Terminal‐Area Aircraft Intent Inference Approach Based on Online Trajectory Clustering

Abstract

Terminal-area aircraft intent inference (T-AII) is a prerequisite to detect and avoid potential aircraft conflict in the terminal airspace. T-AII challenges the state-of-the-art AII approaches due to the uncertainties of air traffic situation, in particular due to the undefined flight routes and frequent maneuvers. In this paper, a novel T-AII approach is introduced to address the limitations by solving the problem with two steps that are intent modeling and intent inference. In the modeling step, an online trajectory clustering procedure is designed for recognizing the real-time available routes in replacing of the missed plan routes. In the inference step, we then present a probabilistic T-AII approach based on the multiple flight attributes to improve the inference performance in maneuvering scenarios. The proposed approach is validated with real radar trajectory and flight attributes data of 34 days collected from Chengdu terminal area in China. Preliminary results show the efficacy of the presented approach.

Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning

“…(e.g. [14], [33]), the lookahead time ( as in [14] and [7]), or consider specific constraints for making predictions [13]. State of the art approaches in the ATM domain that are closely related to our work are those in [4], [22] and [13].…”