Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction

TL;DR

This paper introduces a method to generate synthetic UAV trajectory data in image space, enabling deep learning models to improve UAV motion prediction without extensive real-world data collection.

Contribution

The authors propose a novel approach for creating synthetic UAV trajectories based on smoothness criteria and optimal control, enhancing training data for deep learning models in UAV tracking.

Findings

Synthetic data enables training effective RNN models.

RNN trained on synthetic data outperforms classic models.

Method improves UAV trajectory prediction accuracy.

Abstract

Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications for motion prediction. On the one hand, these models can capture complex object dynamics with less modeling required, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, we present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. Since UAVs, or rather quadrotors are dynamical systems, they can not follow arbitrary trajectories. With the prerequisite that UAV trajectories fulfill a smoothness criterion corresponding to a minimal change of higher-order motion, methods for planning aggressive quadrotors flights can be utilized to generate optimal trajectories through a sequence of 3D waypoints. By projecting these maneuver trajectories, which are suitable for controlling quadrotors, to image space, a versatile trajectory data set is realized. To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset. The evaluation is done on the publicly available ANTI-UAV dataset.