HSFM-$Σ$nn: Combining a Feedforward Motion Prediction Network and Covariance Prediction
This addresses motion prediction for applications like robotics or autonomous systems, but appears incremental as it builds on existing methods.
The paper tackles motion prediction by combining a feedforward network with model-based transition functions and neural networks for covariance prediction, showing that the method is more precise and efficient than classical and learning-based approaches like social-LSTM.
In this paper, we propose a new method for motion prediction: HSFM-$Σ$nn. 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.