1 code implementation • 12 Apr 2023 • Julian Schmidt, Thomas Monninger, Julian Jordan, Klaus Dietmayer
In contrast to the Euclidean Miss Rate, qualitative results show that LMR yields misses for sequences where predictions are located on wrong lanes.
no code implementations • 12 Apr 2023 • Julian Schmidt, Pascal Huissel, Julian Wiederer, Julian Jordan, Vasileios Belagiannis, Klaus Dietmayer
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle.
1 code implementation • 13 Feb 2023 • Julian Schmidt, Julian Jordan, Franz Gritschneder, Thomas Monninger, Klaus Dietmayer
Combined with our method for knowledge distillation, we achieve results that are close to the original HD map-reliant models.
1 code implementation • 9 Jan 2023 • Thomas Monninger, Julian Schmidt, Jan Rupprecht, David Raba, Julian Jordan, Daniel Frank, Steffen Staab, Klaus Dietmayer
In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders.
Ranked #1 on Node Classification on BGS
no code implementations • 10 Jun 2022 • Julian Schmidt, Julian Jordan, David Raba, Tobias Welz, Klaus Dietmayer
Additionally, an analysis of the datasets and an evaluation of the prediction models based on the agent dynamics is provided.
1 code implementation • 9 Feb 2022 • Julian Schmidt, Julian Jordan, Franz Gritschneder, Klaus Dietmayer
We therefore propose CRAT-Pred, a multi-modal and non-rasterization-based trajectory prediction model, specifically designed to effectively model social interactions between vehicles, without relying on map information.
Ranked #174 on Motion Forecasting on Argoverse CVPR 2020