XILS Credibility Assessment and Scenario Representativeness Methodology Based on Geometric Similarity Analysis for Autonomous Driving Systems
- DOI
- 10.3390/app15126545
- Published
- 2025-06-10
- Container
- Applied Sciences
- Publisher
- MDPI AG
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.3390/app15126545,
title = {XILS Credibility Assessment and Scenario Representativeness Methodology Based on Geometric Similarity Analysis for Autonomous Driving Systems},
author = {Seungjae Han and Taeyoung Oh and Soohyeon Lee and Siyeong Park and Jinwoo Yoo},
year = {2025},
journal = {Applied Sciences},
doi = {10.3390/app15126545},
url = {https://doi.org/10.3390/app15126545}
}RIS
TY - JOUR TI - XILS Credibility Assessment and Scenario Representativeness Methodology Based on Geometric Similarity Analysis for Autonomous Driving Systems AU - Seungjae Han AU - Taeyoung Oh AU - Soohyeon Lee AU - Siyeong Park AU - Jinwoo Yoo PY - 2025 JO - Applied Sciences DO - 10.3390/app15126545 UR - https://doi.org/10.3390/app15126545 ER -
APA
Han, S., Oh, T., Lee, S., Park, S., & Yoo, J. (2025). XILS Credibility Assessment and Scenario Representativeness Methodology Based on Geometric Similarity Analysis for Autonomous Driving Systems. Applied Sciences. https://doi.org/10.3390/app15126545
Source records
- crossref · retrieved 2026-11-10T07:41:20.520Z