Abstract

A hierarchical framework is proposed for improving the automatic test case generation process for high-fidelity models with long execution times. The framework incorporates related low-fidelity models for which certain properties can be analytically or computationally evaluated with provable guarantees (e.g., gradients of safety or performance metrics). The low-fidelity models drive the test case generation process for the high-fidelity models. The proposed framework is demonstrated on a model of a vehicle with Full Range Adaptive Cruise Control with Collision Avoidance (FRACC), for which it generates more challenging test cases on average compared to test cases generated using Simulated Annealing.

Original languageEnglish (US)
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages1059-1064
Number of pages6
Volume2017-August
ISBN (Electronic)9781509067800
DOIs
StatePublished - Jan 12 2018
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: Aug 20 2017Aug 23 2017

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
CountryChina
CityXi'an
Period8/20/178/23/17

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Tuncali, C. E., Yaghoubi, S., Pavlic, T., & Fainekos, G. (2018). Functional gradient descent optimization for automatic test case generation for vehicle controllers. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (Vol. 2017-August, pp. 1059-1064). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256245