Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, driver inattention monitoring systems are crucial. Even with the growing development of advanced driver assistance systems and the introduction of third-level autonomous vehicles, this task is still trending and complex due to challenges such as the illumination change and the dynamic background. To reliably compare and validate driver inattention monitoring methods, a limited number of public datasets are available. In this paper, we put forward a public, well-structured and complete dataset, named Multiview, Multimodal and Multispectral Driver Action Dataset (3MDAD). The dataset is mainly composed of two sets: the first one recorded in daytime and the second one at nighttime. Each set consists of two synchronized data modalities, both from frontal and side views. More than 60 drivers are asked to execute 16 in-vehicle actions under a wide range of naturalistic driving settings. In contrast to other public datasets, 3MDAD presents multiple modalities, spectrums and views under different time and weather conditions. To highlight the utility of our dataset, we independently analyze the driver action recognition results adapted to each modality and those obtained of several combinations of modalities.

驾驶员分心和疲劳已成为严重交通事故的主要原因之一。因此，驾驶员注意力不集中监视系统至关重要。即使随着高级驾驶员辅助系统的不断发展和三级自动驾驶汽车的推出，由于诸如照明变化和动态背景之类的挑战，这项任务仍在趋向复杂。为了可靠地比较和验证驾驶员注意力不集中的监视方法，可以使用有限数量的公共数据集。在本文中，我们提出了一个公共的，结构良好的完整数据集，称为多视图，多模式和多光谱驾驶员行为数据集（3MDAD）。数据集主要由两套组成：第一套在白天记录，第二套在夜间记录。每个集合都包含两个同步数据模态，从正面和侧面看。要求60多名驾驶员在各种自然驾驶条件下执行16种车内动作。与其他公共数据集相比，3MDAD在不同的时间和天气条件下提供了多种模式，频谱和视图。为了突出我们数据集的效用，我们独立分析了适合每种方式的驾驶员动作识别结果以及从多种方式组合中获得的结果。