Year: 2025
Type: conference-paper
Abstract: We showcase the workflow of PAVE (Pedestrian Awareness Via Edge analytics), a scalable system for real-time video analytics that leverages street cameras to improve pedestrians' safety while maintaining their privacy. PAVE distributes computation across edge servers and end-user mobile devices. Cameras' live streams are processed at the edge to forecast vehicles' trajectories and detect danger zones. Pedestrians' mobile devices then locally determine if the user is inside a danger zone and trigger timely alerts via a custom iOS app. In addition, anonymized metadata, such as pedestrian and vehicle positions, speeds, and directions, are aggregated and displayed on a public map for broader situational awareness. We evaluated PAVE's performance through implementation on the NSF COSMOS testbed's edge server while processing real-time video stream from cameras in diverse urban environments. Live field tests at an intersection in New York City show that PAVE can alert at-risk pedestrians about 0.9 s before a vehicle reaches them. With low-latency cameras, this lead time extends to around 1.6 s which is within the 1–2 s window pedestrians typically need to react.
Institutions: Columbia University, Tel Aviv University
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: CPS: Medium: Hybrid Twins for Urban Transportation: From Intersections to Citywide Management, NSF Engineering Research Center for Smart Streetscapes (CS3), RINGS: Enabling Wireless Edge-cloud Services via Autonomous Resource Allocation and Robust Physical Layer Technologies, CIRC: GRAND/ENS: COSMOS3: Enhancing and Sustaining the COSMOS Platform
OpenAlex ID: W4416956276