.png){kind=icon}
Return
Efficient Box Approximation for Data-Driven Probabilistic Geofencing
.png){kind=icon}
PRE
AI
DOI：10.1142/S2301385024410024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Advanced Air Mobility (AAM) 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 AAM is how to manage large-scale flight operations with safety guarantees in high-density, dynamic, and uncertain airspace environments in real time. To address these challenges, we introduce the concept of a data-driven probabilistic geofence, which can guarantee that the probability of potential conflicts between eVTOL aircraft is bounded under data-driven uncertainties. To evaluate the probabilistic geofences online, Kernel Density Estimation (KDE) based on Fast Fourier Transform (FFT) is customized to model data-driven uncertainties. Based on the FFT-KDE values from data-driven uncertainties, we introduce an optimization framework of Integer Linear Programming (ILP) to find a parallelogram box to approximate the data-driven probabilistic geofence. To overcome the computational burden of ILP, an efficient heuristic algorithm is further developed. Numerical results demonstrate the feasibility and efficiency of the proposed algorithms.
Keywords:
Probabilistic geofence
box approximation
integer linear programming
Journal
IF:
2.4
Papers:
302
Citations:
705
Organization
Cited Papers
.png){kind=icon}
Conflict-free four-dimensional path planning for urban air mobility considering airspace occupancy
OAAI
OAAIShareSave
ShareSaveShareSaveShareSave
View more