A Lightweight Architecture For Driver Status Monitoring Via Convolutional Neural Networks Nowadays, a significant portion of road accidents is attributed to dirverxe2x80x99s inattention or dangerous behavior such as call or smoke. Monitoring the driverxe2x80x99s status through computer vision techniques to detect driving errors in real time to avoid vehicle collisions is one of the most important issues in machine vision and Advanced Driver Assistant System(ADAS). In this paper, we propose a novel facial landmark model and a lightweight architecture of Driver Status Monitoring(DSM) by integrating several deep learning models in order to monitor the driverxe2x80x99s inattention caused by drowsiness, fatigue and distraction in real time. With inverted residual block and multiscale feature map, the proposed facial landmark model achieve 4.90% normalized mean error on common set of 300W dataset. The proposed detection model achieved a mean average precise(mAP0.5) of 90% on our validation dataset. Several results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development.

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.