Mahmudul Hasan¹, Shuji Doman¹, A. S. M. Bakibillah², Md Abdus Samad Kamal^1,*, Kou Yamada¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-17, 2026, DOI:10.32604/cmc.2025.070222 - 10 November 2025

Abstract Traffic at urban intersections frequently encounters unexpected obstructions, resulting in congestion due to uncooperative and priority-based driving behavior. This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles (CAVs) at single-lane intersections, particularly in the context of left-hand side driving on roads. The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks. We consider that all approaching vehicles share relevant information through vehicular communications. The Intersection Coordination Unit (ICU) processes this information and communicates the optimal crossing or turning times to the vehicles. The primary objective of this… More >

Hui Wei^1,2, Zhanfei Ma^1,3,*, Jing Jiang¹, Bisheng Wang¹, Zhong Di¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069236 - 10 November 2025

Abstract The Internet of Vehicles (IoV) operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms. Existing methods often suffer from complex certificate management, inefficient consensus protocols, and poor resilience in high-frequency communication, resulting in high latency, poor scalability, and unstable network performance. To address these issues, this paper proposes a secure and efficient distributed authentication scheme for IoV with reputation-driven consensus and SM9. First, this paper proposes a decentralized authentication architecture that utilizes the certificate-free feature of SM9, enabling lightweight authentication and key negotiation, thereby… More >

Junjun Ren¹, Guoqiang Chen², Zheng-Yi Chai³, Dong Yuan^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068795 - 10 November 2025

Abstract Vehicle Edge Computing (VEC) and Cloud Computing (CC) significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit (RSU), thereby achieving lower delay and energy consumption. However, due to the limited storage capacity and energy budget of RSUs, it is challenging to meet the demands of the highly dynamic Internet of Vehicles (IoV) environment. Therefore, determining reasonable service caching and computation offloading strategies is crucial. To address this, this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading. By… More >

Zhenpeng Jiang, Qingquan Liu^*, Ende Wang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068780 - 10 November 2025

Abstract Rapidly-exploring Random Tree (RRT) and its variants have become foundational in path-planning research, yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety. To address these challenges, we introduce HS-APF-RRT*, a novel algorithm that fuses layered sampling, an enhanced Artificial Potential Field (APF), and a dynamic neighborhood-expansion mechanism. First, the workspace is hierarchically partitioned into macro, meso, and micro sampling layers, progressively biasing random samples toward safer, lower-energy regions. Second, we augment the traditional APF by More >

Ghulam Mujtaba^1,2,#, Wenbiao Liu^1,#, Mohammed Alshehri³, Yahya AlQahtani⁴, Nouf Abdullah Almujally⁵, Hui Liu^1,6,7,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.067895 - 10 November 2025

Abstract As urban landscapes evolve and vehicular volumes soar, traditional traffic monitoring systems struggle to scale, often failing under the complexities of dense, dynamic, and occluded environments. This paper introduces a novel, unified deep learning framework for vehicle detection, tracking, counting, and classification in aerial imagery designed explicitly for modern smart city infrastructure demands. Our approach begins with adaptive histogram equalization to optimize aerial image clarity, followed by a cutting-edge scene parsing technique using Mask2Former, enabling robust segmentation even in visually congested settings. Vehicle detection leverages the latest YOLOv11 architecture, delivering superior accuracy in aerial contexts… More >

Muflih Alhazmi¹, Waqar Ul Hassan², Saba Shaheen³, Mohammed M. A. Almazah⁴, Azmat Ullah Khan Niazi^3,*, Nafisa A. Albasheir⁵, Ameni Gargouri⁶, Naveed Iqbal⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1903-1940, 2025, DOI:10.32604/cmes.2025.072973 - 26 November 2025

Abstract This paper proposes a model-based control framework for vehicle platooning systems with second-order nonlinear dynamics operating over switching signed networks, time-varying delays, and deception attacks. The study includes two configurations: a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus (FNTBC) and Fixed-Time Bipartite Consensus (FXTBC), and a leader—follower structure ensuring structural balance and robustness against deceptive signals. In the leaderless model, a bipartite controller based on impulsive control theory, gauge transformation, and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays. The FNTBC achieves finite-time convergence depending on initial More >

Bappa Muktar^*, Vincent Fono, Adama Nouboukpo

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4705-4727, 2025, DOI:10.32604/cmc.2025.067733 - 23 October 2025

Abstract Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization,… 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 >

Abdulwahab Alazeb¹, Muhammad Hanzla², Naif Al Mudawi^1,*, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4677-4697, 2025, DOI:10.32604/cmc.2025.065899 - 30 July 2025

Abstract Unmanned Aerial Vehicles (UAVs) have become indispensable for intelligent traffic monitoring, particularly in low-light conditions, where traditional surveillance systems struggle. This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination, noise, and occlusions. Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges, while YOLOv10 enables accurate vehicle localization. The framework employs GLOH and Dense-SIFT for discriminative feature extraction, optimized using the Whale Optimization Algorithm to enhance classification performance. A Swin Transformer-based classifier provides the final categorization, leveraging hierarchical attention mechanisms More >

Naif Al Mudawi^1,*, Muhammad Hanzla², Abdulwahab Alazeb¹, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4491-4509, 2025, DOI:10.32604/cmc.2025.065490 - 30 July 2025

Abstract Unmanned Aerial Vehicles (UAVs) are increasingly employed in traffic surveillance, urban planning, and infrastructure monitoring due to their cost-effectiveness, flexibility, and high-resolution imaging. However, vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes, frequent occlusions in dense traffic, and environmental noise, such as shadows and lighting inconsistencies. Traditional methods, including sliding-window searches and shallow learning techniques, struggle with computational inefficiency and robustness under dynamic conditions. To address these limitations, this study proposes a six-stage hierarchical framework integrating radiometric calibration, deep learning, and classical feature engineering. The workflow… More >