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Assessing the driver's attention and detecting various hazardous andnon-hazardous events during a drive are critical for driver's safety. Attentionmonitoring 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 beenexplored sufficiently yet in this paradigm. This is the detection of specificevents, hazardous and non-hazardous, during driving that affects the driver'smental and physiological states. The other challenge in evaluating multi-modalsensory applications is the absence of very large scale EEG data because of thevarious limitations of using EEG in the real world. In this paper, we use bothof the above sensor modalities and compare them against the two tasks ofassessing the driver's attention and detecting hazardous vs. non-hazardousdriving events. We collect user data on twelve subjects and show how in theabsence of very large-scale datasets, we can still use pre-trained deeplearning convolution networks to extract meaningful features from both of theabove modalities. We used the publicly available KITTI dataset for evaluatingour platform and to compare it with previous studies. Finally, we show that theresults presented in this paper surpass the previous benchmark set up in theabove driver awareness-related applications.

Attention Monitoring and Hazard Assessment with Bio-Sensing and Vision: Empirical Analysis Utilizing CNNs on the KITTI Dataset