  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.
