An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software
(2021) The Third IEEE International Conference On Artificial Intelligence Testingp.81-82
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7f8920a2-3620-4155-adf3-aca7a0b60094
- author
- Song, Qunying LU ; Tan, Kaige ; Runeson, Per LU and Persson, Stefan
- organization
- publishing date
- 2021-08-25
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)
- pages
- 81 - 82
- publisher
- IEEE Computer Society
- conference name
- The Third IEEE International Conference On Artificial Intelligence Testing<br/>
- conference location
- Oxford, United Kingdom
- conference dates
- 2021-08-23 - 2021-08-26
- external identifiers
-
- scopus:85118774820
- DOI
- 10.1109/AITEST52744.2021.00024
- project
- Software testing of autonomous systems
- WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
- language
- English
- LU publication?
- yes
- id
- 7f8920a2-3620-4155-adf3-aca7a0b60094
- date added to LUP
- 2021-09-15 15:11:44
- date last changed
- 2022-05-05 03:41:56
@inproceedings{7f8920a2-3620-4155-adf3-aca7a0b60094, 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 = {{Song, Qunying and Tan, Kaige and Runeson, Per and Persson, Stefan}}, booktitle = {{The IEEE Third International Conference on Artificial Intelligence Testing (AITest 2021)}}, language = {{eng}}, month = {{08}}, pages = {{81--82}}, publisher = {{IEEE Computer Society}}, title = {{An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software}}, url = {{https://lup.lub.lu.se/search/files/102481798/AITest21_Workbench.pdf}}, doi = {{10.1109/AITEST52744.2021.00024}}, year = {{2021}}, }