论文标题
HSFM- $σ$ nn：结合前进运动预测网络和协方差预测
HSFM-$Σ$nn: Combining a Feedforward Motion Prediction Network and Covariance Prediction
论文作者
论文摘要
在本文中，我们提出了一种运动预测的新方法：HSFM- $σ$ nn。我们提出的方法结合了两种不同的方法：一个前馈网络，其层是基于模型的过渡函数，使用HSFM和神经网络（NN），在这些层上，以进行协方差预测。我们将将我们的方法与经典方法进行比较，以显示其局限性的协方差估计。我们还将与基于学习的方法Social-LSTM进行比较，这表明我们的方法更加精确和有效。
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.
