Pre-instruction for Pedestrians Interacting Autonomous Vehicles with an eHMI: Effects on Their Psychology and Walking Behavior

TL;DR

This paper addresses how pedestrians interpret AVs with eHMI and how pre-instruction affects their understanding and walking behavior. It employs a Wizard-of-Oz road-crossing experiment with four scenarios (MV, AV w/o eHMI, AV w/ eHMI, AV w/ eHMI after pre-instruction) and measures subjective evaluations (Q1–Q6) and walking speeds using OpenPose. The findings show that AVs without eHMI degrade perceived understandability, safety, trust, and relief; eHMI improves these metrics, and pre-instruction further enhances comprehension and prediction, yielding more consistent walking speeds across trials. The results support calibrating pedestrians' mental models via pre-instruction and suggest broader adoption and standardization of eHMI, with implications for safety and traffic-system design.

Abstract

External human-machine interface (eHMI) is considered as a new explicit communication method for pedestrian-AV interactions, particularly in encounter scenarios. Pedestrians without prior negotiation experience with eHMI may misinterpret the driving intentions of AV, leading to confusion and unpredictable behavior. To address this, our study suggests providing pre-instruction on eHMI to enhance comprehension. To compare pedestrians' subjective feelings and walking behavior changes with and without the use of eHMI, as well as before and after receiving pre-instructions, a road crossing experiment using a within-subject design was conducted. In the experiment, the participants were challenged to recognize situations and experienced uncertainty when encountering AVs lacking eHMI, in contrast to manual driving vehicles. After the pre-instruction, participants could understand the driving intention of an AV with eHMI and predict its driving behavior more easily. Furthermore, participants' subjective feelings and hesitation to make decisions improved to align with the same criteria as encountered with a manual driving vehicle. Additionally, this study found that the information guidance effect of using eHMI makes participants' walking speeds more consistent over multiple trials after pre-instruction.

Figures (7)

• Figure 1: Pre-instruction for the calibration of the mental model based on the cognition-decision-behavior model proposed in Liu2022_APMV. The Q1 to Q6 are subjective evaluation questions designed based on this model were used in the experiments (see section \ref{['sec:SE_Q']}).
• Figure 2: Experimental scene: simulation of pedestrian encounters with a car in a parking lot.
• Figure 3: Experimental car with an eHMI: a left-hand-driven car stimulating a right-hand-driven AV. An eHMI is installed on the right bottom of the windshield.
• Figure 4: Procedure of a within-participants design experiment.
• Figure 5: The walking behaviors of participants are estimated using OpenPose with BODY 25 joint set. (a) shows the BODY 25 joint set. (b) Method for extracting the representative point of pedestrian position.