  Driver distractions are known to be the dominant cause of road accidents.
While monitoring systems can detect non-driving-related activities and
facilitate reducing the risks, they must be accurate and efficient to be
applicable. Unfortunately, state-of-the-art methods prioritize accuracy while
ignoring latency because they leverage cross-view and multimodal videos in
which consecutive frames are highly similar. Thus, in this paper, we pursue
time-effective detection models by neglecting the temporal relation between
video frames and investigate the importance of each sensing modality in
detecting drives' activities. Experiments demonstrate that 1) our proposed
algorithms are real-time and can achieve similar performances (97.5\% AUC-PR)
with significantly reduced computation compared with video-based models; 2) the
top view with the infrared channel is more informative than any other single
modality. Furthermore, we enhance the DAD dataset by manually annotating its
test set to enable multiclassification. We also thoroughly analyze the
influence of visual sensor types and their placements on the prediction of each
class. The code and the new labels will be released.
