This paper presents a novel approach to automated recognition of the driver's activity, which is a crucial factor for determining the take-over readiness in conditionally autonomous driving scenarios. Therefore, an architecture based on head-and eye-tracking data is introduced in this study and several features are analyzed. The proposed approach is evaluated on data recorded during a driving simulator study with 73 subjects performing different secondary tasks while driving in an autonomous setting. The proposed architecture shows promising results towards in-vehicle driver-activity recognition. Furthermore, a significant improvement in the classification performance is demonstrated due to the consideration of novel features derived especially for the autonomous driving context.


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    Title :

    Driver-Activity Recognition in the Context of Conditionally Autonomous Driving




    Publication date :

    2015-09-01


    Size :

    733382 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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