4 papers
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,…
Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling
Tobias Leemann, Moritz Sackmann, Jörn Thielecke +1
Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast's uncertainty. The Multiple Hypotheses Predicti…