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 this https URL

过去十年来，司机分心成为严重交通事故的一个重要原因。本文介绍了一个考虑车内外背景信息的辅助驾驶感知数据集（AIDE），该数据集通过三个特征实现了全面的驾驶员监测，包括驾驶员和场景的多视角设置、对面部、身体、姿势和手势进行多模态注释，以及四种用于驾驶理解的实用任务设计。通过广泛的方法，在三种基准框架上提供了对 AIDE 的实验基准。此外，介绍了两种融合策略，以深入研究学习有效的多流 / 模态表示的新见解。同时，还系统地调查了 AIDE 和基准中关键组件的重要性和合理性。项目链接见 https URL。