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  • Open Access

    ARTICLE

    Intelligent Vehicle Lane-Changing Strategy through Polynomial and Game Theory

    Buwei Dang, Huanming Chen*, Heng Zhang, Jixian Wang, Jian Zhou

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2003-2023, 2025, DOI:10.32604/cmc.2025.062653 - 16 April 2025

    Abstract This paper introduces a lane-changing strategy aimed at trajectory planning and tracking control for intelligent vehicles navigating complex driving environments. A fifth-degree polynomial is employed to generate a set of potential lane-changing trajectories in the Frenet coordinate system. These trajectories are evaluated using non-cooperative game theory, considering the interaction between the target vehicle and its surroundings. Models considering safety payoffs, speed payoffs, comfort payoffs, and aggressiveness are formulated to obtain a Nash equilibrium solution. This way, collision avoidance is ensured, and an optimal lane change trajectory is planned. Three game scenarios are discussed, and the More >

  • Open Access

    ARTICLE

    Real-Time Proportional-Integral-Derivative (PID) Tuning Based on Back Propagation (BP) Neural Network for Intelligent Vehicle Motion Control

    Liang Zhou1, Qiyao Hu1,2,3,*, Xianlin Peng4,5, Qianlong Liu6

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2375-2401, 2025, DOI:10.32604/cmc.2025.061894 - 16 April 2025

    Abstract Over 1.3 million people die annually in traffic accidents, and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems. In modern industrial and technological applications and collaborative edge intelligence, control systems are crucial for ensuring efficiency and safety. However, deficiencies in these systems can lead to significant operational risks. This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control, particularly the limitations of traditional Proportional-Integral-Derivative (PID) controllers in managing nonlinear and time-varying dynamics, such as varying road conditions… More >

  • Open Access

    ARTICLE

    Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles

    Cui Zhang1, Maoxin Ji2, Qiong Wu2,*, Pingyi Fan3, Qiang Fan4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3519-3536, 2025, DOI:10.32604/cmc.2025.061292 - 16 April 2025

    Abstract As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system’s robustness and security. To protect vehicle information, we propose a… More >

  • Open Access

    ARTICLE

    A Privacy-Preserving Graph Neural Network Framework with Attention Mechanism for Computational Offloading in the Internet of Vehicles

    Aishwarya Rajasekar*, Vetriselvi Vetrian

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 225-254, 2025, DOI:10.32604/cmes.2025.062642 - 11 April 2025

    Abstract The integration of technologies like artificial intelligence, 6G, and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications. However, these advancements also generate a surge in data processing requirements, necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles. Despite recent advancements, the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources, as well as privacy, remain a concern. In this paper, a lightweight… More >

  • Open Access

    ARTICLE

    Selection and Parameter Optimization of Constraint Systems for Girder-End Longitudinal Displacement Control in Three-Tower Suspension Bridges

    Zihang Wang1, Ying Peng1, Xiong Lan2, Xiaoyu Bai3, Chao Deng1, Yuan Ren1,*

    Structural Durability & Health Monitoring, Vol.19, No.3, pp. 643-664, 2025, DOI:10.32604/sdhm.2025.060302 - 03 April 2025

    Abstract To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge, this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis. This bridge employs an unprecedented tower-girder constraint method, with all vertical supports placed at the transition piers at both ends. This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure, relying on finite element (FE) analysis. Initially, based on the Weigh In Motion (WIM) data, a random vehicle load… More >

  • Open Access

    ARTICLE

    Safety Evaluation of Bridge under Moving Abnormal Indivisible Load Based on Fusing Bridge Inspection Data and Load Test Data

    He Zhang1,2,*, He-Qing Mu2,*, Xiao Zhang3, He Zhang2, Yuedong Yang4

    Structural Durability & Health Monitoring, Vol.19, No.3, pp. 499-530, 2025, DOI:10.32604/sdhm.2025.059070 - 03 April 2025

    Abstract Safety evaluation of a bridge under Moving Abnormal Indivisible Loads (MAILs) directly relates to whether an oversized and/or overweight Large-Cargo Transportation (LCT) vehicle is permitted to pass the bridge. Safety evaluation can be updated by fusing bridge inspection data and load test data, but there are two fundamental difficulties in updating. The first difficulty is to develop an updating scheme to utilize the unstructured inspection data. The second difficulty is to develop a successive updating scheme using load test data based on the previous updating results of the inspection data. This paper proposed a framework,… More >

  • Open Access

    ARTICLE

    Wood Gasification in Catastrophes: Electricity Production from Light-Duty Vehicles

    Baxter L. M. Williams1,*, Henri Croft1, James Hunt1, Josh Viloria1, Nathan Sherman1, James Oliver1, Brody Green1, Alexey Turchin2, Juan B. García Martínez2, Joshua M. Pearce3,4, David Denkenberger1,2,*

    Energy Engineering, Vol.122, No.4, pp. 1265-1285, 2025, DOI:10.32604/ee.2025.063276 - 31 March 2025

    Abstract Following global catastrophic infrastructure loss (GCIL), traditional electricity networks would be damaged and unavailable for energy supply, necessitating alternative solutions to sustain critical services. These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use, such as decentralized electricity generation from wood gas. This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification. A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator. The vehicle successfully produced… More >

  • Open Access

    ARTICLE

    Blockchain-Enabled Edge Computing Techniques for Advanced Video Surveillance in Autonomous Vehicles

    Mohammad Tabrez Quasim*, Khair Ul Nisa

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1239-1255, 2025, DOI:10.32604/cmc.2025.061541 - 26 March 2025

    Abstract The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system (STS). This system lets customers, video creators, and service providers directly connect with each other. Blockchain-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different users. On the other hand, existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer power. Large amounts of video data being sent and analyzed put too much… More >

  • Open Access

    REVIEW

    Artificial Intelligence Revolutionising the Automotive Sector: A Comprehensive Review of Current Insights, Challenges, and Future Scope

    Md Naeem Hossain1, Md. Abdur Rahim2, Md Mustafizur Rahman1,3,*, Devarajan Ramasamy1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3643-3692, 2025, DOI:10.32604/cmc.2025.061749 - 06 March 2025

    Abstract The automotive sector is crucial in modern society, facilitating essential transportation needs across personal, commercial, and logistical domains while significantly contributing to national economic development and employment generation. The transformative impact of Artificial Intelligence (AI) has revolutionised multiple facets of the automotive industry, encompassing intelligent manufacturing processes, diagnostic systems, control mechanisms, supply chain operations, customer service platforms, and traffic management solutions. While extensive research exists on the above aspects of AI applications in automotive contexts, there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research. This review introduces a… More >

  • Open Access

    ARTICLE

    Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification

    Jiyang Xu1, Qi Wang1,*, Xin Xiong2, Weidong Min1,3, Jiang Luo4, Di Gai1, Qing Han1,3

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmc.2024.058586 - 06 March 2025

    Abstract The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive… More >

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