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[2512.12206] ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB
Summary
The paper presents the ALERT dataset and an input-size-agnostic Vision Transformer (ISA-ViT) for driver activity recognition using IR-UWB radar, addressing challenges in distracted driving detection.
Why It Matters
With distracted driving being a major cause of accidents, this research provides a significant contribution by offering a large-scale dataset and a novel framework that enhances the accuracy of driver activity recognition systems, promoting safer driving practices.
Key Takeaways
- Introduction of the ALERT dataset with 10,220 radar samples for driver activity recognition.
- Development of the ISA-ViT framework that adapts Vision Transformers for non-standard UWB data.
- Demonstrated 22.68% accuracy improvement over existing methods for distracted driving detection.
Computer Science > Computer Vision and Pattern Recognition arXiv:2512.12206 (cs) [Submitted on 13 Dec 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB Authors:Jeongjun Park, Sunwook Hwang, Hyeonho Noh, Jin Mo Yang, Hyun Jong Yang, Saewoong Bahk View a PDF of the paper titled ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB, by Jeongjun Park and 5 other authors View PDF HTML (experimental) Abstract:Distracted driving contributes to fatal crashes worldwide. To address this, researchers are using driver activity recognition (DAR) with impulse radio ultra-wideband (IR-UWB) radar, which offers advantages such as interference resistance, low power consumption, and privacy preservation. However, two challenges limit its adoption: the lack of large-scale real-world UWB datasets covering diverse distracted driving behaviors, and the difficulty of adapting fixed-input Vision Transformers (ViTs) to UWB radar data with non-standard dimensions. This work addresses both challenges. We present the ALERT dataset, which contains 10,220 radar samples of seven distracted driving activities collected in real driving conditions. We also propose the input-size-agnostic Vision Transformer (ISA-ViT), a framework designed for radar-based DAR. The proposed method resizes UWB data to meet ViT input requirements while preserv...
