Literature DB >> 29617161

Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving.

Bernhard Wandtner1, Nadja Schömig2, Gerald Schmidt3.   

Abstract

OBJECTIVE: Aim of the study was to evaluate the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving.
BACKGROUND: During highly automated driving, it is allowed to engage in NDR tasks temporarily. However, drivers must be able to take over control when reaching a system limit. There is evidence that the type of NDR task has an impact on takeover performance, but little is known about the specific task characteristics that account for performance decrements.
METHOD: Thirty participants drove in a simulator using a highly automated driving system. Each participant faced five critical takeover situations. Based on assumptions of Wickens's multiple resource theory, stimulus and response modalities of a prototypical NDR task were systematically manipulated. Additionally, in one experimental group, the task was locked out simultaneously with the takeover request.
RESULTS: Task modalities had significant effects on several measures of takeover performance. A visual-manual texting task degraded performance the most, particularly when performed handheld. In contrast, takeover performance with an auditory-vocal task was comparable to a baseline without any task. Task lockout was associated with faster hands-on-wheel times but not altered brake response times.
CONCLUSION: Results showed that NDR task modalities are relevant factors for takeover performance. An NDR task lockout was highly accepted by the drivers and showed moderate benefits for the first takeover reaction. APPLICATION: Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.

Entities:  

Keywords:  autonomous driving; distraction; driver behavior; human-automation interaction; multiple resource models; vehicle automation

Mesh:

Year:  2018        PMID: 29617161     DOI: 10.1177/0018720818768199

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


  3 in total

1.  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

2.  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

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

  3 in total

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