11 citations · 12 across the 9 of their papers we have counts for
9 papers
Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling
Marion Neumeier, Niklas Roßberg, Michael Botsch +1
Accurate and uncertainty-aware trajectory prediction remains a core challenge for autonomous driving, driven by complex multi-agent interactions, diverse scene contexts and the inh…
X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Prediction
Aanchal Rajesh Chugh, Marion Neumeier, Sebastian Dorn
Accurate trajectory prediction is crucial for safe and reliable autonomous driving systems, requiring models that capture long-term temporal dependencies while accounting for socia…
Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis
Niklas Roßberg, Marion Neumeier, Sinan Hasirlioglu +2
The ability to operate safely in increasingly complex traffic scenarios is a fundamental requirement for Automated Driving Systems (ADS). Ensuring the safe release of ADS functions…
Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
Marion Neumeier, Sebastian Dorn, Michael Botsch +1
This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. The proposed mode…
Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain
Marion Neumeier, Sebastian Dorn, Michael Botsch +1
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 o…
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.…
A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
Marion Neumeier, Andreas Tollkühn, Michael Botsch +1
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GN…
Gradient Derivation for Learnable Parameters in Graph Attention Networks
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
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 powe…
Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space
Marion Neumeier, Andreas Tollkühn, Thomas Berberich +1
This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides part…