Issue
Security and Safety
Volume 5, 2026
Security and Safety in Intelligent Connected Vehicle
Article Number 2026006
Number of page(s) 23
Section Intelligent Transportation
DOI https://doi.org/10.1051/sands/2026006
Published online 22 April 2026
  1. Gill SS, Wu H, Patros P, et al. Modern computing: Vision and challenges. Telemat Inform Rep 2024; 13: 100116. [Google Scholar]
  2. Yang K, Li J, Liu M, et al. Complex systems and network science: A survey. J Syst Eng Electron 2023; 34: 543–573. [Google Scholar]
  3. Loyola-Gonzalez O. Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE Access 2019; 7: 154096–154113. [CrossRef] [Google Scholar]
  4. Hassija V, Chamola V, Mahapatra A, et al. Interpreting black-box models: A review on explainable artificial intelligence. Cogn Comput 2024; 16: 45–74. [Google Scholar]
  5. Antony MM and Whenish R. Advanced driver assistance systems (ADAS). In: Automotive Embedded Systems: Key Technologies, Innovations, and Applications. Cham: Springer International Publishing, 2021. [Google Scholar]
  6. Nidamanuri J, Nibhanupudi C, Assfalg R, et al. A progressive review: Emerging technologies for ADAS driven solutions. IEEE Trans Intell Veh 2021; 7: 326–341. [Google Scholar]
  7. Alaqail H and Ahmed S. Overview of software testing standard ISO/IEC/IEEE 29119. Int J Comput Sci Netw Secur (IJCSNS) 2018; 18: 112–116. [Google Scholar]
  8. Kertusha I, Assress G, Duman O, et al. A survey on web testing: On the rise of AI and applications in industry. arXiv preprint arXiv:https://arxiv.org/abs/2503.05378, 2025. [Google Scholar]
  9. Mehmood MA, Mahmood A, Khan MNA, et al. A scenario-based distributed testing model for software applications. Int J Adv Appl Sci 2016; 3: 64–71. [Google Scholar]
  10. Wu J. Cyberspace Mimic Defense: Generalized Robust Control and Endogenous Security. Springer Nature, 2021. [Google Scholar]
  11. Wu J. Cyber Resilience System Engineering Empowered by Endogenous Security and Safety. Springer, 2024. [Google Scholar]
  12. Li Y, Liu Q, Zhuang W, et al. Dynamic heterogeneous redundancy-based joint safety and security for connected automated vehicles: Preliminary simulation and field test results. IEEE Veh Technol Mag 2023; 18: 89–97. [Google Scholar]
  13. Han Z, Yu W, Hao L, et al. Intelligent dynamic heterogeneous redundancy architecture for IoT systems. China Commun 2024; 21: 291–306. [Google Scholar]
  14. Liu Q, Wang Z, Wang P, et al. Dynamic heterogeneous redundancy-based endogenous security and safety for connected automated vehicles: Preliminary test results and assessment. Secur Saf 2026; 5: 2025009. [Google Scholar]
  15. Wang Z, Jiang D, and Lv Z. AI-assisted trustworthy architecture for industrial IoT based on dynamic heterogeneous redundancy. IEEE Trans Ind Inform 2022; 19: 2019–2027. [Google Scholar]
  16. Shao S, Ji Y, Zhang W, et al. A DHR executor selection algorithm based on historical credibility and dissimilarity clustering. Sci China Inf Sci 2023; 66: 212304. [Google Scholar]
  17. Wu JX. Problems and solutions regarding generalized functional safety in cyberspace. Secur Saf 2022; 1: 2022001. [Google Scholar]
  18. Zhang F, Chen X, Huang W, et al. Harnessing dynamic heterogeneous redundancy to empower deep learning safety and security. Secur Saf 2024; 3: 2024011. [Google Scholar]
  19. Okada S, Ohzeki M and Taguchi S. Efficient partition of integer optimization problems with one-hot encoding. Sci Rep 2019; 9: 13036. [Google Scholar]
  20. Rodríguez P, Bautista MA, Gonzalez J, et al. Beyond one-hot encoding: Lower dimensional target embedding. Image Vis Comput 2018; 75: 21–31. [Google Scholar]
  21. Henderi H, Wahyuningsih T and Rahwanto E. Comparison of min-max normalization and Z-score normalization in the K-nearest neighbor (kNN) algorithm to test the accuracy of types of breast cancer. Int J Inform Inf Syst 2021; 4: 13–20. [Google Scholar]
  22. Fei N, Gao Y, Lu Z, et al. Z-score normalization, hubness, and few-shot learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, 142–151. [Google Scholar]
  23. Ebert C, Cain J, Antoniol G, et al. Cyclomatic complexity. IEEE Softw 2016; 33: 27–29. [Google Scholar]

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