Advancing ADAS Acceptance: Interventions and Comparative Analysis of Robotic Human Machine Interfaces

Advanced Driver Assistance Systems (ADAS) enhance vehicle safety by providing critical information, warning drivers, and automating control tasks to reduce manual operation. However, the acceptability of ADAS is often limited by the human-machine interface (HMI) used, due to issues such as perceived usefulness, ease of use and trust of the ADAS operations. This study proposes using a robotic human-machine interface (RHMI) to improve the acceptability of ADAS and explores whether a small humanoid robot or a minimally designed robot is more effective as an RHMI in widely used three ADAS operations: Adaptive Cruise Control (ACC), Lane Tracking Assistance (LTA), and Blind Spot Monitoring (BSM). We conducted three experimental conditions in a driving simulator using a within-subject design: only a conventional HMI (C-HMI), C-HMI and a humanoid RHMI (RoBoHoN), and C-HMI and a minimally designed RHMI prototype (RHMI-P) in a within-subject design. Participants’ subjective assessments and eye gaze data were analyzed. The findings indicate that the acceptability of the BSM operation increased with RoBoHoN due to its familiar and human-like appearance. However, the objective measures revealed that RHMI-P increased gaze alertness and was perceived as more competent and trustworthy. This study highlights the importance of incorporating human-like elements and effectively using non-verbal cues when designing an interface for ADAS to improve the acceptability of ADAS operations and increase their usage for safer roads.