Abstract-Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. Thus, we propose a novel technique for on-line detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data. We show that Long Short-Term Memory (LSTM) recurrent neural networks enable a reliable, subject-independent detection of inattention with an accuracy of up to 96.6 %. Thereby our LSTM framework significantly outperforms conventional approaches such as Support Vector Machines.
Besides reduction of energy consumption, which implies alternate actuation and light construction, the main research domain in automobile development in the near future is dominated by driver assistance and natural driver-car communication. The ability of a car to understand natural speech and provide a human-like driver assistance system can be expected to be a factor decisive for market success on par with automatic driving systems. Emotional factors and affective states are thereby crucial for enhanced safety and comfort. This paper gives an extensive literature overview on work related to influence of emotions on driving safety and comfort, automatic recognition, control of emotions, and improvement of in-car interfaces by affect sensitive technology. Various use-case scenarios are outlined as possible applications for emotion-oriented technology in the vehicle. The possible acceptance of such future technology by drivers is assessed in a Wizard-Of-Oz user study, and feasibility of automatically recognising various driver states is demonstrated by an example system for monitoring driver attentiveness. Thereby an accuracy of 91.3% is reported for classifying in real-time whether the driver is attentive or distracted.
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