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    Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems

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    Accepted manuscript (1.105Mb)
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    Publication date
    2021-08
    Author
    Aslansefat, K.
    Kabir, Sohag
    Abdullatif, Amr R.A.
    Vasudevan, Vinod
    Papadopoulos, Y.
    Keyword
    Autonomous systems
    Safety assurance
    AI safety
    Statistical distance measure
    SafeML
    Safe machine learning
    Rights
    © 2021 IEEE. Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    Green
    
    Metadata
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    Abstract
    This article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-the loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software. The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly. As autonomous vehicles operate in dynamic environments, the risk that they can face an unknown observation is relatively high due to insufficient training data, distributional shift, or cyber-security attack. Thus, AI-based algorithms should make dependable decisions to improve their interpretation of the environment, lower the risk of autonomous driving, and avoid catastrophic accidents. This paper proposes an approach named SafeML II, which applies empirical cumulative distribution function (ECDF)-based statistical distance measures in a designed human-in-the-loop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software. The approach is model-agnostic and it can cover various machine learning and deep learning classifiers. The German Traffic Sign Recognition Benchmark (GTSRB) is used to illustrate the capabilities of the proposed approach.
    URI
    http://hdl.handle.net/10454/18591
    Version
    Accepted manuscript
    Citation
    Aslansefat K, Kabir S, Abdullatif ARA et al (2021) Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems. Computer. 54(8): 66-76.
    Link to publisher’s version
    https://doi.org/10.1109/MC.2021.3075054
    Type
    Article
    Collections
    Engineering and Informatics Publications

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