A Dataset of Real World Driving to Assess Driver Workload

Driving a car is becoming increasingly complex. Many new features (e.g., for communication or entertainment) that can be used in addition to the primary task of driving a car increase the driver’s workload. Assessing the driver’s workload, however, is still a challenging task. A variety of means are explored which rather focus on experimental conditions than on real world scenarios (e.g., questionnaires). We focus on physiological data that may be assessed in an non-obtrusive way in the future and is therefore applicable in the real world. Hence, we conducted a real world driving experiment with 10 participants measuring a variety of physiological data as well as a post-hoc video rating session. We recorded all parameters and release the dataset to be publicly available for other research projects.

As a first analysis (see citation below) we used this data to analyze the differences in the workload in terms of road type as well as especially important parts of the route such as exits and on-ramps. Furthermore, we investigated the correlation between the objective assessed and subjective measured data.

Citation / First Paper

A first publication about this data collection project has been published at AutomotiveUI ’13:

Stefan Schneegass, Bastian Pfleging, Nora Broy, Albrecht Schmidt, and Frederik Heinrich. 2013. A data set of real world driving to assess driver workload. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’13). ACM, New York, NY, USA, 150-157. DOI=10.1145/2516540.2516561 http://doi.acm.org/10.1145/2516540.2516561

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