Preprint Open Access

Measuring trust in automated driving using a multi-level approach to human factors

Clement, Philipp; Danzinger, Herbert; Veledar, Omar; Koenczoel, Clemens; Macher, Georg; Eichberger, Arno


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">In cabin measurement, HMI, trust, comfort, driver state, ADAS/AD, multi-level approach, psychophysiology, heart-rate</subfield>
  </datafield>
  <controlfield tag="005">20210907014842.0</controlfield>
  <controlfield tag="001">5464509</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">AVL List GmbH</subfield>
    <subfield code="a">Danzinger, Herbert</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">AVL List GmbH</subfield>
    <subfield code="a">Veledar, Omar</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University of Graz</subfield>
    <subfield code="a">Koenczoel, Clemens</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Graz University of Technology</subfield>
    <subfield code="a">Macher, Georg</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Graz University of Technology</subfield>
    <subfield code="a">Eichberger, Arno</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">761134</subfield>
    <subfield code="z">md5:ed7466c1be80797fff4d3f00603b6924</subfield>
    <subfield code="u">https://zenodo.org/record/5464509/files/DSD2021_ITS_Measure_Human_Factors_PREPRINT.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2021-09-06</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-teaching-h2020</subfield>
    <subfield code="o">oai:zenodo.org:5464509</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">AVL List GmbH</subfield>
    <subfield code="a">Clement, Philipp</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Measuring trust in automated driving using a multi-level approach to human factors</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-teaching-h2020</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">723324</subfield>
    <subfield code="a">Improved trustworthiness and weather-independence of conditional automated vehicles in mixed traffic scenarios</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">871385</subfield>
    <subfield code="a">A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;As the driving is shifting towards automation, the maximization of related benefits would profit from improved user acceptance of the new technology. Studies suggest a strong connection between acceptance and trust in technical solutions. We investigate the improvement of user trust to driving automation through demonstrations that carried on a sophisticated driving simulator. The study correlates subjective data with objective psycho-physiological measurements. The multi-factorial and multivariate analysis of variance investigates the influence of learning effects and pre-experience with ADAS on trust. Results show improvement in trust through user interaction with a human-machine interface of the demonstrated AD system, hence illustrating the relevance of human-centered development processes. The conclusion is supported by the observation of driver cardiac signals.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.5464508</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.5464509</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">preprint</subfield>
  </datafield>
</record>
30
29
views
downloads
All versions This version
Views 3030
Downloads 2929
Data volume 22.1 MB22.1 MB
Unique views 2626
Unique downloads 2727

Share

Cite as