Poster: Sim-ATAV: Simulation-based adversarial testing framework for autonomous vehicles

Cumhur Erkan Tuncali, Georgios Fainekos, Hisahiro Ito, James Kapinski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

One of the main challenges in testing autonomous driving systems is the presence of machine learning components, such as neural networks, for which formal properties are difficult to establish. We present a simulation-based testing framework that supports methods used to evaluate cyberphysical systems, such as test case generation and automatic falsification. We demonstrate how the framework can be used to evaluate closed-loop properties of autonomous driving system models that include machine learning components.

Original languageEnglish (US)
Title of host publicationHSCC 2018 - Proceedings of the 21st International Conference on Hybrid Systems
Subtitle of host publicationComputation and Control (part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Pages283-284
Number of pages2
ISBN (Electronic)9781450356428
DOIs
StatePublished - Apr 11 2018
Event21st International Conference on Hybrid Systems: Computation and Control, HSCC 2018 - Porto, Portugal
Duration: Apr 11 2018Apr 13 2018

Publication series

NameHSCC 2018 - Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)

Other

Other21st International Conference on Hybrid Systems: Computation and Control, HSCC 2018
Country/TerritoryPortugal
CityPorto
Period4/11/184/13/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Networks and Communications

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