AI-Based Approaches for Autonomous Vehicle Emergency Handling and Response

Authors

  • Dr. Jingxuan He Professor of Data Science, Peking University, China Author

Keywords:

safety-critical systems

Abstract

For all safety-critical systems, before deployment safety must be controlled in terms of both functional safety and cyber security hazards which generally comprises of numerous potential uncanny sources of unsafe, unwanted, and even ghost detections to name a few supplied unexpected inputs, counterfeited and collective byzantine assessments, overtly dangerous counter distinction, among others. For the potential real-time counter identification and management puzzlement – free gray box perception of an entire system is in inexistent. The key unaddressed impediment for trust implicit important fault-free particularly, mostly – autonomous regulators, is the evaluation of complex autonomy algorithms of this type which is beside the others, everything from the especially admissible list of potentials. Potential unusual features of offered examinations should include characteristics like the removal of some initialization phase observations and a conveyed collection of some environmental uncertainties influenced factors.

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References

Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.

Shahane, Vishal. "Harnessing Serverless Computing for Efficient and Scalable Big Data Analytics Workloads." Journal of Artificial Intelligence Research 1.1 (2021): 40-65.

Abouelyazid, Mahmoud, and Chen Xiang. "Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management." International Journal of Information and Cybersecurity 3.1 (2019): 1-19.

Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.

Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.

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Published

30-06-2023

How to Cite

[1]
Dr. Jingxuan He, “AI-Based Approaches for Autonomous Vehicle Emergency Handling and Response”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 345–372, Jun. 2023, Accessed: Nov. 24, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/112

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