Conference item
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
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 790.6KB, Terms of use)
-
- Publisher copy:
- 10.1109/IV55156.2024.10588556
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- 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:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2024
- Rights statement:
- © 2024 IEEE.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available online from IEEE at https://dx.doi.org/10.1109/IV55156.2024.10588556
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