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Generating Executable Test Scenarios from Autonomous Vehicle Disengagements using Natural Language Processing

Song, Qunying LU orcid ; Anderberg, Rune ; Olsson, Henrik and Runeson, Per LU orcid (2024) p.98-104
Abstract
With the emergence of autonomous vehicles comes requirements on adequate and rigorous testing techniques, particularly as systems continuously adapt to changing environments. Scenario-based, simulated testing is one approach that has received attention, where deriving relevant scenarios from various sources is still a challenge. We therefore explore creating executable test scenarios from textual disengagement reports, collected from autonomous vehicle test drives, by DMV California. We mined information from 183 182 disengagements, using NLP techniques and developed a tool to output scenarios in OpenScenario format. The data quality of the reports was substandard, resulting in only 36 disengagements be useful and half of the generated... (More)
With the emergence of autonomous vehicles comes requirements on adequate and rigorous testing techniques, particularly as systems continuously adapt to changing environments. Scenario-based, simulated testing is one approach that has received attention, where deriving relevant scenarios from various sources is still a challenge. We therefore explore creating executable test scenarios from textual disengagement reports, collected from autonomous vehicle test drives, by DMV California. We mined information from 183 182 disengagements, using NLP techniques and developed a tool to output scenarios in OpenScenario format. The data quality of the reports was substandard, resulting in only 36 disengagements be useful and half of the generated scenarios were correctly reconstructed. However, the NLP approach was effective and may be used for other data sets. Further work includes working with more and better data sources and advancing the scenario generation. (Less)
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
keywords
testing, driving scenarios, scenario generation, autonomous vehicles, disengagement, natural language processing
host publication
SEAMS '24: Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
pages
7 pages
external identifiers
  • scopus:85196380328
DOI
10.1145/3643915.3644098
language
English
LU publication?
yes
id
fe2f8bbb-47d2-4a3f-b7f0-be4e20b6b056
date added to LUP
2024-07-13 16:21:19
date last changed
2024-07-24 12:31:23
@inproceedings{fe2f8bbb-47d2-4a3f-b7f0-be4e20b6b056,
  abstract     = {{With the emergence of autonomous vehicles comes requirements on adequate and rigorous testing techniques, particularly as systems continuously adapt to changing environments. Scenario-based, simulated testing is one approach that has received attention, where deriving relevant scenarios from various sources is still a challenge. We therefore explore creating executable test scenarios from textual disengagement reports, collected from autonomous vehicle test drives, by DMV California. We mined information from 183 182 disengagements, using NLP techniques and developed a tool to output scenarios in OpenScenario format. The data quality of the reports was substandard, resulting in only 36 disengagements be useful and half of the generated scenarios were correctly reconstructed. However, the NLP approach was effective and may be used for other data sets. Further work includes working with more and better data sources and advancing the scenario generation.}},
  author       = {{Song, Qunying and Anderberg, Rune and Olsson, Henrik and Runeson, Per}},
  booktitle    = {{SEAMS '24: Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems}},
  keywords     = {{testing; driving scenarios; scenario generation; autonomous vehicles; disengagement; natural language processing}},
  language     = {{eng}},
  month        = {{06}},
  pages        = {{98--104}},
  title        = {{Generating Executable Test Scenarios from Autonomous Vehicle Disengagements using Natural Language Processing}},
  url          = {{http://dx.doi.org/10.1145/3643915.3644098}},
  doi          = {{10.1145/3643915.3644098}},
  year         = {{2024}},
}