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...
Saved in:
| Main Authors: | Yahui Wang, Zhoushuo Liang, Yue He, Jiahao Wu, Pengfei Tian, Zhicheng Ling |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0329107 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features
by: Yahui Wang, et al.
Published: (2025-01-01) -
Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction
by: Yanzhan Chen, et al.
Published: (2024-12-01) -
A Two-Stage Sequential Framework for Traffic Accident Post-Impact Prediction Utilizing Real-Time Traffic, Weather, and Accident Data
by: Amirhossein Abdi, et al.
Published: (2023-01-01) -
An Adaptable Framework for Identifying and Prioritising Road Traffic Accident Hotspots
by: Kaliprasana MUDULI, et al.
Published: (2025-03-01) -
ACCIDENTS IN ROAD TRAFFIC. RESEARCHES OF ROAD ACCIDENTS BY MEANS OF INSURANCE STATISTICS
by: D. V. Kapsky
Published: (2011-02-01)