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An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Song, Qunying LU orcid ; Tan, Kaige ; Runeson, Per LU orcid and Persson, Stefan (2021) The Third IEEE International Conference On Artificial Intelligence Testing
 p.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:
author
; ; and
organization
publishing date
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}},
}