An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Qunying Song; Kaige Tan; Per Runeson; Stefan Persson

doi:10.1109/AITEST52744.2021.00024

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 proceeding › Paper in conference proceeding › peer-review

221 Downloads (Pure)

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 language	English
Title of host publication	The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)
Publisher	IEEE Computer Society
Pages	81-82
DOIs	https://doi.org/10.1109/AITEST52744.2021.00024
Publication status	Published - 2021 Aug 25
Event	The Third IEEE International Conference On Artificial Intelligence Testing - Virtual confrence organized by Oxford University, Oxford, United Kingdom Duration: 2021 Aug 23 → 2021 Aug 26

Conference

Conference	The Third IEEE International Conference On Artificial Intelligence Testing
Country/Territory	United Kingdom
City	Oxford
Period	2021/08/23 → 2021/08/26

Subject classification (UKÄ)

Software Engineering

Access to Document

10.1109/AITEST52744.2021.00024

AITest21 WorkbenchAccepted author manuscript, 93.5 KBLicence: CC BY

1 Finished
1 Active

WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
Årzén, K.-E. (Researcher)
2015/10/01 → 2029/12/31
Project: Research
Software testing of autonomous systems
Song, Q. (Researcher), Runeson, P. (Supervisor) & Engström, E. (Assistant supervisor)
2020/01/01 → 2024/12/31
Project: Dissertation

Cite this

@inproceedings{7f8920a236204155adf3aca7a0b60094,

title = "An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software",

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.",

author = "Qunying Song and Kaige Tan and Per Runeson and Stefan Persson",

year = "2021",

month = aug,

day = "25",

doi = "10.1109/AITEST52744.2021.00024",

language = "English",

pages = "81--82",

booktitle = "The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)",

publisher = "IEEE Computer Society",

address = "United States",

note = "The Third IEEE International Conference On Artificial Intelligence Testing<br/> ; Conference date: 23-08-2021 Through 26-08-2021",

}

Song, Q, Tan, K, Runeson, P & Persson, S 2021, An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software. in The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021). IEEE Computer Society, pp. 81-82, The Third IEEE International Conference On Artificial Intelligence Testing
, Oxford, United Kingdom, 2021/08/23. https://doi.org/10.1109/AITEST52744.2021.00024

An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software. / Song, Qunying; Tan, Kaige; Runeson, Per et al.
The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021). IEEE Computer Society, 2021. p. 81-82.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

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PY - 2021/8/25

Y1 - 2021/8/25

N2 - 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.

AB - 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.

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M3 - Paper in conference proceeding

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BT - The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)

PB - IEEE Computer Society

T2 - The Third IEEE International Conference On Artificial Intelligence Testing<br/>

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ER -

An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Abstract

Conference

Subject classification (UKÄ)

Access to Document

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Projects

WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University

Software testing of autonomous systems

Cite this