1. Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving.
- Author
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Wandtner, Bernhard, Schömig, Nadja, and Schmidt, Gerald
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AUTOMATIC systems in automobiles , *DRIVERLESS cars , *MULTIPLE resource theory (Communication) , *AUTOMOBILE driving , *TRAFFIC safety - 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. [ABSTRACT FROM AUTHOR]- Published
- 2018
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