajectory to detect violations. Subsequently, an Identification Module extracts vehicle ID information, such as the license plate, to generate violation notices sent to relevant authorities

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Computer Vision-Based Traffic Sign Detection and Extraction: A Hybrid Approach Using GIS And Machine Learning

Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google Street View image-based approach). Then 32 traffic signs were recognized and pinpointed using the second method (GoPro video-based approach) for better location accuracy, within 10 meters. The average distance from the observation points to the 32 ground truth references was 7.78 meters. The advantages of these methods were discussed. GoPro video-based approach has higher location accuracy, while Google Street View image-based approach is more accessible in most major cities around the world. The proposed traffic sign detection workflow can thus extract and locate traffic signs in other cities. For further consideration and development of this research, IMU (Inertial Measurement Unit) and SLAM (Simultaneous Localization and Mapping) methods could be integrated to incorporate more data and improve location prediction accuracy