This paper introduces a control-physics informed machine learning (CPhy-ML) framework to infer drone intentions from observational data. The framework combines deep learning with aerospace models to improve robustness and reduce bias in intention classification and prediction. CPhy-ML shows significant performance improvements over existing methods in trajectory prediction and reward function inference.
Publisher
Communications Engineering
Published On
Feb 24, 2024
Authors
Adolfo Perrusquía, Weisi Guo, Benjamin Fraser, Zhuangkun Wei
Tags
machine learning
drone intentions
trajectory prediction
reward function inference
deep learning
aerospace models
CPhy-ML
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