61 citations · 64 across the 6 of their papers we have counts for
6 papers · 1 filter
LMR: Lane Distance-Based Metric for Trajectory Prediction
Julian Schmidt, Thomas Monninger, Julian Jordan +1
The development of approaches for trajectory prediction requires metrics to validate and compare their performance. Currently established metrics are based on Euclidean distance, w…
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
Julian Schmidt, Pascal Huissel, Julian Wiederer +3
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estim…
Exploring Navigation Maps for Learning-Based Motion Prediction
Julian Schmidt, Julian Jordan, Franz Gritschneder +2
The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (H…
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht +5
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to enco…
MEAT: Maneuver Extraction from Agent Trajectories
Julian Schmidt, Julian Jordan, David Raba +2
Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction…
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention
Julian Schmidt, Julian Jordan, Franz Gritschneder +1
Predicting the motion of surrounding vehicles is essential for autonomous vehicles, as it governs their own motion plan. Current state-of-the-art vehicle prediction models heavily…