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20222026
most citedSCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

61 citations · 64 across the 10 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.RO2023

Joint Out-of-Distribution Detection and Uncertainty Estimation for Trajectory Prediction

Julian Wiederer, Julian Schmidt, Ulrich Kressel +2

Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we…

cs.CV2023

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…

cs.CV2023

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…

cs.CV2023★ 2 cited

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…

cs.CV2023★ 61 cited

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…