Driver distraction has become a significant cause of severe traffic accidents over the past decade. Despite the growing development of vision-driven driver monitoring systems, the lack of comprehensive perception datasets restricts road safety and traffic security. In this paper, we present an AssIstive Driving pErception dataset (AIDE) that considers context information both inside and outside the vehicle in naturalistic scenarios. AIDE facilitates holistic driver monitoring through three distinctive characteristics, including multi-view settings of driver and scene, multi-modal annotations of face, body, posture, and gesture, and four pragmatic task designs for driving understanding. To thoroughly explore AIDE, we provide experimental benchmarks on three kinds of baseline frameworks via extensive methods. Moreover, two fusion strategies are introduced to give new insights into learning effective multi-stream/modal representations. We also systematically investigate the importance and rationality of the key components in AIDE and benchmarks. The project link is https://github.com/ydk122024/AIDE.

翻译：过去十年中，驾驶员分心已成为导致严重交通事故的重要原因。尽管视觉驱动的驾驶员监控系统发展迅速，但缺乏全面的感知数据集限制了道路安全与交通保障。本文提出了一个辅助驾驶感知数据集（AIDE），该数据集考虑了自然场景中车辆内外部的上下文信息。AIDE通过三个独特特性促进全面的驾驶员监控，包括驾驶员与场景的多视角设置、面部、身体、姿态和手势的多模态标注，以及四种用于驾驶理解的实用任务设计。为深入探索AIDE，我们通过多种方法提供了三类基线框架的实验基准。此外，引入两种融合策略，为学习有效的多流/多模态表示提供新见解。我们还系统地研究了AIDE及基准中关键组件的重要性和合理性。项目链接为：https://github.com/ydk122024/AIDE。