Abstract
In the realm of autonomous vehicles, society is undergoing a transition from conventional human-driven vehicles to autonomous driving systems. Therefore, there is an increasing demand for vehicles integrated with assistive driving systems. This pilot study designed to explore which type of driving system reminders, namely Text display, Image display, alarm notification, or humanoid voice command, provokes stronger preferences and higher rates of cooperation from drivers. A high-fidelity driving simulator mainly consisting of a Logitech PlayStation driving system, a reminder playing system and an emotion-detecting model was developed in a lab-setting environment. A cohort of participants (N = 6) was recruited to participate in the experiment, where they were tasked with completing assignments across four driving sessions, followed by a subsequent questionnaire. During each driving session, the participants were exposed to six reminders designed for different driving conditions, including seatbelt check, fuel level check, rear mirror check, over speed reminder, obstacles reminder and drowsy driving reminder. Concurrently, the participants' driving performance was observed by the researcher, while changes in their emotional states were detected by the model. Subsequent to the driving sessions, participants were invited to complete a questionnaire for assessing the various formats of driving reminders presented by the four stimuli, utilizing a 5-level Likert scale. The results revealed that driving reminders with sounds (alarm notification and humanoid voice command) exhibited higher recognition and cooperation rates among drivers than the silent reminders (text display and image display). Participants demonstrated stronger preferences for Voice-based driving reminders, which aligns with the observed behaviours of drivers. Despite the limitations of a small sample size of participants, this within-subject study which collected data from 24 individual driving sessions (6 participants x 4 driving sessions) provides insights on enhancing communication between human drivers and computer-assisted driving systems by developing improved alert systems for drivers. It also seeks to enhance the field of automotive user interface design by developing more intuitive and responsive interactions between humans and humanoid-assistant in future autonomous vehicles.