AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features.
Accurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and police investigations, which can introduce biases...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0329107 |
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| _version_ | 1849248157660086272 |
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| author | Yahui Wang Zhoushuo Liang Yue He Jiahao Wu Pengfei Tian Zhicheng Ling |
| author_facet | Yahui Wang Zhoushuo Liang Yue He Jiahao Wu Pengfei Tian Zhicheng Ling |
| author_sort | Yahui Wang |
| collection | DOAJ |
| description | Accurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and police investigations, which can introduce biases and lack objectivity. To address these limitations, we propose the AMFormer(Arithmetic Feature Interaction Transformer) framework-a deep learning model designed for robust and interpretable traffic accident responsibility prediction. By capturing complex interactions among key factors through spatiotemporal feature modeling, this framework facilitates precise multi-label classification of accident responsibility. Furthermore, we employ SHAP (SHapley Additive Interpretation) analysis to improve transparency by identifying the most influential features in attribution of responsibility, and provide an in-depth analysis of key features and how they combine to significantly influence attribution of responsibility. Experiments conducted on real-world datasets demonstrate that AMFormer outperforms both other deep learning models and traditional approaches, achieving an accuracy of 93.46% and an F1-Score of 93%. This framework not only enhances the credibility of traffic accident responsibility attribution but also establishes a foundation for future research into autonomous vehicle responsibility. |
| format | Article |
| id | doaj-art-1e97bec4d9124a22ba9922bedf62f77c |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1e97bec4d9124a22ba9922bedf62f77c2025-08-20T03:57:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032910710.1371/journal.pone.0329107AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features.Yahui WangZhoushuo LiangYue HeJiahao WuPengfei TianZhicheng LingAccurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and police investigations, which can introduce biases and lack objectivity. To address these limitations, we propose the AMFormer(Arithmetic Feature Interaction Transformer) framework-a deep learning model designed for robust and interpretable traffic accident responsibility prediction. By capturing complex interactions among key factors through spatiotemporal feature modeling, this framework facilitates precise multi-label classification of accident responsibility. Furthermore, we employ SHAP (SHapley Additive Interpretation) analysis to improve transparency by identifying the most influential features in attribution of responsibility, and provide an in-depth analysis of key features and how they combine to significantly influence attribution of responsibility. Experiments conducted on real-world datasets demonstrate that AMFormer outperforms both other deep learning models and traditional approaches, achieving an accuracy of 93.46% and an F1-Score of 93%. This framework not only enhances the credibility of traffic accident responsibility attribution but also establishes a foundation for future research into autonomous vehicle responsibility.https://doi.org/10.1371/journal.pone.0329107 |
| spellingShingle | Yahui Wang Zhoushuo Liang Yue He Jiahao Wu Pengfei Tian Zhicheng Ling AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features. PLoS ONE |
| title | AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features. |
| title_full | AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features. |
| title_fullStr | AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features. |
| title_full_unstemmed | AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features. |
| title_short | AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features. |
| title_sort | amformer based framework for accident responsibility attribution interpretable analysis with traffic accident features |
| url | https://doi.org/10.1371/journal.pone.0329107 |
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