用深度学习模型精准识别车祸中的自动驾驶等级，助力安全评估与监管决策。
Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes
- 采用MambaAttention等三类表格型深度学习模型，结合德州4649起事故数据进行分类。
- MambaAttention在各类别上表现最优，对高阶自动驾驶的识别准确率达99%。
- 模型可帮助厘清不同自动化级别事故责任，适合政策制定与车辆安全评估场景。
自动驾驶汽车数量增长带来了事故分类与安全分析的新挑战。准确识别每起事故中涉及的SAE自动化等级，对于理解事故机理和明确系统责任至关重要。然而，现有方法常忽视自动化特异性因素，且模型复杂度不足，难以区分不同SAE等级。为此，本研究评估了三种先进的表格型深度学习模型——MambaAttention、TabPFN和TabTransformer，在使用德克萨斯州（2024年）结构化事故数据（共4,649例）上的表现，涵盖辅助驾驶（SAE Level 1）、部分自动化（SAE Level 2）以及高级自动化（SAE Levels 3-5合并）。经SMOTEENN进行类别平衡后，模型在7,300条记录的统一数据集上训练与评估。结果显示，MambaAttention整体表现最佳，各等级F1得分分别为：SAE 1为88%，SAE 2为97%，SAE 3-5为99%；TabPFN在零样本推理中表现出色，对罕见事故类别具有高鲁棒性；而TabTransformer表现较差，尤其在检测部分自动化事故时（F1-score仅为55%），表明其在建模人机协同控制动态方面存在困难。结果表明，专为表格数据设计的深度学习模型能显著提升自动化等级分类的准确性与效率。将此类模型整合进事故分析框架，有助于支持政策制定、自动驾驶安全评估及监管决策，特别是在识别中高阶自动化技术的高风险情境方面。
原文摘要 · Abstract (English)
The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved in each crash is essential to understanding crash dynamics and system accountability. However, existing approaches often overlook automation-specific factors and lack model sophistication to capture distinctions between different SAE levels. To address this gap, this study evaluates the performance of three advanced tabular deep learning models MambaAttention, TabPFN, and TabTransformer for classifying SAE automation levels using structured crash data from Texas (2024), covering 4,649 cases categorized as Assisted Driving (SAE Level 1), Partial Automation (SAE Level 2), and Advanced Automation (SAE Levels 3-5 combined). Following class balancing using SMOTEENN, the models were trained and evaluated on a unified dataset of 7,300 records. MambaAttention demonstrated the highest overall performance (F1-scores: 88% for SAE 1, 97% for SAE 2, and 99% for SAE 3-5), while TabPFN excelled in zero-shot inference with high robustness for rare crash categories. In contrast, TabTransformer underperformed, particularly in detecting Partial Automation crashes (F1-score: 55%), suggesting challenges in modeling shared human-system control dynamics. These results highlight the capability of deep learning models tailored for tabular data to enhance the accuracy and efficiency of automation-level classification. Integrating such models into crash analysis frameworks can support policy development, AV safety evaluation, and regulatory decisions, especially in distinguishing high-risk conditions for mid- and high-level automation technologies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。