4 citations · 7 across the 7 of their papers we have counts for
7 papers
A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
Marvin Klemp, Dominic Ebner, Cornelius Schröder +15
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's mul…
SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts
Royden Wagner, Ömer Sahin Tas, Marlon Steiner +5
Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model…
MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction
Marlon Steiner, Marvin Klemp, Christoph Stiller
There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap ne…
JointMotion: Joint Self-Supervision for Joint Motion Prediction
Royden Wagner, Omer Sahin Tas, Marvin Klemp +1
We present JointMotion, a self-supervised pre-training method for joint motion prediction in self-driving vehicles. Our method jointly optimizes a scene-level objective connecting…
MaskedFusion360: Reconstruct LiDAR Data by Querying Camera Features
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. Therefore, state-of-the-art methods for…
RedMotion: Motion Prediction via Redundancy Reduction
Royden Wagner, Omer Sahin Tas, Marvin Klemp +2
We introduce RedMotion, a transformer model for motion prediction in self-driving vehicles that learns environment representations via redundancy reduction. Our first type of redun…
LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
Marvin Klemp, Kevin Rösch, Royden Wagner +2
In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore…