Machine Learning for Advising a Driver: A Survey

Driver distraction has become an area of increasing concern with the explosion of new telematics applications. Drivers are confronted with a plethora of new devices—cell phones, in-car video, navigation systems, dialog systems, etc. The proliferation of mature tools in machine learning, image processing, and intelligent systems provides a compelling methodology to attack the problem of driver distraction. As a step in this direction, we propose a new intelligent systems architecture for studying machine learning in the driver space. We survey technologies and modeling methods that could be incorporated into the new framework.

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