Exploring model quality for ACAS X

Dimitra Giannakopoulou, Dennis Guck, Johann Schumann

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)
    470 Downloads (Pure)


    The next generation airborne collision avoidance system, ACAS X, aims to provide robustness through a probabilistic model that represents sources of uncertainty. From this model, dynamic programming produces a look-up table that is used to give advisories to the pilot in real time. The model is not present in the final system and is therefore not included in the standard certification processes. Rather, the model is checked indirectly, by ensuring that ACAS X performs as well as, or better than, the state-of-the-art, TCAS. We claim that to build confidence in such systems, it is important to target model quality directly. We investigate this issue of model quality as part of our research on informing certification standards for autonomy. Using ACAS X as our driving example, we study the relationship between the probabilistic model and the real world, in an attempt to characterize the quality of the model for the purpose of building ACAS X. This paper presents model conformance metrics, their application to ACAS X, and the results that we obtained from our study.
    Original languageEnglish
    Title of host publicationFM 2016: Formal Methods
    Subtitle of host publication21st International Symposium, Limassol, Cyprus, November 9-11, 2016, Proceedings
    Number of pages17
    ISBN (Electronic)978-3-319-48989-6
    ISBN (Print)978-3-319-48988-9
    Publication statusPublished - Nov 2016
    Event21st International Symposium on Formal Methods, FM 2016 - Limassol, Cyprus
    Duration: 9 Nov 201611 Nov 2016
    Conference number: 21

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer, Cham


    Conference21st International Symposium on Formal Methods, FM 2016
    Abbreviated titleFM 2016
    OtherCo-located with Formal Methods Doctoral Symposium 2016


    • IR-104514
    • EWI-27799


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