Critical scenario identification for realistic testing of autonomous driving systems

Qunying Song, Kaige Tan, Per Runeson, Stefan Persson

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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.

Originalspråkengelska
Sidor (från-till)441-469
TidskriftSoftware Quality Journal
Volym31
Nummer2
Tidigt onlinedatum2022 dec. 3
DOI
StatusPublished - 2023

Bibliografisk information

Publisher Copyright:
© 2022, The Author(s).

Ämnesklassifikation (UKÄ)

  • Datorsystem
  • Programvaruteknik

Fingeravtryck

Utforska forskningsämnen för ”Critical scenario identification for realistic testing of autonomous driving systems”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här