Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles

Abstract

With self-driving vehicles (SDVs), pedestrians can no longer rely on a human driver. Previous research suggests that pedestrians may benefit from an external Human–Machine Interface (eHMI) displaying information to surrounding traffic participants. This paper introduces a natural methodology to compare eHMI concepts from a pedestrian’s viewpoint. To measure eHMI effects on traffic flow, previous video-based studies instructed participants to indicate their crossing decision with interfering data collection devices, such as pressing a button or slider. We developed a quantifiable concept that allows participants to naturally step off a sidewalk to cross the street. Hidden force-sensitive resistor sensors recorded their crossing onset time (COT) in response to real-life videos of approaching vehicles in an immersive crosswalk simulation environment. We validated our method with an initial study of N = 34 pedestrians by showing (1) that it is able to detect significant eHMI effects on COT as well as subjective measures of perceived safety and user experience. The approach is further validated by (2) replicating the findings of a test track study and (3) participants’ reports that it felt natural to take a step forward to indicate their street crossing decision. We discuss the benefits and limitations of our method with regard to related approaches.

Explanations in Autonomous Driving: A Survey

“…Crossing onset time also decreased with the provision of the eHMI. Crossing onset time indicates the time in seconds between the vehicle yielding and the pedestrian stepping off the sidewalk [176]. It was noted that status eHMI can cause pedestrians to overtrust AVs, therefore, intent messages are needed to complement status eHMIs.…”

Measuring Pedestrians’ Gap Acceptance When Interacting with Vehicles - A Human Gait Oriented Approach

“…With regard to the research environment, some measurement approaches are applied in field experiments, while some others are used in laboratory settings with monitor-and virtual reality-based representations of the investigated traffic scenario [5]. On the one hand, measurement approaches used in laboratory settings benefit from the advantages of a high level of control of confounding factors and test conditions as well as the manipulations of variables as crucial requirements for testing hypotheses and answering formulated research questions [6,8]. Moreover, these studies enable a simple and cheap proof-of-concept validation [5].…”

“…With regard to the subjects' stationary position, studies note that merely standing still had negative effects as natural and intuitive body movements are an integral part of the perceptual process [13][14][15]. This shortcoming is overcome by approaches that rely on lower extremity movements, such as walking forward or backward [8,9,11,12,16]. Yet, instructing subjects to step back as soon as they no longer feel safe requires subjects to move in a direction opposite to their natural path when crossing [9].…”

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

“…The last paper regarding the concept of eHMIs "Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles" by Faas, Mattes, Kao and Baumann [9] introduces a methodology to compare eHMI concepts from a pedestrian's viewpoint. Therefore a quantifiable concept that allows participants to naturally step off a sidewalk to cross the street was developed.…”