According to the assembly task model proposed by Stork and Schubö (2010) , the assembly task is divided into commissioning and joining subtasks. Each subtask includes two sequential stages, namely, perception and response selection, and action. This division enables a convenient discussion of the influences of Augmented reality (AR) assistance on operators during different stages of an assembly task. Research results can provide a basis for the further analysis of the influence mechanism of AR assistance on an assembly task. This study is composed of three experiments. Experiment 1 explores the influences of AR assistance on the performance of the overall assembly task and the commissioning and joining subtasks. Combining a variation of task complexities, Experiments 2 and 3 discuss the influences of AR assistance on the different stages of the commissioning and joining subtasks. We found that AR assistance can shorten the time of the overall assembly task and subtasks (commissioning and joining) and reduce mistakes during these tasks. Moreover, AR assistance can decrease cognitive load in the commissioning subtask, but it increases cognitive load in the joining task with low complexity. In the perception and response selection stage of the commissioning and joining subtasks, AR assistance can shorten the time for users to recognize the target part and understand the assembly relation. This advantage is extremely significant for the high-complexity task. In the action stage of two subtasks, AR assistance can shorten the time for users to capture parts, but it prolongs the time for users to build parts.

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Conditional automated driving [level 3, Society of Automotive Engineers (SAE)] requires drivers to take over the vehicle when an automated system’s failure occurs or is about to leave its operational design domain. Two-stage warning systems, which warn drivers in two steps, can be a promising method to guide drivers in preparing for the takeover. However, the proper time intervals of two-stage warning systems that allow drivers with different personalities to prepare for the takeover remain unclear. This study explored the optimal time intervals of two-stage warning systems with insights into the drivers’ neuroticism personality. A total of 32 drivers were distributed into two groups according to their self-ratings in neuroticism (high vs. low). Each driver experienced takeover under the two-stage warning systems with four time intervals (i.e., 3, 5, 7, and 9 s). The takeover performance (i.e., hands-on-steering-wheel time, takeover time, and maximum resulting acceleration) and subjective opinions (i.e., appropriateness and usefulness) for time intervals and situation awareness (SA) were recorded. The results showed that drivers in the 5-s time interval had the best takeover preparation (fast hands-on steering wheel responses and sufficient SA). Furthermore, both the 5- and 7-s time intervals resulted in more rapid takeover reactions and were rated more appropriate and useful than the 3- and 9-s time intervals. In terms of personality, drivers with high neuroticism tended to take over immediately after receiving takeover messages, at the cost of SA deficiency. In contrast, drivers with low neuroticism responded safely by judging whether they gained enough SA. We concluded that the 5-s time interval was optimal for drivers in two-stage takeover warning systems. When considering personality, drivers with low neuroticism had no strict requirements for time intervals. However, the extended time intervals were favorable for drivers with high neuroticism in developing SA. The present findings have reference implications for designers and engineers to set the time intervals of two-stage warning systems according to the neuroticism personality of drivers.

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