Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Ethical Considerations in Deploying IoT Sensors for Anomaly Detection in Autonomous Vehicles

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  • Dr. João Madeira
Dr. João Madeira
Professor of Informatics, Instituto Superior Técnico (IST), Portugal
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Published 02-06-2023

Keywords

  • IoT,
  • Learning Methods
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. João Madeira, “Ethical Considerations in Deploying IoT Sensors for Anomaly Detection in Autonomous Vehicles”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–13, Jun. 2023, Accessed: Nov. 25, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/31

Abstract

In an effort to detect as many anomalies as possible, the transportation industry uses all kinds of sensors on different forms of transportation. Various types of sensors, ranging from simple and inexpensive wear sensors to the more expensive and complex temperature, smoke, fire, toxic gas, and liquid and chemical sensors, are used to monitor the health condition of the vehicle. To expand the detection of anomalies, deep learning methods have been successfully employed in an unsupervised fashion to analyze sensing data. These learned models will prompt the human or system operator about a problem in the vehicle operation. While current IoT sensors promise to provide never-ending streams of information regarding the health and status of vehicle transportation systems, caution needs to be taken to not interfere personally with the traveling passengers or the vehicle operator.

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References

  1. M. Hasan and S. Arifin, "Deep learning-based anomaly detection in autonomous vehicles: A survey," 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 2019, pp. 2606-2611.
  2. Y. Wang, S. S. Iyengar and Y. Qi, "Deep reinforcement learning for autonomous vehicle control in presence of sensor failures," 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Pittsburgh, PA, USA, 2019, pp. 1-6.
  3. D. Zhao, C. Wang, D. Li, S. Ma and Z. Sun, "Anomaly detection in smart vehicles: A deep learning approach," 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Zhangjiajie, China, 2019, pp. 537-544.
  4. S. Sun, J. Li, L. Jiao, S. Zhou and L. Zheng, "A deep learning approach to anomaly detection in intelligent transportation systems," 2017 IEEE International Conference on Smart Computing (SMARTCOMP), Hong Kong, China, 2017, pp. 1-8.
  5. A. Manzano, J. Garcia-Fernandez and C. Laorden, "Deep learning for anomaly detection in intelligent transportation systems," 2017 IEEE International Conference on Smart Computing (SMARTCOMP), Hong Kong, China, 2017, pp. 1-8.
  6. Y. Lu, W. Zheng, M. Tang and J. Dong, "A deep learning-based approach for anomaly detection in autonomous vehicles," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, 2018, pp. 1427-1431.
  7. S. R. Vemula, S. V. Uppalapati and K. M. Reddy, "Deep learning-based anomaly detection in autonomous vehicles using sensor data," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, 2018, pp. 1414-1418.
  8. A. K. Mishra, D. Bansal and S. Kumar, "Anomaly detection in autonomous vehicles using deep learning," 2018 IEEE 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, 2018, pp. 192-196.
  9. W. Yang, Z. Zhang, Y. Li and D. Zhang, "Anomaly detection in autonomous vehicles based on deep learning," 2018 IEEE International Conference on Intelligent Vehicles Symposium (IV), Changshu, China, 2018, pp. 616-621.
  10. Y. Kim, B. Choi, J. Yoo and J. Kim, "Deep learning-based anomaly detection for autonomous vehicles," 2018 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2018, pp. 1-2.
  11. M. A. Malik, M. Rehman and S. S. Rizvi, "Anomaly detection in autonomous vehicles using deep learning," 2019 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), Karachi, Pakistan, 2019, pp. 1-5.
  12. Tatineni, Sumanth. "Cloud-Based Business Continuity and Disaster Recovery Strategies." International Research Journal of Modernization in Engineering, Technology, and Science5.11 (2023): 1389-1397.
  13. Vemori, Vamsi. "From Tactile Buttons to Digital Orchestration: A Paradigm Shift in Vehicle Control with Smartphone Integration and Smart UI–Unveiling Cybersecurity Vulnerabilities and Fortifying Autonomous Vehicles with Adaptive Learning Intrusion Detection Systems." African Journal of Artificial Intelligence and Sustainable Development3.1 (2023): 54-91.
  14. Shaik, Mahammad, Leeladhar Gudala, and Ashok Kumar Reddy Sadhu. "Leveraging Artificial Intelligence for Enhanced Identity and Access Management within Zero Trust Security Architectures: A Focus on User Behavior Analytics and Adaptive Authentication." Australian Journal of Machine Learning Research & Applications 3.2 (2023): 1-31.
  15. Tatineni, Sumanth. "Security and Compliance in Parallel Computing Cloud Services." International Journal of Science and Research (IJSR) 12.10 (2023): 972-1977.
  16. G. Wu, Z. Zhu, W. Zhang and S. Zhao, "Deep learning-based anomaly detection for autonomous vehicles," 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019, pp. 297-302.
  17. H. Li, C. Ma, Q. Liu and J. Wang, "Deep learning for anomaly detection in autonomous vehicles," 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019, pp. 1-6.
  18. Y. Zhou, L. Sun, H. Zhang, C. Li and Y. Wang, "Anomaly detection in autonomous vehicles using deep learning," 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, 2019, pp. 1664-1669.
  19. J. Xue, S. Zhang, K. Xiong and S. Lin, "Deep learning-based anomaly detection for autonomous vehicles," 2020 IEEE International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, 2020, pp. 75-79.
  20. A. B. Shah, S. K. Patel and H. M. Jethva, "Anomaly detection in autonomous vehicles using deep learning," 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2020, pp. 1-4.
  21. Q. Li, Z. Zhang, Y. Li and Y. Liu, "Deep learning-based anomaly detection for autonomous vehicles," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 4320-4325.
  22. S. Guo, Z. Zhang, L. Zhang, Y. Wang and Y. Zhang, "Anomaly detection in autonomous vehicles using deep learning," 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Chengdu, China, 2021, pp. 467-471.
  23. S. Wang, Z. Wang, H. Liu and J. Wang, "Deep learning-based anomaly detection for autonomous vehicles," 2021 IEEE International Conference on Computational Science and Engineering (CSE), Chengdu, China, 2021, pp. 666-669.

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