Towards a Driver Monitoring System for Estimating Driver Situational Awareness Autonomous vehicle technology is rapidly developing but the current stateoftheart still has limitations and requires frequent human intervention. However, handovers from an autonomous vehicle to a human driver are challenging because a human operator may be unaware of the vehicle surroundings during a handover which can lead to dangerous driving outcomes. There is presently an urgent need to develop advanced driverassistance systems capable of monitoring driver situational awareness within an autonomous vehicle and intelligently handingover control to a human driver in emergency situations. Towards this goal, in this paper we present the development and evaluation of a visionbased system that identifies visual cues of a driverxe2x80x99s situational awareness including their: head pose, eye pupil position, average head movement rate and visual focus of attention.

Attention Monitoring and Hazard Assessment with BioSensing and Vision: Empirical Analysis Utilizing CNNs on the KITTI Dataset Assessing the driveru0027s attention and detecting various hazardous and nonhazardous events during a drive are critical for driveru0027s safety. Attention monitoring in driving scenarios has mostly been carried out using vision (camerabased) modality by tracking the driveru0027s gaze and facial expressions. It is only recently that biosensing 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 nonhazardous, during driving that affects the driveru0027s mental and physiological states. The other challenge in evaluating multimodal 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 driveru0027s attention and detecting hazardous vs. nonhazardous driving events. We collect user data on twelve subjects and show how in the absence of very largescale datasets, we can still use pretrained 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 awarenessrelated applications.