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 preserving radar-specific information such as Doppler shifts and phase characteristics. By adjusting patch configurations and leveraging pre-trained positional embedding vectors (PEVs), ISA-ViT overcomes the limitations of naive resizing approaches. In addition, a domain fusion strategy combines range- and frequency-domain features to further improve classification performance. Comprehensive experiments demonstrate that ISA-ViT achieves a 22.68% accuracy improvement over an existing ViT-based approach for UWB-based DAR. By publicly releasing the ALERT dataset and detailing our input-size-agnostic strategy, this work facilitates the development of more robust and scalable distracted driving detection systems for real-world deployment.
Abstract (translated)
驾驶分心是全球致命车祸的主要原因之一。为解决这一问题，研究人员正在使用驾驶员活动识别（DAR）技术结合脉冲无线电超宽带（IR-UWB）雷达，这种技术具有抗干扰能力强、能耗低和保护隐私等优点。然而，两个挑战限制了其广泛应用：一是缺乏涵盖各种分心驾驶行为的大型真实世界UWB数据集；二是难以将固定输入的视觉变换器（ViT）适应于非标准尺寸的UWB雷达数据。 本研究解决了上述两个问题。我们提供了ALERT数据集，其中包含在实际驾驶条件下收集的7种分心驾驶活动的10,220个雷达样本。此外，还提出了一种适用于基于雷达DAR的输入大小无关视觉变换器（ISA-ViT）框架。所提出的这种方法通过调整UWB数据尺寸以满足ViT输入要求的同时保留了如多普勒频移和相位特征等特定于雷达的信息。通过对补丁配置进行调整并利用预训练的位置嵌入向量（PEV），ISA-ViT克服了简单缩放方法的局限性。此外，一种领域融合策略结合范围域和频率域特征进一步提高了分类性能。 全面实验表明，与现有的基于ViT的方法相比，ISA-ViT在基于UWB的DAR上实现了22.68%的准确率提升。通过公开发布ALERT数据集并详细说明我们的输入大小无关策略，本工作促进了更加稳健和可扩展的分心驾驶检测系统的实际应用开发。
URL
https://arxiv.org/abs/2512.12206
