Benchmarking Advanced Tabular AI Models for Classifying SAE Automation Levels in Crash Data

Abstract The growing deployment of vehicles equipped with driver assistance and automated driving systems presents new challenges for crash record classification, as existing police-reported databases vary considerably in the completeness and consistency of automation-level metadata. This study benchmarks five tabular machine learning and deep learning models: random forest, XGBoost, MambaAttention, prior-data fitted network (TabPFN), and TabTransformer for classifying reported SAE automation ca