DDDAS-Based Information-Aggregation for Crowd Dynamics Modeling with UAVs and UGVs

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) collaboratively play important roles in crowd tracking for applications such as border patrol and crowd surveillance. Dynamic data-driven application systems (DDDAS) paradigm has been developed for these applications to take advantage of real-time monitoring data. In the DDDAS paradigm, one crucial step in crowd surveillance is crowd dynamics modeling, which is based on multi-resolution crowd observation data collected from both UAVs and UGVs. Data collected from UAVs capture global crowd motion but have low resolution while those from UGVs have high resolution information of local crowd motion. This paper proposes an information-aggregation approach for crowd dynamics modeling by incorporating multi-resolution data, where a grid-based method is developed to model crowd motion with UAVs’ low-resolution global perception, and an autoregressive model is employed to model individuals’ motion based on UGVs’ detailed perception. A simulation experiment is provided to illustrate and demonstrate the effectiveness of the proposed approach.