# Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models

arXiv: https://arxiv.org/abs/2509.11449 · HTML: https://mlsanity.com/paper/2509.11449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [Mambular: A Sequential Model for Tabular Deep Learning](https://mlsanity.com/paper/2408.06291/) (2024)
- [A Survey on Deep Tabular Learning](https://mlsanity.com/paper/2410.12034/) (2024)
- [MambaTab: A Plug-and-Play Model for Learning Tabular Data](https://mlsanity.com/paper/2401.08867/) (2024)
- [Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba](https://mlsanity.com/paper/2503.18970/) (2025)
- [Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach](https://mlsanity.com/paper/2312.00066/) (2023)
- [Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists](https://mlsanity.com/paper/2503.10474/) (2025)
- [Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet](https://mlsanity.com/paper/2503.11003/) (2025)
- [Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms](https://mlsanity.com/paper/2112.03407/) (2021)

## Built on by (most cited first, 2 of 2)

- [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://mlsanity.com/paper/2511.08667/) (2025)
- [Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev](https://mlsanity.com/paper/2610.10321/) (2026)

## Abstract (arXiv)

This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After filtering for electric-only vehicles, 23,301 EV-involved crash records were analyzed. Feature importance techniques using XGBoost and Random Forest identified intersection relation, first harmful event, person age, crash speed limit, and day of week as the top predictors, along with advanced safety features like automatic emergency braking. To address class imbalance, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTEENN) resampling was applied. Three state-of-the-art deep tabular models, TabPFN, MambaNet, and MambaAttention, were benchmarked for severity prediction. While TabPFN demonstrated strong generalization, MambaAttention achieved superior performance in classifying severe injury cases due to its attention-based feature reweighting. The findings highlight the potential of deep tabular architectures for improving crash severity prediction and enabling data-driven safety interventions in EV crash contexts.