Jul 2026· International Journal of Next-Generation Engineering and Technology· Vol 03, pp. 7-15· 0 citations
Abstract
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.
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.
J. Arsac· International Journal of Mod...· 0 citations
This study investigates the integration of Emerging Computing Technologies into smart road infrastructures as a potential response to these challenges, and explores key enabling technologies for their capacity to support intelligent transportation systems.
Afzal Badshah, Ali Daud, S. Arafat et al.· Computers, Materials & C...· 0 citations
The rapid development of artificial intelligence technology has brought new momentum to the autonomous driving industry. The deep integration of these two fields has become a core direction for transformation in the transportation sector. Relying on core technologies such as environmental perception, decision-making and planning, and vehicle-infrastructure cooperation, artificial intelligence effectively enhances the operational efficiency and travel safety of autonomous vehicles. This paper takes the application of AI in the field of autonomous driving as its research object, briefly outlines the core principles of this technology's implementation, and analyzes the current development opportunities for autonomous driving by considering the scale of China's new energy vehicle industry, massive driving data, and policy and infrastructure advantages. The study finds that the large-scale popularization of autonomous driving is still constrained by multiple factors. These include not only technical bottlenecks such as poor adaptability to long-tail scenarios and unstable algorithmic decision-making in extreme situations, but also practical difficulties like lagging laws and regulations, ambiguous accident liability definitions, and imperfect data security and personal privacy protection systems. To address these issues, this paper, drawing on cutting-edge technological architectures, industry policy directions, and data management standards, proposes breaking through technical challenges via multimodal large models and Vehicle-Road-Cloud integrated architecture, and improving the governance system by revising traffic laws, unifying industry standards, and establishing a tiered data protection mechanism. The research results can provide theoretical reference and ideas for achieving safe and compliant large-scale commercial and civilian use in China.
Yuxuan Peng· Applied and Computational En...· 0 citations
Autonomous vehicles (AVs) represent a foundational cornerstone of future smart city transportation systems, offering the potential to eliminate human driving errors, reduce traffic fatalities by at least 40%, and optimize energy consumption. While AV technology is advancing rapidly toward highly automated driving (HAV) and driver-out Level 4 (L4) deployment, commercial realization remains hindered by significant technological uncertainties, soaring development costs, and strict safety-critical validation requirements. This provides a comprehensive, holistic survey of autonomous vehicle technology, bridging gaps in the existing literature by tracing its historical evolution to modern platforms equipped with LiDAR, radar, cameras, and (V2X) communication. Beyond exploring essential architectural components, it evaluates the modern state of research by benchmarking the three dominant autonomous vehicle software architectures: modular pipelines, pure End-to-End (E2E), and hybrid systems across seven quantitative dimensions: planning quality, safety certification, latency, data efficiency, debuggability, Operational Design Domain (ODD) adaptation, and robustness. Comparative analysis of industry-standard benchmarks, including nuScenes and CARLA, reveals that while E2E and hybrid approaches achieve superior planning scores (88–91%) and lower collision rates (1.1–2.0%) than modular pipelines (84–86% planning; 3.95% collisions), pure E2E models lack a viable regulatory path for 2026 L4 deployment due to prohibitive validation mandates. Conversely, hybrid modular-E2E algorithms recover approximately 98% of E2E planning performance, drastically reduce debugging times to 2–6 hours, and retain compliance with ISO 26262:2018 ASIL-D safety standards. This work maps out the market landscape and identifies hybrid architectures as the current Pareto-optimal solution, balancing operational capability, safety assurance, and immediate regulatory feasibility for L3/L4 automation
Okpala Sanctus Emekumeh, Edje E. Abel, Ojugo A. Arnold· FUDMA Journal of Sciences· 0 citations
Due to increasing vehicle density, urbanization, and complex mobility patterns, road traffic injuries continue to pose a serious threat to public health and safety on a global scale. This ongoing crisis highlights the urgent need for intelligent, connected, and proactive vehicular systems that can prevent collisions, reduce injuries, and optimize traffic flow in real-time. The Internet of Vehicles (IoV) has become a key component of next-generation intelligent transportation, enabling seamless communication, data sharing, and collaborative decision-making among vehicles, roadside infrastructure, and cloud services. However, the effectiveness of current IoV communication frameworks in the real world is hampered by issues such as high latency, inefficient bandwidth utilization, limited scalability, and inadequate trust management. To address these challenges, this survey thoroughly examines 50 cutting-edge studies (2021–2025), including V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure), and hybrid V2X (Vehicle-to-Everything) communication, edge–fog–cloud orchestration, 5G/6G integration, SDN (Software Defined Networking)/NFV (Network Function Virtualization) programmability, and security and trust-aware techniques. We provide a structured comparative analysis of communication types, enabling technologies, and limitations. Building on these insights, we propose 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. Furthermore, we identify persistent research gaps and outline targeted future directions. The analysis suggests that AI-based optimization combined with hybrid and multi-tier designs has the potential to significantly improve network resilience, adaptability, and efficiency, offering a promising foundation for high-performance, secure, and reliable IoV systems.
Arbab Waheed Ahmad, M. Derawi, Raja Sana Gul· IEEE Open Journal of the Com...· 0 citations
Rapid urbanization and increasing traffic demand require intelligent transportation systems that can adapt in real time. Traditional centralized traffic management is limited in handling dynamic traffic conditions, leading to congestion, delays, higher fuel consumption, and reduced road safety. This study proposes 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. The framework enables traffic signals, vehicles, roadside units, and other transportation entities to operate as autonomous, cooperative agents that exchange real-time information and make distributed decisions. By analyzing traffic, environmental, and infrastructure data, the proposed architecture dynamically optimizes signal control, routing, and emergency response. Simulation results demonstrate improvements in traffic flow, congestion reduction, travel time, fuel efficiency, road safety, scalability, and system resilience. The framework provides a scalable and intelligent foundation for future smart cities, connected autonomous transportation, and sustainable urban mobility.
Seshagiri N· International Journal of Mod...· 0 citations