An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Qunying Song, Kaige Tan, Per Runeson, Stefan Persson

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

118 Nedladdningar (Pure)

Sammanfattning

Testing of autonomous vehicles involves enormous challenges for the automotive industry. The number of real-world driving scenarios is extremely large, and choosing effective test scenarios is essential, as well as combining simulated and real world testing. We present an industrial workbench of tools and workflows to generate efficient and effective test scenarios for active safety and autonomous driving functions. The workbench is based on existing engineering tools, and helps smoothly integrate simulated testing, with real vehicle parameters and software. We aim to validate the workbench with real cases and further refine the input model parameters and distributions.
Originalspråkengelska
Titel på värdpublikationThe IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)
FörlagIEEE Computer Society
Sidor81-82
DOI
StatusPublished - 2021 aug. 25
EvenemangThe Third IEEE International Conference On Artificial Intelligence Testing
- Virtual confrence organized by Oxford University, Oxford, Storbritannien
Varaktighet: 2021 aug. 232021 aug. 26

Konferens

KonferensThe Third IEEE International Conference On Artificial Intelligence Testing
Land/TerritoriumStorbritannien
OrtOxford
Period2021/08/232021/08/26

Ämnesklassifikation (UKÄ)

  • Programvaruteknik

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