  Multi-sensor frameworks provide opportunities for ensemble learning and
sensor fusion to make use of redundancy and supplemental information, helpful
in real-world safety applications such as continuous driver state monitoring
which necessitate predictions even in cases where information may be
intermittently missing. We define this problem of intermittent instances of
missing information (by occlusion, noise, or sensor failure) and design a
learning framework around these data gaps, proposing and analyzing an
imputation scheme to handle missing information. We apply these ideas to tasks
in camera-based hand activity classification for robust safety during
autonomous driving. We show that a late-fusion approach between parallel
convolutional neural networks can outperform even the best-placed single camera
model in estimating the hands' held objects and positions when validated on
within-group subjects, and that our multi-camera framework performs best on
average in cross-group validation, and that the fusion approach outperforms
ensemble weighted majority and model combination schemes.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics