Jian Su^1,2,*, Jiaqi Wang²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082531 - 15 June 2026

Abstract Despite significant advances in object detection technology, vulnerable pedestrian detection in intelligent transportation systems remains highly challenging under complex weather conditions. Environmental factors such as fog, rain, and snow often lead to occlusion, motion blur, and low-contrast images, making small-scale or weak-featured vulnerable pedestrians difficult to accurately identify. Therefore, improving the detection accuracy and robustness of vulnerable pedestrians in complex weather scenarios has become an urgent research problem. To address this issue, this paper proposes an improved YOLOv8-based vulnerable pedestrian complex weather detection algorithm, termed VPCW-YOLO. The proposed method enhances detection performance through multiple structural… More >

Akmalbek Abdusalomov¹, Kudratjon Zohirov², Sojida Ochilova², Jakhongir Oramov³, Zafar Ruziyev³, Malika Rustamova⁴, Gulrukh Sherboboyeva⁵, Komil Tashev^6,7, Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081292 - 15 June 2026

Abstract Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted… More >

Ying Tang¹, Chuanyi Ma², Feng Guo^1,*, Wenhao Sun¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079851 - 15 June 2026

Abstract Road Abandoned Objects (RAOs) pose significant threats to traffic safety, particularly due to their small size, irregular shapes, and unpredictable distribution in complex road environments. The primary objective of this study is to develop an accurate and real-time detection framework for RAOs while maintaining low computational cost for practical deployment. To achieve this, we propose RAO-YOLO, a lightweight vision-based detection framework built upon an enhanced YOLO architecture. Specifically, a Mixed Aggregation Network (MANet) is introduced to improve multi-scale feature representation, and a Lightweight Shared Detail-Enhanced Detection (LSDD) head is designed to enhance localization accuracy for More >

Isam Bahaa Aldallal¹, Saadaldeen Rashid Ahmed^2,3, Abdullahi Abdu Ibrahim¹, Oguz Bayat⁴, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078742 - 15 June 2026

Abstract Urban parking problems worsen traffic jams, gas use, and pollution. Old parking systems often lack up-to-date space information, which annoys drivers and wastes their time. This research presents a smart IoT-enabled real-time space monitoring and booking system applicable to both urban parking management and Smart Manufacturing logistics environments, including loading bay coordination and Automated Guided Vehicle (AGV) docking station management. The system employs ultrasonic and IR sensors, managed by an Arduino UNO, to identify vehicles and track space availability. A servo-motor regulates entry. Slot data is presented on a Liquid Crystal Display screen and accessible More >

Muhammet Ali Karabulut¹, A. F. M. Shahen Shah², Al-Sakib Khan Pathan^3,*, Phillip G. Bradford⁴

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077625 - 08 May 2026

Abstract Today, technological progress is broad and deep. The next generation networks and systems will integrate features, technologies, and models requiring smooth cooperation between new and old technologies. This survey’s uniqueness is that it considers an integrated, hybrid and heterogeneous future where Internet of Things (IoT), Sixth-Generation (6G) mobile communications technology, and Artificial Intelligence (AI) will work together, providing a smart and connected Intelligent Transportation System (ITS). This smart ITS will give better road safety and optimized travel. Currently, there is a scarcity of surveys focusing particularly on smart ITS that is expected soon. In this More >

Hamad Ali Abosaq¹, Jarallah Alqahtani^1,*, Fahad Masood², Alanoud Al Mazroa³, Muhammad Asad Khan⁴, Akm Bahalul Haque⁵

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075250 - 12 March 2026

Abstract The technological advancement of the vehicular Internet of Things (IoT) has revolutionized Intelligent Transportation Systems (ITS) into next-generation ITS. The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment. The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient, mobility-aware, and secure system for distributed intelligence. This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning (DRL-FL) approach to design an energy-efficient and threat-resilient ITS. In this approach, a Policy Proximal Optimization (PPO)-based DRL agent is first employed for adaptive client selection. Second, More >

Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Meshari D. Alanazi¹, Gehan Elsayed^2,*, Turki M. Alanazi³, Anis Sahbani⁴, Amr Yousef^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2277-2309, 2025, DOI:10.32604/cmes.2025.070726 - 26 November 2025

Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >

Tarak Nandy^1,*, Sananda Bhattacharyya²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 181-219, 2025, DOI:10.32604/cmc.2025.066306 - 29 August 2025

Abstract In the rapidly evolving landscape of intelligent transportation systems, the security and authenticity of vehicular communication have emerged as critical challenges. As vehicles become increasingly interconnected, the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential. On the other hand, using intelligence in the authentication system is a significant attraction. While existing surveys broadly address vehicular security, a critical gap remains in the systematic exploration of Deep Learning (DL)-based authentication methods tailored to these communication paradigms. This survey fills that gap by offering a comprehensive… More >

Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 255-274, 2025, DOI:10.32604/jai.2025.069008 - 29 August 2025

Abstract Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% More >