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Driver distraction has become a significant cause of severe traffic accidentsover the past decade. Despite the growing development of vision-driven drivermonitoring systems, the lack of comprehensive perception datasets restrictsroad safety and traffic security. In this paper, we present an AssIstiveDriving pErception dataset (AIDE) that considers context information bothinside and outside the vehicle in naturalistic scenarios. AIDE facilitatesholistic driver monitoring through three distinctive characteristics, includingmulti-view settings of driver and scene, multi-modal annotations of face, body,posture, and gesture, and four pragmatic task designs for drivingunderstanding. To thoroughly explore AIDE, we provide experimental benchmarkson three kinds of baseline frameworks via extensive methods. Moreover, twofusion strategies are introduced to give new insights into learning effectivemulti-stream/modal representations. We also systematically investigate theimportance and rationality of the key components in AIDE and benchmarks. Theproject link is https://github.com/ydk122024/AIDE.

AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception