Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study
arXiv:2603.19812v2 Announce Type: replace Abstract: To address this gap, we conduct a Virtual Reality experiment in which pedestrians interact with automated shuttles under varying approach angles (45{\deg}, 90{\deg}, 135{\deg}) and continuous-traffic conditions (single shuttle, two shuttles with 3 or 5-second gaps), collecting synchronized motion, eye gaze, and head orientation data. To investigate to what extent, under what conditions, and in what form fine-grained eye gaze is informative for pedestrian motion prediction, we develop a multi-modal prediction model that fuses these signals thr
The proliferation of automated systems in shared spaces necessitates advanced predictive models for safe and efficient human-robot interaction.
Accurate pedestrian trajectory prediction is critical for the safe deployment and public acceptance of autonomous vehicles and intelligent infrastructure in urban environments.
The ability to incorporate fine-grained eye gaze and context into predictive models improves the robustness and reliability of autonomous systems' understanding of human intent.
- · Autonomous vehicle developers
- · Smart city planners
- · AI model developers
- · Developers of less robust trajectory prediction systems
Improved safety and efficiency of autonomous shuttles operating in mixed traffic.
Faster integration and broader societal acceptance of autonomous systems in public spaces.
Enhanced overall fluidity of urban mobility with fewer human-robot collisions and more predictable interactions.
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Read at arXiv cs.LG