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In this paper, we propose a new method for motion prediction:HSFM-$\Sigma$nn. Our proposed method combines two different approaches: afeedforward network whose layers are model-based transition functions using theHSFM and a Neural Network (NN), on each of these layers, for covarianceprediction. We will compare our method with classical methods for covarianceestimation showing their limitations. We will also compare with alearning-based approach, social-LSTM, showing that our method is more preciseand efficient.

HSFM-$\Sigma$nn: Combining a Feedforward Motion Prediction Network and Covariance Prediction