Special Issues
Table of Content

Intelligent Transportation System (ITS) Safety and Security

Submission Deadline: 01 July 2026 View: 1170 Submit to Special Issue

Guest Editor(s)

Prof. Pengcheng Wang

Email: pcwang@buaa.edu.cn

Affiliation: School of Cyber Science and Technology, Beihang University, Beijing, 100191, China

Homepage:

Research Interests: Intelligent Transportation System, Internet of Vehicle, Cybersecurity on vehicles, Autonomous vehicle

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Prof. Yang Yang

Email: bjtuyang@bjtu.edu.cn

Affiliation: Beijing Jiaotong University, Beijing, China

Homepage:

Research Interests: Road Traffic Safety, Traffic Big Data, ITS Theory and Technology, Connected Autonomous Driving

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Prof. Yaodong Wang

Email: ydwang@bjtu.edu.cn

Affiliation: Beijing Jiaotong University, Beijing, China

Homepage:

Research Interests: Intelligent Inspection Technologies for Rail Transit, Machine Vision and Multi-Dimensional Perception, High-Speed Robotic Vision for Intelligent Inspection

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Summary

With the rapid development of autonomous driving and intelligent transportation systems, ensuring the safety, reliability, and cyber security of intelligent transportation has become increasingly critical. The integration of multi-sensor perception, AI-driven decision-making, and connected infrastructure further highlights the urgency of addressing system robustness and information security, especially in both road and rail domains.


This Special Issue aims to gather recent advances in intelligent transportation systems, with a particular focus on autonomous driving, intelligent rail systems, and security mechanisms of AI-enabled platforms. It will highlight innovative methods in multi-dimensional sensing, system reliability, intelligent inspection, and information security for both road and rail applications. The issue welcomes studies on system vulnerabilities, secure communication, vision-based intelligent inspection, and security-enhanced perception models, ultimately providing technical support for safer and more trustworthy intelligent transportation.


The following subtopics are the particular interests of this Special Issues, including but not limited to:
· Intelligent inspection and perception technologies for road and rail transportation
· Information security and confidentiality mechanisms in intelligent vehicles and intelligent rail systems
· Robust perception, sensor fusion, and multi-dimensional sensing for autonomous driving
· Secure AI models, adversarial robustness, and trustworthy intelligent systems
· Intelligent rail transit monitoring, prediction, and fault-detection technologies
· Safety assurance, reliability analysis, and risk evaluation for intelligent transportation
· Vision-based high-speed detection and monitoring for autonomous and rail systems


Keywords

Autonomous Driving, Intelligent Transportation, Intelligent Rail Systems, Intelligent Inspection, Information Security, Rail Information Security, Multi-sensor Perception, Traffic Safety, Road–rail Safety Optimization.

Published Papers


  • Open Access

    ARTICLE

    Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System

    Pangwei Wang, Jie Wang, Zipeng Wang, Hangrui Dong, Li Wang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080815
    (This article belongs to the Special Issue: Intelligent Transportation System (ITS) Safety and Security)
    Abstract Traffic holographic perception refers to the real-time, high-fidelity, and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors, including cameras, radars, and connected vehicle data. The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System (ITS). However, sensors are vulnerable to environmental interference, which can result in data loss at specific points or along arterial highways for certain periods, potentially undermining system safety and decision-making reliability. To address these challenges, a deep learning method based on Graph Convolutional Networks (GCN) and Gated… More >

  • Open Access

    ARTICLE

    Task-Specific YOLO Optimization for Railway Tunnel Cracks and Water Leakage: Benchmarking and Lightweight Enhancement

    Yang Lei, Kangshuo Zhu, Bo Jiang, Yaodong Wang, Feiyu Jia, Zhaoning Wang, Falin Qi, Qiming Qu
    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077314
    (This article belongs to the Special Issue: Intelligent Transportation System (ITS) Safety and Security)
    Abstract The safe operation of railway systems necessitates efficient and automated inspection of tunnel defects. While deep learning offers solutions, a clear pathway for selecting and optimizing the latest object detectors for distinct defects under strict speed constraints is lacking. This paper presents a two-stage, task-specific framework for high-speed tunnel defect detection. First, this study conducts a comprehensive comparative analysis of state-of-the-art YOLO models (YOLOv5s, YOLOv8s, YOLOv10s, YOLOv11s) on self-constructed datasets. This systematic comparison identifies YOLOv5s as the optimal model for crack detection, achieving an mAP@0.5 of 0.939 at 77.5 FPS, sufficient for inspection at 50… More >

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