6 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…
Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation
Fabian Konstantinidis, Moritz Sackmann, Ulrich Hofmann +1
Scalable multi-agent driving simulation requires behavior models that are both realistic and computationally efficient. We address this by optimizing the behavior model that contro…
From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Fabian Konstantinidis, Ariel Dallari Guerreiro, Raphael Trumpp +4
Accurate motion prediction of surrounding traffic participants is crucial for the safe and efficient operation of automated vehicles in dynamic environments. Marginal prediction mo…
Conditional Prediction by Simulation for Automated Driving
Fabian Konstantinidis, Moritz Sackmann, Ulrich Hofmann +1
Modular automated driving systems commonly handle prediction and planning as sequential, separate tasks, thereby prohibiting cooperative maneuvers. To enable cooperative planning,…
SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts
Royden Wagner, Ãmer 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…