Dataset Information

Assessing acceptance of electric automated vehicles after exposure in a realistic traffic environment.

ABSTRACT: After years of hypothetical surveys and simulator studies, automated vehicles (AVs) are now being tested in realistic traffic environments adding validity to knowledge about their acceptance. We present data from a pilot test with participants (n = 125) after experiencing a ride in an electric AV on a large clinic area in Berlin, Germany. As a first contribution, we bridge the gap between missing definitions of key constructs, confusion about their operationalisations, and a rigorous test of their statistical properties and data structure by examining scales on acceptance, trust, perceived safety, intention to use, and-for the first time applied to AVs-the emotions amusement, fear, surprise, and boredom. Tests of reliability and normality were satisfying for almost all constructs (Cronbach's alphas ? .69; six of eight scales normally distributed). The vehicles were accepted (M = 1.22; SD = 0.70; range -2 to 2), trusted (M = 3.29; SD = 0.81; range 1 to 5), and perceived as safe (M = 3.29; SD = 1.03; range 1 to 5). However, factor analyses did not reflect the hypothesised data structure, and validity concerns question the suitability of some constructs for attitude assessment of electric AVs. Our open item for comments added valuable insights in qualitative aspects of user attitudes towards electric AVs regarding driving style, technical features, and (unsettling) audio-visual feedback. We thus argue for broader conceptualisations of key constructs based on interdisciplinary exchange and multi-methodical study designs.

SUBMITTER: Zoellick JC

PROVIDER: S-EPMC6497263 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Assessing acceptance of electric automated vehicles after exposure in a realistic traffic environment.

Zoellick Jan C JC Kuhlmey Adelheid A Schenk Liane L Schindel Daniel D Blüher Stefan S

PloS one 20190502 5

After years of hypothetical surveys and simulator studies, automated vehicles (AVs) are now being tested in realistic traffic environments adding validity to knowledge about their acceptance. We present data from a pilot test with participants (n = 125) after experiencing a ride in an electric AV on a large clinic area in Berlin, Germany. As a first contribution, we bridge the gap between missing definitions of key constructs, confusion about their operationalisations, and a rigorous test of the ...[more]

PMID: 31048877

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Project description:ObjectiveResearch in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement.ApproachROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline.Main resultsThe electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS.SignificanceWe hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

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Dataset Information

Assessing acceptance of electric automated vehicles after exposure in a realistic traffic environment.

Publications

Assessing acceptance of electric automated vehicles after exposure in a realistic traffic environment.

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