Designing Multi-Robot Ground Video Sensemaking with Public Safety Professionals
This addresses the challenge of integrating multi-robot videos into public safety workflows, though it is incremental as it builds on existing video sensemaking practices.
The paper tackled the problem of designing multi-robot ground video systems for public safety by developing a testbed with 38 events-of-interest and a tool (MRVS) that uses a video understanding model, resulting in participants reporting reduced manual workload and greater confidence.
Videos from fleets of ground robots can advance public safety by providing scalable situational awareness and reducing professionals' burden. Yet little is known about how to design and integrate multi-robot videos into public safety workflows. Collaborating with six police agencies, we examined how such videos could be made practical. In Study 1, we presented the first testbed for multi-robot ground video sensemaking. The testbed includes 38 events-of-interest (EoI) relevant to public safety, a dataset of 20 robot patrol videos (10 day/night pairs) covering EoI types, and 6 design requirements aimed at improving current video sensemaking practices. In Study 2, we built MRVS, a tool that augments multi-robot patrol video streams with a prompt-engineered video understanding model. Participants reported reduced manual workload and greater confidence with LLM-based explanations, while noting concerns about false alarms and privacy. We conclude with implications for designing future multi-robot video sensemaking tools.