no code implementations • 10 May 2024 • Leon Eisemann, Mirjam Fehling-Kaschek, Silke Forkert, Andreas Forster, Henrik Gommel, Susanne Guenther, Stephan Hammer, David Hermann, Marvin Klemp, Benjamin Lickert, Florian Luettner, Robin Moss, Nicole Neis, Maria Pohle, Dominik Schreiber, Cathrina Sowa, Daniel Stadler, Janina Stompe, Michael Strobelt, David Unger, Jens Ziehn
With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models.
no code implementations • 2 May 2024 • Leon Eisemann, Mirjam Fehling-Kaschek, Henrik Gommel, David Hermann, Marvin Klemp, Martin Lauer, Benjamin Lickert, Florian Luettner, Robin Moss, Nicole Neis, Maria Pohle, Simon Romanski, Daniel Stadler, Alexander Stolz, Jens Ziehn, Jingxing Zhou
With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models.
no code implementations • 30 Apr 2024 • Marlon Steiner, Marvin Klemp, Christoph Stiller
This paper addresses that gap by introducing a novel approach to motion prediction, focusing on predicting agent-pair covariance matrices in a ``scene-centric'' manner, which can then be used to model Gaussian joint PDFs for all agent-pairs in a scene.
no code implementations • 8 Mar 2024 • Royden Wagner, Ömer Şahin Taş, Marvin Klemp, Carlos Fernandez
We present JointMotion, a self-supervised learning method for joint motion prediction in autonomous driving.
3 code implementations • 19 Jun 2023 • Royden Wagner, Omer Sahin Tas, Marvin Klemp, Carlos Fernandez, Christoph Stiller
We introduce RedMotion, a transformer model for motion prediction in self-driving vehicles that learns environment representations via redundancy reduction.
1 code implementation • 12 Jun 2023 • Royden Wagner, Marvin Klemp, Carlos Fernandez Lopez
In self-driving applications, LiDAR data provides accurate information about distances in 3D but lacks the semantic richness of camera data.
no code implementations • 17 Feb 2023 • Marvin Klemp, Kevin Rösch, Royden Wagner, Jannik Quehl, Martin Lauer
Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users.