SA-TSDTP: A State-Aware Two-Stage Framework for Logistics Drone Trajectory Prediction

Abstract

The high flexibility and uncertainty of the flight trajectory are important factors affecting the operational safety of logistics drones. Reliable flight trajectory prediction is the core technology to solve this problem. In order to solve the multivariate time series prediction task of short-time flight trajectory prediction, this work proposes a state-aware two-stage prediction model. In the first stage, logistics drone trajectories are segmented into three flight states based on geometric characteristics and altitude change rates. In the second stage, a multi-expert fusion module incorporating flight state information is introduced to capture the underlying motion patterns of drones and enhance prediction accuracy. The proposed prediction model is evaluated using trajectory data collected from multiple real logistics drones in Shenzhen, China. Experimental results demonstrate that the proposed approach exhibits strong potential and consistently outperforms baseline methods across multiple datasets and evaluation metrics. These findings provide valuable technical support for the future development of safe urban air traffic operations and smart cities.