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Semantic Scholar · Article (Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

Moving From Piloted to Autonomous Operations: Investigating Human Factors Challenges in Urban Air Mobility

Published 2021-09-01 From Proceedings of the Human Factors and Ergonomics Society Annual Meeting 4 authors

Attribution

This is the abstract and citation. Full text lives at Semantic Scholar — we link out rather than host. All credit to the authors and Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

Abstract

Verbatim from Semantic Scholar. Not paraphrased, not summarized.

Urban Air Mobility (UAM) is expected to be the next major revolution in the aviation industry and a solution to the growing traffic congestion on the ground. The long-term goal of UAM is to reach fully autonomous operations with minimal to no human supervision. Transitioning from today’s piloted to fully autonomous operations while maintaining or exceeding the current safety level in commercial aviation will require sig-nificant advances in technology and extensive collaboration between government, industry, and community stakeholders. In this paper, we discuss results from a focus group aimed at capturing the perspectives of five subject matter experts on the challenges associated with transitioning to fully autonomous operations. The focus group revealed five major themes for future research: overall system and automation design, training, system safety and security, community and, regulations and policy. The findings from this research highlight gaps in technology, regulations and our understanding of large-scale complex systems.

Authors

  • Karanvir Panesar
  • A. Mathur
  • E. Atkins
  • N. Sarter

Keywords

  • Engineering
  • Environmental Science

Citation: Karanvir Panesar, A. Mathur, E. Atkins , et al. (2021). Moving From Piloted to Autonomous Operations: Investigating Human Factors Challenges in Urban Air Mobility. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Semantic Scholar ID e19adef37e261f95edeeab514c00138eb8bafe32. https://doi.org/10.1177/1071181321651143 ↗