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

2025-03-06

AI Summary

In shared urban spaces, high pedestrian mobility and ambiguous right-of-way render conventional binary external human–machine interfaces (eHMI)—such as static “stop/go” signals—insufficient for safe, predictable vehicle–pedestrian interaction. To address this, we propose PaveFlow: a continuous spatial eHMI that projects the autonomous vehicle’s dynamically planned trajectory onto the ground in real time, enhancing interaction transparency and contextual adaptability. Our approach integrates online trajectory planning, ground-plane geometric mapping, and a virtual reality (VR) experimental platform to evaluate interactions under varying vehicle densities (single- and multi-vehicle scenarios). A VR user study (N=18) demonstrates that PaveFlow significantly improves pedestrian perceived safety, trust, and user experience while reducing cognitive load—effects consistently observed across traffic density conditions. This work introduces the novel paradigm of “continuous spatial path projection” for eHMI design, offering a foundational shift from discrete signaling toward context-aware, spatially grounded communication in autonomous driving.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

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.

Problem

• Improving AV-pedestrian interaction in shared spaces.
• Addressing ambiguity in pedestrian right-of-way and movement.
• Enhancing safety, trust, and user experience with path projection.

Innovation

• Real-time AV path projection on ground
• Enhanced pedestrian safety and trust
• Reduced cognitive workload in shared spaces