Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Felix Batsch, Alireza Daneshkhah, Madeline Cheah, Stratis Kanarachos, Anthony Baxendale

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

    27 Citations (Scopus)
    84 Downloads (Pure)

    Abstract

    Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.
    Original languageEnglish
    Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
    PublisherIEEE
    Pages419-424
    Number of pages6
    ISBN (Electronic)978-1-5386-7024-8
    ISBN (Print)978-1-5386-7025-5
    DOIs
    Publication statusPublished - 2019
    Event22nd IEEE International Conference on Intelligent Transportation Systems - Auckland, New Zealand
    Duration: 27 Oct 201930 Oct 2019

    Conference

    Conference22nd IEEE International Conference on Intelligent Transportation Systems
    Abbreviated titleITSC 2019
    Country/TerritoryNew Zealand
    CityAuckland
    Period27/10/1930/10/19

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Management Science and Operations Research
    • Instrumentation
    • Transportation

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