Online Evaluation for Chance-Constrained Geofences under Data-Driven Uncertainties
Pengcheng Wu, Jun Chen · AIAA AVIATION 2022 Forum · 2022
View Video Presentation: https://doi.org/10.2514/6.2022-3613.vid Urban air mobility (UAM) using electrical vertical take-off and landing (eVTOL) aircraft is an emerging way of air transportation within metropolitan areas. A key challenge for the success of UAM is how to manage large-scale flight operations with safety guarantee in high-density, dynamic and uncertain airspace environments in real-time. To deal with these challenges, in this paper we combine the concept of geofence and chance constraints to obtain chance-constrained geofences under data-driven uncertainties, which can guarantee that the probability of potential conflicts between eVTOL aircraft is bounded given a general empirical distribution. To evaluate the chance-constrained geofences in an online fashion, Kernel Density Estimation (KDE) based on Fast Fourier Transform (FFT) is adopted and customized to model data-driven uncertainties. Comprehensive numerical simulations demonstrate the feasibility and efficiency of the online evaluation of chance-constrained geofence through the algorithm of FFT-based KDE.