Tesla has changed the name of its Full Self-Driving feature in some of its European websites, including Germany, Spain and France, to "Assisted Driving." While the Full Self-Driving feature is already available in some parts of Europe, Tesla is working to get it approved in more countries. As Reuters reports, the rebranding came after Germany revealed that its Transport Minister Steffen Bilger is advocating for its swift approval. In the country's announcement, it explained that the term Full Self-Driving, particularly because it is still of the Supervised variety, is "somewhat misleading." The system doesn't actually drive the car, Germany's Federal Ministry for Digital and Transport said, and it only provides assistance for certain tasks, like accelerating and braking.

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.