Critical scenario identification for realistic testing of autonomous driving systems

Qunying Song, Kaige Tan, Per Runeson, Stefan Persson

Research output: Contribution to journalArticlepeer-review

Abstract

Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial real-world testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical. Thus, critical ones have to be prioritized. We have developed an approach for critical test scenario identification and in this study, we implement the approach and validate it on two real autonomous driving systems from industry by integrating it into their tool-chain. Our main contribution in this work is the demonstration and validation of our approach for critical scenario identification for testing real autonomous driving systems.

Original languageEnglish
Pages (from-to)441-469
JournalSoftware Quality Journal
Volume31
Issue number2
Early online date2022 Dec 3
DOIs
Publication statusPublished - 2023

Subject classification (UKÄ)

  • Computer Systems
  • Software Engineering

Free keywords

  • Autonomous driving
  • Critical scenario identification
  • Software testing
  • Test scenario generation

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