Automatically testing self-driving cars with search-based procedural content generation

Alessio Gambi, Marc Müller, Gordon Fraser

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car software. Our AsFault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that AsFault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing AsFault was not only more efficient, but also caused up to twice as many lane departures.

OriginalspracheEnglisch
TitelISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
Redakteure/-innenDongmei Zhang, Anders Moller
Herausgeber (Verlag)ACM
Seiten273-283
Seitenumfang11
ISBN (elektronisch)9781450362245
DOIs
PublikationsstatusVeröffentlicht - 10 Juli 2019
Extern publiziertJa

Publikationsreihe

NameISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis

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