Literature DB >> 23156621

Highly automated driving, secondary task performance, and driver state.

Natasha Merat1, A Hamish Jamson, Frank C H Lai, Oliver Carsten.   

Abstract

OBJECTIVE: A driving simulator study compared the effect of changes in workload on performance in manual and highly automated driving. Changes in driver state were also observed by examining variations in blink patterns.
BACKGROUND: With the addition of a greater number of advanced driver assistance systems in vehicles, the driver's role is likely to alter in the future from an operator in manual driving to a supervisor of highly automated cars. Understanding the implications of such advancements on drivers and road safety is important.
METHOD: A total of 50 participants were recruited for this study and drove the simulator in both manual and highly automated mode. As well as comparing the effect of adjustments in driving-related workload on performance, the effect of a secondary Twenty Questions Task was also investigated.
RESULTS: In the absence of the secondary task, drivers' response to critical incidents was similar in manual and highly automated driving conditions. The worst performance was observed when drivers were required to regain control of driving in the automated mode while distracted by the secondary task. Blink frequency patterns were more consistent for manual than automated driving but were generally suppressed during conditions of high workload.
CONCLUSION: Highly automated driving did not have a deleterious effect on driver performance, when attention was not diverted to the distracting secondary task. APPLICATION: As the number of systems implemented in cars increases, an understanding of the implications of such automation on drivers' situation awareness, workload, and ability to remain engaged with the driving task is important.

Entities:  

Mesh:

Year:  2012        PMID: 23156621     DOI: 10.1177/0018720812442087

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  6 in total

1.  The effect of varying levels of vehicle automation on drivers' lane changing behaviour.

Authors:  Ruth Madigan; Tyron Louw; Natasha Merat
Journal:  PLoS One       Date:  2018-02-21       Impact factor: 3.240

Review 2.  Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving.

Authors:  Thomas McWilliams; Nathan Ward
Journal:  Front Psychol       Date:  2021-04-15

3.  Analyzing the Influencing Factors and Workload Variation of Takeover Behavior in Semi-Autonomous Vehicles.

Authors:  Hui Zhang; Yijun Zhang; Yiying Xiao; Chaozhong Wu
Journal:  Int J Environ Res Public Health       Date:  2022-02-06       Impact factor: 3.390

4.  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

5.  Component-Based Interactive Framework for Intelligent Transportation Cyber-Physical Systems.

Authors:  Sangsoo Jeong; Youngmi Baek; Sang H Son
Journal:  Sensors (Basel)       Date:  2020-01-02       Impact factor: 3.576

6.  How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks.

Authors:  Aya Ataya; Won Kim; Ahmed Elsharkawy; SeungJun Kim
Journal:  Sensors (Basel)       Date:  2021-03-21       Impact factor: 3.576

  6 in total

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