Abstract
Pedestrian motion behavior involves a combination of individual goals and social interactions with other agents. In this article, we present a non-symmetrical bidirectional recurrent neural network architecture called U-RNN as a sequence encoder and evaluate its relevance to replace LSTMs for various forecasting models. Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules. Our implementation of the asymmetrical Bi-RNNs for the Trajnet++ benchmark is available at: this http URL
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URL
https://arxiv.org/abs/2106.04419