2 citations · 6 across the 15 of their papers we have counts for
15 papers
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
Richard Schwarzkopf, Jonas Merkert, Frank Bieder +22
Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategi…
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
Richard Schwarzkopf, Fabian Immel, Alexander Blumberg +21
Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a E…
Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset
Royden Wagner, Omer Sahin Tas, Jaime Villa +20
Handling rare events is the central open challenge in autonomous driving. Reasoning models, which generate explicit chains of reasoning before acting, promise to generalize to such…
Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models
Marlon Steiner, Royden Wagner, Ömer Sahin Tas +1
Combining motion prediction and motion planning offers a promising framework for enhancing interactions between automated vehicles and other traffic participants. However, this int…
RetroMotion: Retrocausal Motion Forecasting Models are Instructable
Royden Wagner, Omer Sahin Tas, Felix Hauser +7
Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint tr…
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving
Yinzhe Shen, Omer Sahin Tas, Kaiwen Wang +2
Perceiving the environment and its changes over time corresponds to two fundamental yet heterogeneous types of information: semantics and motion. Previous end-to-end autonomous dri…
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…
Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
Omer Sahin Tas, Royden Wagner
Transformer-based models generate hidden states that are difficult to interpret. In this work, we analyze hidden states and modify them at inference, with a focus on motion forecas…
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…
Self-supervised pseudo-colorizing of masked cells
Royden Wagner, Carlos Fernandez Lopez, Christoph Stiller
Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, w…
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection
Royden Wagner, Karl Rohr
Cell detection in microscopy images is important to study how cells move and interact with their environment. Most recent deep learning-based methods for cell detection use convolu…
EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring
Royden Wagner, Karl Rohr
Volumetric cell segmentation in fluorescence microscopy images is important to study a wide variety of cellular processes. Applications range from the analysis of cancer cells to b…