Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles

Submission Deadline: 31 March 2026 View: 3375 Submit to Special Issue

Guest Editors

Assoc. Prof. Shih-Lin Lin

Email: lin040@cc.ncue.edu.tw

Affiliation: Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua City 50007, Taiwan

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Research Interests: autonomous driving, V2X communication, cybersecurity, digital twin, next-generation vehicle technologies

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Summary

The global automotive industry is undergoing a paradigm shift as electric vehicles (EVs) evolve into intelligent, connected systems. This transformation is driven by advancements in artificial intelligence (AI), the Internet of Things (IoT), edge/cloud computing, and cybersecurity, which are redefining vehicle autonomy, connectivity, and user experience. This Special Issue focuses on the computational and algorithmic innovations powering next-generation smart vehicles, emphasizing software-defined mobility, data-driven intelligence, and secure networked systems.

This Special Issue aims to seek cutting-edge research at the intersection of computer science and intelligent vehicle technologies, covering theoretical advances, algorithmic development, and real-world implementations. We welcome contributions that address computational challenges in autonomous systems, networked vehicle ecosystems, AI-driven decision-making, and secure software architectures for EVs. Submissions with interdisciplinary impact and scalable solutions are encouraged.

Suggested Themes:
·AI-Driven Perception and Control for Autonomous Driving
·Smart Mobility and IoT Integration
·Intelligent Cabin Systems, HMI, and In-Vehicle Edge Computing
·V2X Communications, 5G/6G Integration, and Smart Mobility Services
·Cybersecurity, Functional Safety, and Secure OTA Updates
·Digital Twin, Big-Data Analytics, and Predictive Maintenance for Vehicle Fleets
·Software-Defined Vehicle (SDV) Architectures

Keywords

Autonomous Vehicles; AI/ML for Automotive Systems; Edge Computing in EVs; V2X Communication; Cybersecurity for Connected Vehicles; Software-Defined Vehicles; Digital Twin Technology; Federated Learning; Human-Machine Interaction (HMI); 5G/6G Vehicle Networks

Published Papers

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Open Access

ARTICLE

Research on Integrating Deep Learning-Based Vehicle Brand and Model Recognition into a Police Intelligence Analysis Platform
Shih-Lin Lin, Cheng-Wei Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071915
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >

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41
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ARTICLE

Deep Multi-Agent Stochastic Optimization for Traffic Management in IoT-Enabled Transportation Networks
Nada Alasbali
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4943-4958, 2025, DOI:10.32604/cmc.2025.068330
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract Intelligent Traffic Management (ITM) has progressively developed into a critical component of modern transportation networks, significantly enhancing traffic flow and reducing congestion in urban environments. This research proposes an enhanced framework that leverages Deep Q-Learning (DQL), Game Theory (GT), and Stochastic Optimization (SO) to tackle the complex dynamics in transportation networks. The DQL component utilizes the distribution of traffic conditions for epsilon-greedy policy formulation and action and choice reward calculation, ensuring resilient decision-making. GT models the interaction between vehicles and intersections through probabilistic distributions of various features to enhance performance. Results demonstrate that the proposed More >

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118
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ARTICLE

Advanced Multi-Channel Echo Separation Techniques for High-Interference Automotive Radars
Shih-Lin Lin
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1365-1382, 2025, DOI:10.32604/cmc.2025.067764
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave (FMCW) automotive radar performance under high noise and interference. The four-stage pipeline is applied consecutively: (i) an improved independent component analysis (ICA) blindly separates the two-channel echoes, isolating target and interference components; (ii) a recursive least-squares (RLS) filter compensates amplitude- and phase-mismatches, restoring signal fidelity; (iii) variational mode decomposition (VMD) followed by the Hilbert-Huang Transform (HHT) extracts noise-free intrinsic mode functions (IMFs) and sharpens their time-frequency signatures; and (iv) HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information. Finally, More >

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2027
Open Access

REVIEW

A Review of AI-Driven Automation Technologies: Latest Taxonomies, Existing Challenges, and Future Prospects
Weiqiang Jin, Ningwei Wang, Lei Zhang, Xingwu Tian, Bohang Shi, Biao Zhao
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 3961-4018, 2025, DOI:10.32604/cmc.2025.067857
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract With the growing adoption of Artifical Intelligence (AI), AI-driven autonomous techniques and automation systems have seen widespread applications, become pivotal in enhancing operational efficiency and task automation across various aspects of human living. Over the past decade, AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures. These technologies not only increase productivity but also enable more scalable and adaptable solutions, proving particularly beneficial in industries such as healthcare, finance, and customer service. However, the absence of a unified review for categorization, benchmarking, and ethical risk assessment hinders the AI-driven automation progress.… More >

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Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles

Guest Editors

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Published Papers

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366

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118

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2027

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1154

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