161 citations · 335 across the 32 of their papers we have counts for
5 papers · 1 filter
Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions
Daniel Grimm, Maximilian Zipfl, Felix Hertlein +7
Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between tra…
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence
Svetlana Pavlitska, Hannes Grolig, J. Marius Zöllner
Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques, includin…
Conditioning Latent-Space Clusters for Real-World Anomaly Classification
Daniel Bogdoll, Svetlana Pavlitska, Simon Klaus +1
Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles. In this work, we focus on high-resolution camera data from…
Utilizing Hybrid Trajectory Prediction Models to Recognize Highly Interactive Traffic Scenarios
Maximilian Zipfl, Sven Spickermann, J. Marius Zöllner
Autonomous vehicles hold great promise in improving the future of transportation. The driving models used in these vehicles are based on neural networks, which can be difficult to…
Holistic Graph-based Motion Prediction
Daniel Grimm, Philip Schörner, Moritz Dreßler +1
Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many fac…