Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Botsch, Michael"'
Autor:
Kruber, Friedrich, Sánchez Morales, Eduardo, Egolf, Robin, Wurst, Jonas, Chakraborty, Samarjit, Botsch, Michael
Publikováno v:
Leibniz Transactions on Embedded Systems, Vol 8, Iss 1, Pp 02:1-02:27 (2022)
The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper
Externí odkaz:
https://doaj.org/article/3cebdf024b6e4dcc94066d55baeea359
This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. The proposed model ensures the drivability of the predicted trajectory by integrating n
Externí odkaz:
http://arxiv.org/abs/2405.14384
This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios. Based on a spatio-temporal vehicle interaction graph, an observed traffic scenario can be transformed into the graph spectral do
Externí odkaz:
http://arxiv.org/abs/2309.16702
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
Publikováno v:
2022 IEEE Intelligent Vehicles Symposium (IV)
Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity measures or good representations for the traffic scenarios. In this work,
Externí odkaz:
http://arxiv.org/abs/2207.09120
Autor:
Balasubramanian, Lakshman, Wurst, Jonas, Egolf, Robin, Botsch, Michael, Utschick, Wolfgang, Deng, Ke
Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view predictio
Externí odkaz:
http://arxiv.org/abs/2207.08609