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Concrete problems for autonomous vehicle safety: Advantages of Bayesian deep learning

Abstract:

Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AV software must consider the ultimate effect of each component's errors. Further, improving safety alone is not sufficient. Passengers must also feel safe to trust and use AV systems. To address such concerns, we investigate three under-explored themes for AV research: safety, interpretability, and ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.24963/ijcai.2017/661

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Publisher:
International Joint Conferences on Artificial Intelligence Organization Publisher's website
Journal:
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Journal website
Pages:
4745-4753
Host title:
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, (IJCAI-17)
Publication date:
2017-07-28
Acceptance date:
2017-04-23
DOI:
ISSN:
1045-0823
Source identifiers:
746869
ISBN:
9780999241103
Pubs id:
pubs:746869
UUID:
uuid:06eb02bf-0071-4ab5-b5b1-7830ab1ba345
Local pid:
pubs:746869
Deposit date:
2018-02-28

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