Ziheng Lu¹, Yingfeng Cai^1,*, Wei Dong¹, Hai Wang^2,*, Long Chen¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086003 - 28 August 2026

Abstract Vision-language-action models have recently gained attention in autonomous driving, as language can express high-level behavioral intent beyond geometric waypoints. However, existing planners often treat language as an external command or auxiliary explanatory signal, while the hidden states of generated control instructions are rarely aligned explicitly with the latent variables that produce executable trajectories. This paper presents MALT-Drive, a moment-aligned language-to-trajectory end-to-end planning framework for autonomous driving. Given front-camera visual input, ego-state history, route priors, and a driving-oriented VQA prompt, MALT-Drive first autoregressively generates a concise control instruction from a controlled instruction space and then decodes… More >

Ping Ma^1,2, Quan Wang^1,2, Yiyang Chen^3,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084488 - 13 August 2026

Abstract Connected vehicles operating in V2X-enabled intelligent transportation systems often perform repetitive trajectory tracking in repeated tasks. In practical applications, traffic conditions, communication quality, and sensing accuracy may vary from trial to trial. These variations induce time-varying dynamics across repeated runs and reduce the effectiveness of iterative learning control (ILC) schemes when fixed or inaccurately identified models are used. To address this issue, this paper proposes a parameter-estimation-based ILC framework for connected vehicles. Parameter estimation is integrated with a norm-optimal ILC design through an expectation-maximization strategy. The time-varying model parameters and the learning input are updated More >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084571 - 23 July 2026

Abstract Connected automated driving increasingly relies on cooperative perception from onboard sensors, roadside units (RSUs), smart traffic lights, and vehicle-to-everything (V2X) links, but communication uncertainty can concentrate residual risk on vulnerable road users (VRUs). This study proposes an Ethical-Improved risk-allocation objective for edge-assisted Internet of Vehicles (IoV) cooperative autonomous driving. The objective internalizes responsibility as a bounded risk weight, normalizes equality and maximin terms, and adds explicit VRU tail-risk and VRU/Ego ratio penalties. The evaluation is organized into two strictly separated tracks. In the planner-objective track, Ethical-Improved reduces physical collision rate, aggregate harm, inequality, and VRU More >

Jing Liu^1,2, Weijie Tan^1,2,3,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083401 - 23 July 2026

Abstract Due to its low hardware cost and ease of deployment, WiFi fingerprinting has become a prominent research direction in indoor positioning. However, traditional methods based on Received Signal Strength (RSS) still face three critical challenges: susceptibility to noise interference, low retrieval efficiency as fingerprint databases scale up, and trajectory instability in dynamic environments. These challenges are inherently rooted in the stochastic fluctuation of RSS signals, the high-dimensional and non-Euclidean nature of fingerprint space, and the unpredictability of user movement patterns. To address these limitations, an adaptive trajectory-assisted dynamic indoor positioning algorithm based on RSS fingerprinting,… More >

Alaa M. Momani¹, Deema Mohammed Alsekait², Mahmoud Ahmad Al-Khasawneh^1,*, Siti Hajar Othman³, Ibraheem Al-Tarawneh⁴, Nikunj Sharma⁵, Wee How Khoh⁶

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082572 - 23 July 2026

Abstract Unmanned Aerial Vehicles (UAVs) are finding more and more applications in logistics, surveillance, and other operations at a large scale. However, autonomous navigation in dynamic traffic situations is not an easy task due to limited energy, moving obstacles, and inter-agent interactions. The proposed paper can be discussed as a Generative World Modeling (GWM) framework of risk-focused UAV navigation in the dynamic traffic network. This paper proposes a GWM framework for risk-aware UAV navigation in dynamic traffic networks. The proposed design incorporates three key elements; a generative world model for predicting future environmental conditions, a diffusion-based… More >

Wei Yin^1,*, Jun Wang¹, Ming Tong¹, Zhijun Chen¹, Ke Zhang¹, Meiqing Huang², Ran Gu², Keman Lin²

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.075066 - 18 June 2026

Abstract With the high penetration of renewable energy and the rapid development of AC/DC (Alternating Current/Direct Current) hybrid power grid, the power grid is confronted with challenges such as frequent voltage fluctuations and insufficient dynamic reactive power reserves. Full utilization of unified power flow controller (UPFC) in dynamic voltage regulation is of great significance for mitigating voltage excursions of the power grid. This paper proposes a double-time-scale dynamic reactive power optimization method for the AC/DC hybrid power grid with UPFC. A control framework for reactive power optimization of slow-time-scale and fast-time-scale is constructed incorporating the LCC-HVDC… More >

Yao-Bo Long¹, Yu-Ke Ouyang², Bo Zhuang², Ao-Qi Liu^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081203 - 15 June 2026

Abstract This study addresses the real-time visual tracking task in edge environments by proposing a robust visual servoing control system based on a higher-order sliding mode observer, enabling a quadrotor UAV to autonomously track a moving soccer ball during outdoor sports broadcasts while relying solely on a monocular camera and an inertial measurement unit, thereby eliminating any dependency on external positioning or velocity sensors such as GPS. The system adopts a hierarchical control architecture in which the observer plays a central role: operating on resource-constrained edge devices, it leverages only visual information to estimate unknown external… More >

Xuan-Thuc Nguyen¹, Le-Minh Nguyen¹, Ngoc-Quynh Nguyen¹, Nhu-Nghia Bui², Dinh-Quy Vu^3,*, Thai-Viet Dang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080008 - 15 June 2026

Abstract The development of unmanned automated vehicles (UAVs) has become a key focus in aerial robotics, fueling the need for navigation systems capable of performing complex and delicate tasks with speed and precision. However, the end-to-end path tracking process often encounters challenges in learning efficiency, generalization, and varying environmental conditions. In this paper, we propose the novel IRL-TP framework for learning-based UAVs’ trajectory planning that employs a deep inverse reinforcement learning (IRL) approach. Firstly, the RL-based path planner must develop a reward function that effectively captures flight safety, collision avoidance, trajectory smoothness, and navigation efficiency within… More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Chedia Jarray², Huda Althumali³, Faten A. Saif ⁴, Raja Azlina Raja Mahmood¹, Mohammed Sani Adam⁵, Nor Fadzilah Abdullah⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077054 - 27 May 2026

Abstract Non-terrestrial networks (NTNs)—including UAVs, HAPs, and satellite systems—are rapidly becoming key enablers of wide-area, resilient connectivity for large-scale IoT applications. As these platforms integrate with terrestrial networks to form space–air–ground architectures, optimization challenges related to trajectory, resource management, energy efficiency, and security become increasingly complex. Machine learning (ML) has emerged as a central tool for addressing these challenges by enabling adaptive, data-driven decision-making under uncertainty. This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets. Using a structured methodology involving dataset curation, keyword filtering, metadata analysis, and citation-based paper selection,… More >

Feng-Cheng Lin^*, Bo-Chiao Jan, Hui-An Wu

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079393 - 08 May 2026

Abstract With the increasing refinement of ornamental fish culture, understanding fish behavioral patterns has become critical. Fish movements not only reflect daily activity ranges but also reveal responses to environmental changes such as water currents and obstacles. However, traditional manual observation is limited by manpower and time, making it difficult to record fish behaviors over long periods stably. Existing automated tracking techniques often suffer from ID switches and track interruptions caused by rapid fish movement, occlusions, or intermingling, which in turn degrade the reliability of subsequent analyses. This paper proposes a deep learning-based multi-object fish tracking… More >