Literature DB >> 28902526

Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles.

Alexander Eriksson1, Neville A Stanton1.   

Abstract

OBJECTIVE: This study aims to explore whether driver-paced, noncritical transitions of control may counteract some of the aftereffects observed in the contemporary literature, resulting in higher levels of vehicle control.
BACKGROUND: Research into control transitions in highly automated driving has focused on urgent scenarios where drivers are given a relatively short time span to respond to a request to resume manual control, resulting in seemingly scrambled control when manual control is resumed.
METHOD: Twenty-six drivers drove two scenarios with an automated driving feature activated. Drivers were asked to read a newspaper or monitor the system and relinquish or resume control from the automation when prompted by vehicle systems. Driving performance in terms of lane positioning and steering behavior was assessed for 20 seconds post resuming control to capture the resulting level of control.
RESULTS: It was found that lane positioning was virtually unaffected for the duration of the 20-second time span in both automated conditions compared to the manual baseline when drivers resumed manual control; however, significant increases in the standard deviation of steering input were found for both automated conditions compared to baseline. No significant differences were found between the two automated conditions.
CONCLUSION: The results indicate that when drivers self-paced the transfer back to manual control they exhibit less of the detrimental effects observed in system-paced conditions. APPLICATION: It was shown that self-paced transitions could reduce the risk of accidents near the edge of the operational design domain. Vehicle manufacturers must consider these benefits when designing contemporary systems.

Keywords:  automated driving; automation; cognitive systems engineering; control transitions; distributed cognition; driving performance; takeover requests; task regulation

Mesh:

Year:  2017        PMID: 28902526     DOI: 10.1177/0018720817728774

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


  2 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.  A toolbox for automated driving on the STISIM driving simulator.

Authors:  Alexander Eriksson; Joost de Winter; Neville A Stanton
Journal:  MethodsX       Date:  2018-08-15
  2 in total

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