Multimodal Warnings Design for In-Vehicle Robots under Driving Safety Scenarios

Abstract

In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of multimodal warnings for in-vehicle robots under driving safety warning scenarios. Based on transparency theory, this study addressed the content and timing of visual and auditory modality warning outputs and discussed the effects of different robot speech and facial expressions on driving safety. Two rounds of experiments were conducted on a driving simulator to collect vehicle data, subjective data, and behavioral data. The results showed that driving safety and workload were optimal when the robot was designed to use negative expressions for the visual modality during the comprehension (SAT 2) phase and speech at a rate of 345 words/minute for the auditory modality during the comprehension (SAT 2) and prediction (SAT 3) phases. The design guideline obtained from the study provides a reference for the interaction design of driver assistance systems with robots as the interface.