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.

The present study aimed to explore the effects of various tactile takeover requests (TORs) (i.e., tactile sliding TOR and traditional vibration TOR) on the takeover performance in an automated driving system. A tactile sliding motor device was developed to signal the sliding TOR on the seatback of a driving simulator. Twenty-five young drivers were recruited as participants. Four types of TOR patterns were adopted in the study: ipsilateral motor rotation (IR), contralateral motor rotation (CR), ipsilateral and contralateral motor rotation (ICR), and ipsilateral motor vibration (IV). The participants were required to sit on the seat and underwent the automated driving in a low- or high-complexity scenario, then one of the four types of TORs was triggered randomly. The participants were asked to make a lane change using the steering wheel as soon as possible. Objective measures and subjective evaluations were used to assess the takeover performance. Results showed that the participants exhibited a shorter steering response time and lane change time under the three tactile sliding TORs (compared to the traditional vibration TOR). In the high-complexity scenarios and low-complexity scenarios conditions, different result patterns appeared regarding the maximum lateral acceleration and situational awareness. Our findings suggested that the tactile sliding motor is a promising way to signal a TOR in an automated driving system.

Human failures occur in nuclear power plants when operators are under acute stress. Therefore, an automatic stressed recognition system should be developed for nuclear power work. Previous studies on the prediction of stress are limited because of their reliance on subjective ratings and contact physiological measurement. To solve this problem, we developed a non-intrusive way by using voice features to detect stress. We aim to build a system that can estimate the level of stress from speech which may be applied to nuclear power plants where operators engage in regular verbal communication as part of their duties. In this study, we collected voice recordings from 34 participants during a simulated nuclear plant power task in a time-limited situation that requires high cognitive resources. Mel frequency cepstrum coefficients (MFCCs) were extracted from stressed voice samples and the neural network model was used to assess stress levels continuously. The experimental results showed that voice features can provide satisfactory predictions of the stress state. Mean relative errors of prediction are possible within approximately 5%. We discuss the implications of the use of voice as a minimally intrusive means for monitoring the effects of stress on cognitive performance in practical applications.