融合语义、上下文与运动特征，提升事故预测准确率
The Context of Crash Occurrence: A Complexity-Infused Approach Integrating Semantic, Contextual, and Kinematic Features
- 构建两阶段框架，融合多维特征生成道路复杂度特征
- 加入复杂度特征后事故预测准确率达90.15%
- 大模型标注的复杂度指数优于人工标注，适合智能驾驶安全研究
理解复杂驾驶环境中事故发生的背景对提升交通安全和推动自动驾驶发展至关重要。以往研究基于语义、上下文或车辆运动学特征使用统计模型和深度学习预测事故，但未考察这些因素的综合影响。本文将这些特征的整合称为“道路复杂度”。提出一种两阶段框架：第一阶段通过编码器从多维特征中提取隐含上下文信息，生成复杂度增强特征；第二阶段结合原始特征与复杂度增强特征进行事故概率预测，仅用原始特征时准确率为87.98%，加入复杂度特征后达90.15%。消融实验表明，语义、运动学与上下文特征联合使用效果最佳，凸显其在捕捉道路复杂性中的作用。此外，由大语言模型生成的复杂度指数优于亚马逊众包标注，表明AI工具在构建可扩展、高精度事故预测系统方面具有潜力。
原文摘要 · Abstract (English)
Understanding the context of crash occurrence in complex driving environments is essential for improving traffic safety and advancing automated driving. Previous studies have used statistical models and deep learning to predict crashes based on semantic, contextual, or vehicle kinematic features, but none have examined the combined influence of these factors. In this study, we term the integration of these features ``roadway complexity''. This paper introduces a two-stage framework that integrates roadway complexity features for crash prediction. In the first stage, an encoder extracts hidden contextual information from these features, generating complexity-infused features. The second stage uses both original and complexity-infused features to predict crash likelihood, achieving an accuracy of 87.98\% with original features alone and 90.15\% with the added complexity-infused features. Ablation studies confirm that a combination of semantic, kinematic, and contextual features yields the best results, which emphasize their role in capturing roadway complexity. Additionally, complexity index annotations generated by the Large Language Model outperform those by Amazon Mechanical Turk, highlighting the potential of AI-based tools for accurate, scalable crash prediction systems.
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