An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Qunying Song, Kaige Tan, Per Runeson, Stefan Persson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationThe IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)
PublisherIEEE Computer Society
Pages81-82
DOIs
Publication statusPublished - 2021 Aug 25
EventThe Third IEEE International Conference On Artificial Intelligence Testing
- Virtual confrence organized by Oxford University, Oxford, United Kingdom
Duration: 2021 Aug 232021 Aug 26

Conference

ConferenceThe Third IEEE International Conference On Artificial Intelligence Testing
Country/TerritoryUnited Kingdom
CityOxford
Period2021/08/232021/08/26

Subject classification (UKÄ)

  • Software Engineering

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