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This is a Quantitative Study study.

October 20, 2025Open Access

Learning collision risk proactively from naturalistic driving data at scale

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GSSM achieves a median time advance of 2.6 seconds for potential collision prevention, enhancing road safety.
Utilizing a diverse dataset of naturalistic driving, GSSM demonstrates an area under the precision-recall curve of 0.9, indicating high accuracy.
The approach incorporates various contextual factors, consistently outperforming existing collision risk identification baselines.
GSSM establishes a scalable foundation for proactive collision risk assessment, supporting traffic safety evaluation and emergency management.

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Implication

This analysis reveals a new data-driven method for assessing collision risk in traffic interactions, suggesting improvements for autonomous driving systems.

Cite This Study

Jiao et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcdc8d54a28a75cf22e5 https://doi.org/10.48550/arxiv.2505.13556

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Implication

This analysis reveals a new data-driven method for assessing collision risk in traffic interactions, suggesting improvements for autonomous driving systems.

Learning collision risk proactively from naturalistic driving data at scale

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Discussion

Implication

Cite This Study

Learning collision risk proactively from naturalistic driving data at scale

Key Points

Discussion

Implication

Cite This Study

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