Visual context capture and analysis for driver attention monitoring

Driver distraction is recognized as a major factor in the cause of automobile accidents. Therefore, it is extremely important for an intelligent driver support system to be able to monitor the driver's attentive state. This paper proposes a system to monitor driver attention based on a variety of information sources. The LISA-Q test vehicle is used to synchronously capture video, audio, vehicle information, LASER RADAR information, and GPS information as input to the driver state evaluation. Information about the driver's facial affects, lane keeping, steering movements, and time headway are all extracted from the multimodal data streams and evaluated.

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