Literature DB >> 23156620

Control task substitution in semiautomated driving: does it matter what aspects are automated?

Oliver Carsten1, Frank C H Lai, Yvonne Barnard, A Hamish Jamson, Natasha Merat.   

Abstract

OBJECTIVE: The study was designed to show how driver attention to the road scene and engagement of a choice of secondary tasks are affected by the level of automation provided to assist or take over the basic task of vehicle control. It was also designed to investigate the difference between support in longitudinal control and support in lateral control.
BACKGROUND: There is comparatively little literature on the implications of automation for drivers' engagement in the driving task and for their willingness to engage in non-driving-related activities.
METHOD: A study was carried out on a high-level driving simulator in which drivers experienced three levels of automation: manual driving, semiautomated driving with either longitudinal or lateral control provided, and highly automated driving with both longitudinal and lateral control provided. Drivers were free to pay attention to the roadway and traffic or to engage in a range of entertainment and grooming tasks.
RESULTS: Engagement in the nondriving tasks increased from manual to semiautomated driving and increased further with highly automated driving. There were substantial differences in attention to the road and traffic between the two types of semiautomated driving.
CONCLUSION: The literature on automation and the various task analyses of driving do not currently help to explain the effects that were found. Lateral support and longitudinal support may be the same in terms of levels of automation but appear to be regarded rather differently by drivers.

Mesh:

Year:  2012        PMID: 23156620     DOI: 10.1177/0018720812460246

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


  10 in total

1.  The Challenges of Partially Automated Driving.

Authors:  Stephen M Casner; Edwin L Hutchins; Don Norman
Journal:  Commun ACM       Date:  2016-04-26       Impact factor: 4.654

2.  The Effect of Cognitive Load on Auditory Susceptibility During Automated Driving.

Authors:  Remo M A Van der Heiden; J Leon Kenemans; Stella F Donker; Christian P Janssen
Journal:  Hum Factors       Date:  2021-03-11       Impact factor: 3.598

3.  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

4.  Redesigning Today's Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces.

Authors:  Christopher D D Cabrall; Jork C J Stapel; Riender Happee; Joost C F de Winter
Journal:  Hum Factors       Date:  2020-01-29       Impact factor: 2.888

5.  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

6.  Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire.

Authors:  Sina Nordhoff; Jork Stapel; Xiaolin He; Alexandre Gentner; Riender Happee
Journal:  PLoS One       Date:  2021-12-21       Impact factor: 3.240

7.  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

8.  An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis.

Authors:  Tao Xu; Andrei Dragomir; Xucheng Liu; Haojun Yin; Feng Wan; Anastasios Bezerianos; Hongtao Wang
Journal:  Front Neuroinform       Date:  2022-08-16       Impact factor: 3.739

9.  Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach.

Authors:  Weixi Ren; Bo Yu; Yuren Chen; Kun Gao
Journal:  Int J Environ Res Public Health       Date:  2022-09-09       Impact factor: 4.614

Review 10.  A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices.

Authors:  Nili E Krausz; Levi J Hargrove
Journal:  Sensors (Basel)       Date:  2019-11-28       Impact factor: 3.576

  10 in total

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