A data set of real world driving to assess driver workload

Stefan Schneegass; Bastian Pfleging; Nora Broy; Frederik Heinrich; Albrecht Schmidt

doi:10.1145/2516540.2516561

A data set of real world driving to assess driver workload

Stefan Schneegass, Bastian Pfleging, Nora Broy, Frederik Heinrich, Albrecht Schmidt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

100 Citations (Scopus)

Abstract

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 use 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 investigate the correlation between the objective assessed and subjective measured data.

Original language	English
Title of host publication	AutomotiveUI
Publisher	Association for Computing Machinery, Inc
Pages	150-157
Number of pages	8
DOIs	https://doi.org/10.1145/2516540.2516561
Publication status	Published - 28 Oct 2013
Externally published	Yes

Bibliographical note

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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10.1145/2516540.2516561

Cite this

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title = "A data set of real world driving to assess driver workload",

abstract = "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 use 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 investigate the correlation between the objective assessed and subjective measured data.",

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doi = "10.1145/2516540.2516561",

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N2 - 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 use 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 investigate the correlation between the objective assessed and subjective measured data.

AB - 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 use 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 investigate the correlation between the objective assessed and subjective measured data.

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ER -

A data set of real world driving to assess driver workload

Abstract

Bibliographical note

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