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arXiv · 2503.05041

Enhancing Autonomous Vehicle-Pedestrian Interaction in Shared Spaces: The Impact of Intended Path-Projection

Le Yue·Tram Thi Minh Tran·Xinyan Yu·Marius Hoggenmueller
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Abstract

External Human-Machine Interfaces (eHMIs) are critical for seamless interactions between autonomous vehicles (AVs) and pedestrians in shared spaces. However, they often struggle to adapt to these environments, where pedestrian movement is fluid and right-of-way is ambiguous. To address these challenges, we propose PaveFlow, an eHMI that projects the AV's intended path onto the ground in real time, providing continuous spatial information rather than a binary stop/go signal. Through a VR study (N=18), we evaluated PaveFlow's effectiveness under two AV density conditions (single vs. multiple AVs) and a baseline condition without PaveFlow. The results showed that PaveFlow significantly improved pedestrian perception of safety, trust, and user experience while reducing cognitive workload. This performance remained consistent across both single and multiple AV conditions, despite persistent tensions in priority negotiation. These findings suggest that path projection enhances eHMI transparency by offering richer movement cues, which may better support AV-pedestrian interaction in shared spaces.

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Le Yue, Tram Thi Minh Tran, Xinyan Yu, Marius Hoggenmueller. 2025-03-06. Enhancing Autonomous Vehicle-Pedestrian Interaction in Shared Spaces: The Impact of Intended Path-Projection. https://doi.org/10.1145/3706599.3720174

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