Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Tollkühn, Andreas"'
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizatio
Externí odkaz:
http://arxiv.org/abs/2305.16196
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GNNs), the GFTNN is a novel network architecture that operates on graph
Externí odkaz:
http://arxiv.org/abs/2305.07416
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powerful frameworks for processing graph-structured data and, hence, have
Externí odkaz:
http://arxiv.org/abs/2304.10939
This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides partial interpretability. The novel approach is based on the architecture
Externí odkaz:
http://arxiv.org/abs/2103.13726