Human-derived deceleration: will the role of external Human-Machine Interface change if the automated vehicle yields like a human?

There has been extensive research on how automated vehicles (AVs) should interact with pedestrians, most of which focused on explicit communication via external Human-Machine Interface (eHMI). However, evidence from human-human interactions shows that pedestrians rarely rely on explicit signals; instead, they predominantly interpret implicit cues from vehicle movements. Prior AV research has generally not designed motion cues to resemble human driving behaviours, which may have exaggerated dependence on eHMIs. This study asks: how would human-derived AV kinematics modulate the influence of eHMIs on pedestrian behaviour? In a within-participant design, forty participants completed a cave-based pedestrian simulator experiment examining three deceleration profiles: (1) HH - human-derived deceleration pattern and stopping distance; (2) AH - non-human-derived deceleration pattern and human-derived stopping distance (AH); (3) AA - non-human-derived deceleration and stopping distance. The presence of eHMI was also manipulated. Dependent variables include Crossing Initiation Time (CIT), Perceived Safety (PS), Reaction Time to perceiving deceleration onset (RTdec) and Reaction Time to perceiving the eHMI (RTeHMI). Using Bayesian Multilevel Distributional Models, results showed that human-derived behaviour (both HH and AH) led to shorter CIT, higher PS ratings compared to AA. HH and AH were largely similar, with only a subtle difference in RTdec. Due to the high saliency of eHMI, HH and AH showed slight additional improvements in CIT and PS compared to without. This study demonstrates that adopting human-derived behaviours can substantially enhance AV-pedestrian interaction, without introducing the safety concerns often associated with eHMI, presenting a promising approach for AV communication.