Light-based external Human-Machine Interfaces: The effect of animation

Abstract

As the development of autonomous driving vehicle technology accelerates, the necessity for effective communication between autonomous vehicles and other road users is increasingly emphasized. To this end, usability research using various design factors of the proposed external Human-Machine Interfaces (eHMIs) is progressing rapidly. This study specifically focuses on Light-based eHMIs which have fewer cultural/environmental constraints and allow for faster information transmission. As well as we aim to investigate the effects of various animation types applied to Light-based eHMIs. Conducted with a total of 40 participants, we employed survey and interview methods to understand the impact between Light-based eHMIs and pedestrian behavior. The study utilized four types of animations (i.e., Sweeping (Dual), Scale Up-down (Dual), Flickering, Pulsing), along with two AV-First messages (i.e., Walk, Braking), and two Pedestrian-First messages (i.e., Don’t Walk, Driving). Participants evaluated the animations subjectively on aspects of Intuitiveness, Distraction, Variance, Perceived Safety, Trust, Satisfaction, and Preference. Additionally, the interview was conducted regarding the preference evaluation results. The analysis methods included ANOVA for survey results and Semantic Network Analysis (SNA) of affective vocabulary in interview transcripts. The findings indicated that the Flickering animation was significantly higher in Perceived Safety, Trust, and Satisfaction, and to some extent, the Sweeping Animation also showed positive outcomes from pedestrians. Through network analysis, positive/negative vocabulary for each animation was visualized and the eHMIs design direction was explored accordingly. The outcomes of this study could serve as foundational data contributing to the establishment of a safer and more efficient road traffic environment in the era of autonomous driving.

Keywords: Autonomous Vehicles, external Human Machine Interfaces (eHMIs), Light-based eHMIs, Animation, Semantic Network Analysis

DOI: 10.54941/ahfe1005441