Testing of autonomous vehicles using surrogate models and stochastic optimization

Halil Beglerovic, Michael Stolz, Martin Horn

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

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.

Originalspracheenglisch
Titel2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten1-6
Seitenumfang6
Band2018-March
ISBN (elektronisch)9781538615256
DOIs
PublikationsstatusVeröffentlicht - 14 Mär 2018
Veranstaltung20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan
Dauer: 16 Okt 201719 Okt 2017

Konferenz

Konferenz20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Land/GebietJapan
OrtYokohama, Kanagawa
Zeitraum16/10/1719/10/17

ASJC Scopus subject areas

  • Fahrzeugbau
  • Maschinenbau
  • Angewandte Informatik

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