Lidar-Based Detection and Analysis of Serendipitous Collisions in Shared Indoor Spaces

Abstract

Indoor environments significantly influence human interaction, collaboration, and well-being, yet evaluating how architectural designs actually perform in fostering social connections remains challenging. This study demonstrates the use of 11 static-mounted lidar sensors to detect serendipitous encounters—collisions—between people in a shared common space of a mixed academic–residential university building. A novel collision detection algorithm achieved 86.1% precision and detected 14,022 interactions over 115 days (67 million person-seconds) of an academic semester. While occupancy strongly predicted collision frequency overall (R2 ≥ 0.74), significant spatiotemporal variations revealed the complex relationship between co-presence and social interaction. Key findings include the following: (1) collision frequency peaked early in the semester then declined by ~25% by mid-semester; (2) temporal lags between occupancy and collision peaks of 2–3 h in the afternoon indicate that social interaction differs from physical presence; (3) collisions per occupancy peaked on the weekend, with Saturday showing 52% higher rates than the weekly average; and (4) collisions clustered at key transition zones (elevator areas, stair bases), with an additional “friction effect”, where proximity to seating increased interaction rates (>30%) compared to open corridors. This methodology establishes a scalable framework for post-occupancy evaluation, enabling evidence-based assessment of design effectiveness in fostering the spontaneous interactions essential for creativity, innovation, and place-making in built environments.