2024· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
The proposed framework brings in intelligent decision-making algorithms and edge-enabled V2X communication to enable dynamic traffic control, accident prevention, route optimization, and emergency response coordination to enhance transportation systems and road safety.
Abstract
Intelligent Transportation Systems (ITS) have become a transformative solution to address the emerging challenges such as traffic congestion, road accidents, environmental pollution and inefficient mobility management in urban transportation. The Vehicle-to-Everything (V2X) communication has greatly improved the potential of ITS by allowing for the seamless and real-time exchange of information between vehicles, road-side infrastructure, pedestrians, cloud platforms and communication networks. V2X communication is defined as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Network (V2N) and Vehicle-to-Grid (V2G) communication, all of which will result in an intelligent transportation ecosystem that will support autonomous driving, cooperative traffic management and smart mobility services. The fifth-generation (5G) and emerging sixth-generation (6G) wireless communication technologies, along with Artificial Intelligence (AI), Edge Computing, IoT and Cloud Computing, have driven the deployment of very reliable, low latency V2X-enabled transportation systems even further. This paper provides a thorough review of current Intelligent Transportation Systems (ITS) technologies, highlights research gaps and introduces an artificial intelligence (AI) based communication framework to enhance transportation systems and road safety. The proposed framework brings in intelligent decision-making algorithms and edge-enabled V2X communication to enable dynamic traffic control, accident prevention, route optimization, and emergency response coordination. The study also covers communication protocols, system architecture, performance metrics, and challenges in implementing V2X. The results show that intelligent V2X communication greatly contributes to traffic efficiency, communication latency, and driving safety, as well as sustainable smart city transportation infrastructures.
Urban transportation networks are essential infrastructures that support the movement of people and goods in modern cities. However, rapid urbanization, population growth, and the increasing number of vehicles have led to serious challenges such as traffic congestion, road accidents, increased travel time, and environmental pollution. Conventional traffic management systems rely mainly on fixed-time signal control strategies, which are unable to adapt to dynamic and real-time traffic conditions, resulting in inefficient traffic flow and underutilization of road infrastructure. To overcome these limitations, Artificial Intelligence (AI) and the Internet of Things (IoT) have been integrated into Intelligent Transportation Systems (ITS) to enable intelligent and adaptive traffic management. IoT-enabled devices such as smart cameras, inductive loop detectors, GPS modules, and environmental sensors continuously collect real-time traffic parameters including vehicle density, speed, queue length, and traffic flow. These heterogeneous data streams are transmitted through wireless communication networks to cloud or edge computing platforms for real-time data processing and analysis. AI-based algorithms, including machine learning and deep learning models, analyze the collected traffic data to identify spatiotemporal traffic patterns, predict congestion levels, and optimize traffic signal timings. Advanced techniques such as reinforcement learning enable adaptive traffic signal control by dynamically adjusting signal phases based on current traffic conditions, thereby reducing vehicle delays and improving intersection throughput. Furthermore, Vehicle-to-Everything Communication (V2X) facilitates communication between vehicles, infrastructure, and pedestrians, enabling cooperative traffic management and improved road safety. Despite these advancements, challenges such as high deployment costs, data privacy concerns, and interoperability issues remain. Continuous research in AI-driven IoT frameworks and secure communication technologies is essential to achieve efficient, scalable, and sustainable smart transportation systems.
M. Rakshana, Natalia Andria, P. Muthukumar et al.· International Conference on...· 0 citations
The rapid advancements in intelligent mobility, linked cars, and autonomous transportation systems have elevated transportation safety to the forefront of research priorities. When it comes to monitoring, decision-making, and road safety, traditional transport is being utterly transformed by technologies like AI, ML, the Internet of Things (IoT), V2X communications, edge computing, and autonomous cars. The present review provides an extensive overview of the key concepts of transportation safety, such as VRUs, camera-based perception, AI detection and recognition, tracking and trajectory prediction techniques. In addition, the state-of-the-art in the areas of IoT enabled transportation systems, intelligent vehicles and V2X communication frameworks is discussed to explore how these innovations enable cooperative and connected mobility. Besides that, the main challenges faced by the autonomous vehicles, especially safety and reliability issues, legal and moral concerns, infrastructure issues and human-machine interaction are considered. Moreover, the recent literature in the area of emerging transportation technologies is reviewed in order to examine the most recent trends, advancements and open problems. This analysis demonstrates how critical it is to build a trustworthy transportation ecosystem by integrating intelligent communication networks, artificial intelligence (AI), and edge computing.
Manish Jain· International Journal of Nex...· 0 citations
A Multi-Agent Autonomous Control Framework that integrates Multi-Agent Systems (MAS), Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Internet of Things (IoT), Vehicle-to-Everything (V2X) communication, and edge-cloud computing is proposed.
Seshagiri N· International Journal of Mod...· 0 citations
An adaptive multi-tier IoV connectivity architecture that offers ultra-low latency, high scalability, and robust interoperability through distributed edge–cloud processing, AI-driven resource orchestration, adaptive blockchain-enabled security, and cross-technology communication control is proposed.
Arbab Waheed Ahmad, M. Derawi, Raja Sana Gul· IEEE Open Journal of the Com...· 0 citations
With the acceleration of the global digitalization process, intelligent connected vehicles have become the strategic direction for the transformation and upgrading of the automobile industry. This paper focuses on the application of 5G technology in the innovation of intelligent connected automobile industry to carry out systematic research, builds a hierarchical architecture model and fusion perception algorithm based on 5G-V2X, and proposes a multivariate data fusion method combining Kalman filter and attention mechanism neural network, which significantly improves the environmental perception and collaborative decision-making ability of vehicles. Through multi-scenario experimental verification, it can be seen that the system can reduce the communication delay to 8.2 milliseconds, the positioning accuracy to 12.3 cm, the emergency braking frequency to 65.71% and the traffic efficiency to 19.06% in the 5G network environment, which effectively solves the core problems of intelligent connected vehicles in real-time, safety and traffic collaborative optimization. To sum up, the research results clarify the key roles of 5G-V2X communication, edge computing and vehicle-road-cloud collaboration mechanism in the integration of production and research and commercialization, and provide theoretical support and practical reference for the industrial innovation path.
Bin Guo· International Conference on...· 0 citations
—Efficient traffic signal control is essential for reducing congestion, emissions and travel delays in modern urban environments. Traditional Vehicle-to-Infrastructure (V2I) systems are limited by infrastructure coverage, while Vehicle-to-Vehicle (V2V) communication alone lacks global signal-state awareness. This study proposes a hybrid V2V-V2I communication model that enhances Green Light Optimal Speed Advisory (GLOSA) performance by allowing vehicles to relay Signal Phase and Timing (SPaT) information in low-infrastructure or obstructed environments. A mathematical formulation describing vehicle motion, inter-vehicle message propagation and signal-state transitions is developed, complemented by a detailed algorithmic description of the hybrid control logic. The model is implemented using SUMO with the Krauss microscopic car-following model to simulate real-world driving behavior. Results demonstrate that the hybrid model reduces stop frequency, travel time, fuel consumption, and CO₂ emissions compared to standalone V2I and V2V schemes. The added V2V relaying mechanism improves prediction accuracy, particularly in scenarios with limited roadside units. The findings highlight the feasibility, scalability and ecological benefits of integrating multi-source communication in intelligent traffic systems, offering a promising solution for future connected urban mobility.
Abdullah Alsaleh· Journal of Advances in Infor...· 0 citations
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