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Keep off the grass: permissible driving routes from radar with weak audio supervision

Abstract:

Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment. The performance of LiDAR and vision-based perception systems deteriorates significantly if certain environmental factors are present e.g. rain, fog, darkness. Perception systems based on Frequency-Modulated Continuous Wave scanning radar maintain full performance regardless of environmental conditions and with a longer range than alternative sensors. Learning t...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6121-5839
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6782-6657
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
Proceedings of the 2020 IEEE Intelligent Transportation Systems Conference (ITSC) Journal website
Pages:
1-6
Publication date:
2020-12-24
Acceptance date:
2020-05-07
Event title:
IEEE Intelligent Transportation Systems Conference (ITSC)
Event website:
https://www.ieee-itsc2020.org/
Event start date:
2020-09-20T00:00:00Z
Event end date:
2020-09-23T00:00:00Z
DOI:
EISBN:
978-1-7281-4149-7
ISBN:
978-1-7281-4150-3
Language:
English
Keywords:
Pubs id:
1103959
Local pid:
pubs:1103959
Deposit date:
2020-05-11

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