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Creators/Authors contains: "Galbas, Roland"

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  1. ; (, 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS))
    We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue capturing requirements for controlling criticality for highly autonomous vehicles. We sketch underlying mathematical foundations which allow to derive formal confidence levels that vehicles tested by such a scenario catalogue will maintain the required control of criticality in real traffic matching the probability distributions of key parameters of data recorded in the reference data base employed for this process. 
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    Accepted Manuscript Full Text Available