Enhancing Robustness in Multimodal Traffic Accident Prediction Under Incomplete Modalities and Multi-Level Spatiotemporal Modeling

Engineering notes

Experiments conducted on the large-scale CAP-DATA dataset, which contains over 11,727 traffic scenarios and multimodal sensor records, demonstrate that EAR-MLSR-Net achieves an AUC of 0.860, an AP of 0.764, and a TTA0.5 of 4.252 s. Compared with the strongest baseline, the proposed method achieves a 1.40% improvement in AUC and a 2.10% improvement in AP, while reducing performance degradation by 0.012 under missing-modality conditions.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Global public safety is increasingly threatened by frequent road traffic accidents, making early accident risk prediction essential for intelligent transportation systems. However, incomplete multimodal data and insufficient spatiotemporal modeling lead to unstable performance in real-world scenarios with sensor failure, data loss, or visual occlusion. To address these issues, we propose the Early Accident Risk Multi-Level Spatiotemporal Robustness Network (EAR-MLSR-Net). Unlike conventional GCN-LSTM or Transformer-based architectures that separately model spatial and temporal dependencies, EAR-MLSR-Net introduces a multi-level spatiotemporal learning paradigm, which jointly models local spatial interactions, regional propagation patterns, and global temporal evolution in a unified hierarchical framework, enabling more structured dependency learning across scales. The proposed EAR-MLSR-Net follows a hierarchical multi-level spatiotemporal learning paradigm that integrates spatial reasoning, cross-modal temporal alignment, and multi-scale temporal dynamics modeling into a unified framework. Experiments conducted on the large-scale CAP-DATA dataset, which contains over 11,727 traffic scenarios and multimodal sensor records, demonstrate that EAR-MLSR-Net achieves an AUC of 0.860, an AP of 0.764, and a TTA0.5 of 4.252 s. Compared with the strongest baseline, the proposed method achieves a 1.40% improvement in AUC and a 2.10% improvement in AP, while reducing performance degradation by 0.012 under missing-modality conditions.

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