2019
DOI: 10.48550/arxiv.1910.04443
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Misbehaviour Prediction for Autonomous Driving Systems

Abstract: Deep Neural Networks (DNNs) are the core component of modern autonomous driving systems. To date, it is still unrealistic that a DNN will generalize correctly in all driving conditions. Current testing techniques consist of offline solutions that identify adversarial or corner cases for improving the training phase, and little has been done for enabling online healing of DNN-based vehicles.In this paper, we address the problem of estimating the confidence of DNNs in response to unexpected execution contexts wi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 17 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?