Literature DB >> 27107472

Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving.

Kathrin Zeeb1, Axel Buchner2, Michael Schrauf3.   

Abstract

Currently, development of conditionally automated driving systems which control both lateral and longitudinal vehicle guidance is attracting a great deal of attention. The driver no longer needs to constantly monitor the roadway, but must still be able to resume vehicle control if necessary. The relaxed attention requirement might encourage engagement in non-driving related secondary tasks, and the resulting effect on driver take-over is unclear. The aim of this study was to examine how engagement in three different naturalistic secondary tasks (writing an email, reading a news text, watching a video clip) impacted take-over performance. A driving simulator study was conducted and data from a total of 79 participants (mean age 40 years, 35 females) were used to examine response times and take-over quality. Drivers had to resume vehicle control in four different non-critical scenarios while engaging in secondary tasks. A control group did not perform any secondary tasks. There was no influence of the drivers' engagement in secondary tasks on the time required to return their hands to the steering wheel, and there seemed to be only little if any influence on the time the drivers needed to intervene in vehicle control. Take-over quality, however, deteriorated for distracted drivers, with drivers reading a news text and drivers watching a video deviating on average approximately 8-9cm more from the lane center. These findings seem to indicate that establishing motor readiness may be carried out almost reflexively, but cognitive processing of the situation is impaired by driver distraction. This, in turn, appears to determine take-over quality. The present findings emphasize the importance to consider both response times and take-over quality for a comprehensive understanding of factors that influence driver take-over. Furthermore, a training effect in response times was found to be moderated by the drivers' prior experience with driver assistance systems. This shows that besides driver distraction, driver-related factors influencing take-over performance exist.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Automated driving; Conditional automation; Driver distraction; Driver take-over; Driving simulator

Mesh:

Year:  2016        PMID: 27107472     DOI: 10.1016/j.aap.2016.04.002

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  8 in total

1.  Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority.

Authors:  Davide Maggi; Richard Romano; Oliver Carsten
Journal:  Hum Factors       Date:  2020-08-31       Impact factor: 2.888

2.  Predicting takeover response to silent automated vehicle failures.

Authors:  Callum Mole; Jami Pekkanen; William Sheppard; Tyron Louw; Richard Romano; Natasha Merat; Gustav Markkula; Richard Wilkie
Journal:  PLoS One       Date:  2020-11-30       Impact factor: 3.240

3.  Drivers use active gaze to monitor waypoints during automated driving.

Authors:  Callum Mole; Jami Pekkanen; William E A Sheppard; Gustav Markkula; Richard M Wilkie
Journal:  Sci Rep       Date:  2021-01-08       Impact factor: 4.996

4.  The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process.

Authors:  Lichao Yang; Mahdi Babayi Semiromi; Yang Xing; Chen Lv; James Brighton; Yifan Zhao
Journal:  Sensors (Basel)       Date:  2021-12-22       Impact factor: 3.576

5.  How Does Approaching a Lead Vehicle and Monitoring Request Affect Drivers' Takeover Performance? A Simulated Driving Study with Functional MRI.

Authors:  Chimou Li; Xiaonan Li; Ming Lv; Feng Chen; Xiaoxiang Ma; Lin Zhang
Journal:  Int J Environ Res Public Health       Date:  2021-12-31       Impact factor: 3.390

6.  Age-related effects of executive function on takeover performance in automated driving.

Authors:  Qijia Peng; Yanbin Wu; Nan Qie; Sunao Iwaki
Journal:  Sci Rep       Date:  2022-03-30       Impact factor: 4.379

7.  Analysing the effect of gender on the human-machine interaction in level 3 automated vehicles.

Authors:  Shuo Li; Phil Blythe; Yanghanzi Zhang; Simon Edwards; Weihong Guo; Yanjie Ji; Paul Goodman; Graeme Hill; Anil Namdeo
Journal:  Sci Rep       Date:  2022-07-08       Impact factor: 4.996

8.  Drivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid.

Authors:  Niek Beckers; Luciano Cavalcante Siebert; Merijn Bruijnes; Catholijn Jonker; David Abbink
Journal:  Sci Rep       Date:  2022-09-28       Impact factor: 4.996

  8 in total

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