Dynamic monocular machine vision
Machine Vision and Applications
Applications of dynamic monocular machine vision
Machine Vision and Applications
Rapidly Adapting Machine Vision for Automated Vehicle Steering
IEEE Expert: Intelligent Systems and Their Applications
Autonomous Driving Goes Downtown
IEEE Intelligent Systems
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Fast obstacle detection for urban traffic situations
IEEE Transactions on Intelligent Transportation Systems
Making use of drivers' glances onto the screen for explicit gaze-based interaction
Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Proceedings of the Symposium on Eye Tracking Research and Applications
Multi-mode saliency dynamics model for analyzing gaze and attention
Proceedings of the Symposium on Eye Tracking Research and Applications
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Current road safety initiatives are approaching the limit of their effectiveness in developed countries. A paradigm shift is needed to address the preventable deaths of thousands on our roads. Previous systems have focused on one or two aspects of driving: environmental sensing, vehicle dynamics or driver monitoring. Our approach is to consider the driver and the vehicle as part of a combined system, operating within the road environment. A driver assistance system is implemented that is not only responsive to the road environment and the driver's actions but also designed to correlate the driver's eye gaze with road events to determine the driver's observations. Driver observation monitoring enables an immediate in-vehicle system able to detect and act on driver inattentiveness, providing the precious seconds for an inattentive human driver to react. We present a prototype system capable of estimating the driver's observations and detecting driver inattentiveness. Due to the "look but not see" case it is not possible to prove that a road event has been observed by the driver. We show, however, that it is possible to detect missed road events and warn the driver appropriately.