  Assessing the driver's attention and detecting various hazardous and
non-hazardous events during a drive are critical for driver's safety. Attention
monitoring in driving scenarios has mostly been carried out using vision
(camera-based) modality by tracking the driver's gaze and facial expressions.
It is only recently that bio-sensing modalities such as Electroencephalogram
(EEG) are being explored. But, there is another open problem which has not been
explored sufficiently yet in this paradigm. This is the detection of specific
events, hazardous and non-hazardous, during driving that affects the driver's
mental and physiological states. The other challenge in evaluating multi-modal
sensory applications is the absence of very large scale EEG data because of the
various limitations of using EEG in the real world. In this paper, we use both
of the above sensor modalities and compare them against the two tasks of
assessing the driver's attention and detecting hazardous vs. non-hazardous
driving events. We collect user data on twelve subjects and show how in the
absence of very large-scale datasets, we can still use pre-trained deep
learning convolution networks to extract meaningful features from both of the
above modalities. We used the publicly available KITTI dataset for evaluating
our platform and to compare it with previous studies. Finally, we show that the
results presented in this paper surpass the previous benchmark set up in the
above driver awareness-related applications.
