  Ensuring traffic safety and preventing accidents is a critical goal in daily
driving, where the advancement of computer vision technologies can be leveraged
to achieve this goal. In this paper, we present a multi-view, multi-scale
framework for naturalistic driving action recognition and localization in
untrimmed videos, namely M$^2$DAR, with a particular focus on detecting
distracted driving behaviors. Our system features a weight-sharing, multi-scale
Transformer-based action recognition network that learns robust hierarchical
representations. Furthermore, we propose a new election algorithm consisting of
aggregation, filtering, merging, and selection processes to refine the
preliminary results from the action recognition module across multiple views.
Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset
demonstrate the effectiveness of our approach, where we achieved an overlap
score of 0.5921 on the A2 test set. Our source code is available at
\url{https://github.com/PurdueDigitalTwin/M2DAR}.
