HSFM-Sigmann: Combining a Feedforward Motion Prediction Network and Covariance Prediction

In this paper, we propose a new method for motion prediction: HSFM-Sigmann. Our proposed method combines two different approaches: a feedforward network whose layers are model-based transition functions using the HSFM and a Neural Network (NN), on each of these layers, for covariance prediction. We will compare our method with classical methods for covariance estimation showing their limitations. We will also compare with a learning-based approach, social-LSTM, showing that our method is more precise and efficient.