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ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity
- Researchers introduce ST-GraphNet, a spatio-temporal graph neural network framework for understanding and predicting automated vehicle crash severity.
- ST-GraphNet utilizes fine-grained and region-aggregated spatial graphs constructed from real-world AV-related crash reports from Texas.
- The framework employs multimodal data enriched with semantic, spatial, and temporal attributes, including textual embeddings from crash narratives.
- ST-GraphNet achieves a test accuracy of 97.74% using a Dynamic Spatio-Temporal GCN on a coarse-grained spatial graph, demonstrating superior performance compared to fine-grained models.
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