Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components

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

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

140 Scopus citations


Many organizations are developing autonomous driving systems, which are expected to be deployed at a large scale in the near future. Despite this, there is a lack of agreement on appropriate methods to test, debug, and certify the performance of these systems. One of the main challenges is that many autonomous driving systems have machine learning (ML) components, such as deep neural networks, for which formal properties are difficult to characterize. We present a testing framework that is compatible with test case generation and automatic falsification methods, which are used to evaluate cyber-physical systems. We demonstrate how the framework can be used to evaluate closed-loop properties of an autonomous driving system model that includes the ML components, all within a virtual environment. We demonstrate how to use test case generation methods, such as covering arrays, as well as requirement falsification methods to automatically identify problematic test scenarios. The resulting framework can be used to increase the reliability of autonomous driving systems.

Original languageEnglish (US)
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538644522
StatePublished - Oct 18 2018
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: Sep 26 2018Sep 30 2018

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Other2018 IEEE Intelligent Vehicles Symposium, IV 2018
CityChangshu, Suzhou

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modeling and Simulation


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