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Hum Factors. 2017 Dec;59(8):1233-1248. doi: 10.1177/0018720817728774. Epub 2017 Sep 13.

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

Author information

1
University of Southampton, Southampton, UK.

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

PMID:
28902526
DOI:
10.1177/0018720817728774
[Indexed for MEDLINE]

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