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How well do drivers adapt to remote operation? Learning from remote drivers with on-road experience

Abstract:
Remote driving is a promising strategy for helping Autonomous Vehicles (AVs) navigate many environments where edge cases may otherwise limit their abilities. For some companies, remote driving is an alternative to AVs altogether. Much remote driving research has taken place in simulated or controlled environments with novice operators, leaving the needs of operators with real-world experience under-explored. This research aims to understand if experienced operators are satisfied with current production remote driving systems, if they adapt to the difference in control, and how their job satisfaction compares to in-vehicle safety driving. This paper briefly overviews recent remote driving research and presents results from a questionnaire and a semi-structured interview with experienced teleoperators. The findings indicate that operators do adjust to the new domain, but latency and network reliability remain a challenge. Likewise, standardised training practices for operators are found to be lacking.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IV55156.2024.10588556

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-6088-3955
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author


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Funder identifier:
https://ror.org/0439y7842
Grant:
EP/W011344/1
Programme:
RAILS


Publisher:
IEEE
Host title:
2024 IEEE Intelligent Vehicles Symposium (IV)
Pages:
23-28
Publication date:
2024-07-15
Acceptance date:
2024-03-29
Event title:
35th IEEE Intelligent Vehicles Symposium (IV 2024)
Event location:
Jeju Island, Korea
Event website:
https://ieee-iv.org/2024/
Event start date:
2024-06-02
Event end date:
2024-06-05
DOI:
EISSN:
2642-7214
ISSN:
1931-0587
EISBN:
9798350348811
ISBN:
9798350348828


Language:
English
Keywords:
Pubs id:
1994715
Local pid:
pubs:1994715
Deposit date:
2024-05-05
ARK identifier:

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