Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing Article Swipe
Jingwei Ge
,
Huile Xu
,
Jiawei Zhang
,
Yi Zhang
,
Danya Yao
,
Li Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8655514
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8655514
Virtual simulation-based testing of autonomous vehicles (AVs) needs massive challenging corner cases to reach high testing accuracy. Current methods achieve this goal by finding testing scenarios with low sampling frequency in the empirical distribution. However, these methods neglect modeling heterogeneous driving behavior, which actually is crucial for finding corner cases. To fill this gap, we propose an interpretable and operable method for sampling corner cases. Firstly, we initialize a testing scenario and allocate testing tasks to AV. Then, to simulate the variability in driving behaviors, we design utility functions with several hyperparameters and generate aggressive, conservative, and normal driving strategies by adjusting hyperparameters. By changing the heterogeneous driving behavior of surrounding vehicles (SVs), we can sample the challenging corner cases in the scenario. Finally, we conduct a series of simulation experiments in a typical lane-changing scenario. The simulation results reveal that by adjusting the occurrence frequency of heterogeneous SVs in the testing scenario, more corner cases can be found in limited rounds of simulations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/8655514
- https://downloads.hindawi.com/journals/jat/2022/8655514.pdf
- OA Status
- gold
- Cited By
- 22
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280531543
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- OpenAlex ID
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https://openalex.org/W4280531543Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/8655514Digital Object Identifier
- Title
-
Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles TestingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-11Full publication date if available
- Authors
-
Jingwei Ge, Huile Xu, Jiawei Zhang, Yi Zhang, Danya Yao, Li LiList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/8655514Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/jat/2022/8655514.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/jat/2022/8655514.pdfDirect OA link when available
- Concepts
-
Hyperparameter, Computer science, Sampling (signal processing), Test case, Sample (material), Simulation, Driving simulator, Scenario testing, Machine learning, Artificial intelligence, Computer vision, Filter (signal processing), Chromatography, Regression analysis, Chemistry, Variety (cybernetics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 11, 2023: 7, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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