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Learn from experience: probabilistic prediction of perception performance to avoid failure

Abstract:

Despite significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their mistakes can be catastrophic. As such, it is important to characterize the performance of the system and predict when and where it may fail in order to take appropriate action. While similar...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1177/0278364917730603

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More from this funder
Name:
Seventh Framework Programme
Grant:
610603
More from this funder
Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M019918/1
Publisher:
SAGE Publications
Journal:
International Journal of Robotics Research More from this journal
Publication date:
2017-10-01
Acceptance date:
2017-08-17
DOI:
EISSN:
1741-3176
ISSN:
0278-3649
Keywords:
Pubs id:
pubs:820392
UUID:
uuid:0bd0e376-bfd3-4386-a221-5b8e67367fb1
Local pid:
pubs:820392
Source identifiers:
820392
Deposit date:
2018-01-22

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