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In the presented driving simulator study, we propose a new test protocol for the assessment of the distraction potential of head-up display (HUD) technologies in the driving context. The method combines driving-related measures with the visual detection response task (DRT) as common evaluation protocols using eye glance measurement are no longer valid. The protocol was applied comparing several use cases in two conditions: a head-down display (HDD) and a head-mounted HUD using smart glasses. The results revealed that in relation to a reference task (manual radio tuning), the smart glasses did not impair either driving performance or visual workload significantly. Additionally, they led to lower visual workload than the HDD when reading text messages, but not when performing simpler tasks. The study points towards a positive effect on the distraction potential of visual-manual tasks with head-mounted HUDs and includes a first proposal for a standardised distraction assessment for these technologies.

Reflecting the increasing demand for harmonization of human machine interfaces (HMI) of automated vehicles, different taxonomies of use cases for investigating automated driving systems (ADS) have been proposed. Existing taxonomies tend to serve specific purposes such as categorizing transitions between automation modes; however, they cannot be generalized to different systems or combinations of systems. In particular, there is no exhaustive set of use cases that allows entities to assess and validate the HMI of a given ADS that takes into account all possible system modes and transitions. The present paper describes a newly developed framework based on combinatorics of SAE (Society of Automotive Engineers) automation levels that incorporates a comprehensive taxonomy of use cases required for the assessment and validation of ADS HMIs. This forms a much-needed basis for test methods required to verify whether an HMI meets minimum requirements such as those outlined in the National Highway Traffic Safety Administration’s Federal Automated Vehicles policy.