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This paper addresses the issue of driving while drowsy and proposes a passive eye monitoring-based driver eye closure recognition system. It reviews the core algorithmic building blocks of this system along with in-depth analysis of operational test field characteristics. The system operates equally in both day and night-time. It automatically finds the drivers eyes in the images, tracks the eye location in a wide range of head and eye motion, and estimates in real-time the eye state as either open or closed eye, and further infers to driver drowsiness state. This paper reports as well the experimental results on a diverse and challenging set of subjects and environmental driving conditions.

Despite the rapid progress in the development of autonomous vehicles, as seen in the DARPA Urban Challenge 2007, there has been very little emphasis on minimizing costs. Some teams spent upward of $10 million in developmental expenses. The cost factor is very important as it is the primary driver leading to the commercialization of autonomous technology. With this fact in mind, an alternative approach has been emphasized here, wherein a fully autonomous vehicle designed for urban environments has been developed and tested for under $20,000 in hardware costs. Moreover, this vehicle passed several rounds of elimination to participate in the semi-finals of the DARPA Urban Challenge at Victorville, California, in November 2007.

This paper addresses the issue of driving while distracted and presents a frontal/non-frontal head pose-based driver distraction alert system along with its integration with conventional Lane Departure Warning and Forward Collision Warning systems. It overviews the core algorithmic building blocks of these systems while reporting the experimental results obtained on a diverse and challenging set of subjects and environmental driving conditions.