Running into Traffic: Investigating External Human-Machine Interfaces for Automated Vehicle-Runner Interaction

Abstract

Automated vehicles (AVs) must communicate their yielding intentions to pedestrians at crossings. External Human-Machine Interfaces (eHMIs, on-vehicle displays) are promising solutions, but were primarily tested with walking pedestrians. Runners are a significant pedestrian group who move faster and face distinct bodily and perceptual demands, raising questions about how pedestrian activity influences eHMI use. We conducted an outdoor study using an augmented reality simulator. Participants navigated a virtual crossing while walking and running; an approaching AV displayed one of three eHMIs: red/green colour-changing lights, animated cyan lights, or no-eHMI. No-eHMI consistently underperformed. Walkers mostly stopped and validated eHMI signals with vehicle behaviour; they processed both eHMI animations and colour changes effectively. Runners experienced greater time pressure to cross, increasing reliance on the eHMI over vehicle behaviour. They preferred colour changes over animation for rapid decisions. These findings are crucial for promoting eHMI inclusivity and physical wellbeing as AVs join our roads.

Figures (8)

• Figure 1: The AR simulator used in the study. The left image shows a participant moving in real physical space while wearing a Meta Quest 3 headset. The middle image shows the participant's point of view (POV) of the virtual urban environment projected by the headset with passthrough, and the right shows the virtual AV with the LightRing eHMI approaching the crossing and not yielding.
• Figure 2: The eHMIs explored in the study. The left image shows the baseline condition with No-eHMI. Top: LightRing communicating its autonomous state (vehicle is automated with all sensors functioning), its yielding state and then its non-yielding state. Bottom: CyanBand communicating the same three states; the arrows show the direction of cyan animations.
• Figure 3: Top-down view of the study's crossing scenario. A two-lane intersection with one lane for an AV turning left onto the main road and another for a vehicle turning off the main road. The AV approached from the pedestrian’s left, travelling in the lane furthest from them. The blue arrow indicates the pedestrian’s path; the orange arrow indicates the AV’s path.
• Figure 4: Plots showing pedestrian behaviours when using each eHMI per walking and running conditions. Left: Boxplot of Crossing Speed; Middle: Frequency Plot of Stopping Frequency; Right: Boxplot of Head Rotations. In the Crossing Speed and Head Rotation plots, orange boxes represent walking and blue ones represent running.
• Figure 5: Boxplots showing the distribution of scores for each eHMI across walking (orange) and running (blue) conditions, for the Reliance on the eHMI, Perceived Instruction, Perceived Risk and Perceived Safety subscales. Data for No-eHMI were omitted for the Reliance on the eHMI metric. LightRing was consistently the best-performing, CyanBand performed better for walkers than runners, and No-eHMI underperformed in all metrics.