Sentence-resampled BERT-CRF model for autonomous vehicle crash causality analysis from large-scale accident narrative text data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new model to analyze autonomous vehicle (AV) crash causes from text narratives, identifying rear-end and lane-change collisions as high-risk scenarios due to human-computer interaction and perception issues.
Area Of Science
- Traffic Safety
- Artificial Intelligence
- Natural Language Processing
Background
- Autonomous vehicle (AV) safety is paramount, yet crash causality analysis often overlooks unstructured narrative data.
- Existing methods struggle with data imbalance and small sample sizes inherent in crash narratives.
Purpose Of The Study
- To develop a novel framework for analyzing AV crash causality using unstructured crash narratives.
- To identify high-risk crash scenarios and their underlying causal factors.
Main Methods
- A Sentence-resampled BERT-CRF model was developed to extract causal movement chains (CMC) from narratives.
- A DREAM-inspired hierarchical causal attribution framework was employed for systematic analysis.
- Data imbalance was addressed using sentence-level resampling.
Main Results
- The model achieved 98.03% accuracy on complete data and 96.14% on 10% of the data.
- Rear-end (48.57%) and lane-change (17.04%) collisions were identified as high-risk scenarios.
- Causal factors include conventional vehicle (CV) proximity, AV decision-making, and intent recognition delays.
Conclusions
- The proposed framework effectively bridges unstructured data and causal inference for AV safety.
- Key causal factors involve human-computer interaction, environmental perception, and roadway infrastructure.
- Findings support AV manufacturers in optimizing algorithms and authorities in developing regulations.
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