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      Non-Driving-Related Tasks During Level 3 Automated Driving Phases–Measuring What Users Will Be Likely to Do

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          Abstract

          After activation of a Society of Automotive Engineers (SAE) Level 3 automated driving function ( SAE International, 2021), the function takes over the driving task and the human user may engage in other, non-driving-related tasks (NDRTs). Meanwhile, the user needs to remain receptive to requests by the function because s/he needs to reengage in the driving task, when the function approaches a system limit and requests the user to take over. Hence, the effects of NDRT engagement on driver state and takeover behavior have been investigated closely. However, concerning relevance to traffic safety, it is important to take a step back and investigate what NDRTs users are likely to engage in, and what methods are suitable to collect respective data. Two experiments were conducted to answer these questions. In Experiment 1, participants experienced Level 3 automated driving in a Wizard-of-Oz vehicle on German motorways. After the ride, participants were asked to name NDRTs that they would engage in, if the function was available in their own vehicle. In Experiment 2, participants were asked to bring their own NDRTs along and experienced Level 3 automated driving in the Wizard-of-Oz vehicle. Comparison of results shows preferences of similar NDRTs (e.g., smartphone usage and reading). Moreover, we found that different methods provide different insights into NDRT engagement (i.e., engagement rate, total duration rate of engagement, and naming rate). Integrating our results in current literature landscape highlights the strong dependence of resulting NDRTs from the investigation method. Results, strengths, and weaknesses of the employed methods are discussed.

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          Foundations for an Empirically Determined Scale of Trust in Automated Systems

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              EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions.

              The purpose of this study is to show the effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions. An algorithm for the identification of alpha spindles is described. The performance of the algorithm is tested based on simulated data. The method is applied to real data recorded under real traffic conditions and compared with the performance of traditional EEG fatigue measures, i.e. alpha-band power. As a highly valid fatigue reference, the last 20 min of driving from participants who aborted the drive due to heavy fatigue were used in contrast to the initial 20 min of driving. Statistical analysis revealed significant increases from the first to the last driving section of several alpha spindle parameters and among all traditional EEG frequency bands, only of alpha-band power; with larger effect sizes for the alpha spindle based measures. An increased level of fatigue over the same time periods for drop-outs, as compared to participants who did not abort the drive, was observed only by means of alpha spindle parameters. EEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha-band power. It is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
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                Author and article information

                Journal
                Technology, Mind, and Behavior
                American Psychological Association
                2689-0208
                2021
                : 2
                : 1
                Affiliations
                [1]Chair of Ergonomics, Technical University of Munich
                [2]Section F4 Automated Driving, Federal Highway Research Institute (BASt), Bergisch Gladbach, Germany
                Author notes
                Action Editor: C. Shawn Green was the action editor for this article.
                Data collection in Experiment 1 was sponsored by the German Research Association for Automotive Technology (FAT). Data Collection in Experiment 2 was sponsored by the German Federal Ministry for Economic Affairs and Energy (BMWi) based on a resolution of the German Bundestag. Data cannot be made publicly available because of the respective data protection concepts. Analytic methods are described in this publication. Study materials can be found in the Appendix. The authors have no conflicts of interest to disclose.
                [*] Elisabeth Shi, Section F4 Automated Driving, Federal Highway Research Institute (BASt), Bruederstr. 53, Bergisch Gladbach D-51427, Germany shi@bast.de
                Author information
                https://orcid.org/0000-0001-6742-7023
                Article
                10.1037/tmb0000006
                1fc68fb5-f66f-47d8-9abd-7e3bdf6fd39c
                © 2021 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

                History

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                non-driving-related task,automated vehicle,level 3,method,Wizard-of-Oz

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