Applying MambaAttention, TabPFN, and TabTransformers to Classify SAE Automation Levels in Crashes

TL;DR

This study evaluates three advanced tabular deep learning models—MambaAttention, TabPFN, and TabTransformers—for classifying SAE automation levels in crash data, demonstrating high accuracy especially with MambaAttention, and highlighting their potential for improving crash analysis and safety assessments.

Contribution

The paper introduces the application of MambaAttention, TabPFN, and TabTransformers to classify SAE automation levels in crash data, showing their effectiveness and robustness in this domain.

Findings

MambaAttention achieved the highest F1-scores across SAE levels.

TabPFN demonstrated strong zero-shot inference capabilities.

TabTransformer underperformed in detecting Partial Automation crashes.

Abstract

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.