In autonomous driving vehicles, the driver can engage in non-driving-related tasks and does not have to pay attention to the driving conditions or engage in manual driving. If an unexpected situation arises that the autonomous vehicle cannot manage, then the vehicle should notify and help the driver to prepare themselves for retaking manual control of the vehicle. Several effective notification methods based on multimodal warning systems have been reported. In this paper, we propose an advanced method that employs alarms for specific conditions by analyzing the differences in the driver’s responses, based on their specific situation, to trigger visual and auditory alarms in autonomous vehicles. Using a driving simulation, we carried out human-in-the-loop experiments that included a total of 38 drivers and 2 scenarios (namely drowsiness and distraction scenarios), each of which included a control-switching stage for implementing an alarm during autonomous driving. Reaction time, gaze indicator, and questionnaire data were collected, and electroencephalography measurements were performed to verify the drowsiness. Based on the experimental results, the drivers exhibited a high alertness to the auditory alarms in both the drowsy and distracted conditions, and the change in the gaze indicator was higher in the distraction condition. The results of this study show that there was a distinct difference between the driver’s response to the alarms signaled in the drowsy and distracted conditions. Accordingly, we propose an advanced notification method and future goals for further investigation on vehicle alarms.

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This study investigated the factors contributing to older adults’ acceptance of assistive social robots. A survey was conducted to find factors explaining and predicting older adults’ acceptance behavior of assistive social robots. Three factors of older adults’ needs for assistive social robots were found (advanced needs, social needs, and physiological needs) which integrated Maslow’s five levels of basic human needs. According to older adults’ self-reported scores, the most important needs were physiological needs, followed by advanced needs and social needs. A regression analysis showed that the advanced needs and social needs significantly influence older adults’ use intention of assistive social robots. The results can assist in the future design of assistive social robot functions and features targeting the older population.

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