Testing of autonomous vehicles using surrogate models and stochastic optimization

Halil Beglerovic, Michael Stolz, Martin Horn

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

Abstract

Advancement in testing and verification methodologies is one of the key requirements for the commercialization and standardization of autonomous driving. Even though great progress has been made, the main challenges encountered during testing of autonomous vehicles, e.g., high number of test scenarios, huge parameter space and long simulation runs, still remain. In order to reduce current testing efforts, we propose an innovative method based on surrogate models in combination with stochastic optimization. The approach presents an iterative zooming-in algorithm aiming to minimize a given cost function and to identify faulty behavior regions within the parameter space. The surrogate model is updated in each iteration and is further used for intensive evaluation tasks, such as exploration and optimization.

Original languageEnglish
Title of host publication2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
Volume2018-March
ISBN (Electronic)9781538615256
DOIs
Publication statusPublished - 14 Mar 2018
Event20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan
Duration: 16 Oct 201719 Oct 2017

Conference

Conference20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
CountryJapan
CityYokohama, Kanagawa
Period16/10/1719/10/17

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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