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.