Ground Risk Assessment for Unmanned Aircraft Systems Based on Dynamic Model

Abstract

Ground risk, as one of the key parameters for assessing risk before an operation, plays an important role in the safety management of unmanned aircraft systems. However, how to correctly identify ground risk and to predict risk accurately remains challenging due to uncertainty in relevant parameters (people density, ground impact, etc.). Therefore, we propose a dynamic model based on a deep learning approach to assess the ground risk. First, the parameters that affect ground risk (people density, ground impact, sheltered, etc.) are defined and analyzed. Second, a kinetic-theory-based model is applied to assess the extent of ground impact. Third, a joint convolutional neural network–deep neural network model (C-Snet model) is built to predict the density of people on the ground and to calculate the shelter factor for different degrees of ground impact. Last, a dynamic model combining a deep learning and a kinetic model is established to predict ground risk. We performed simulations to validate the effectiveness and efficiency of the model. The results indicate that ground risk has spatial-temporal characteristics and that our model can predict risk accurately by capturing these characteristics.