161 citations · 220 across the 27 of their papers we have counts for
8 papers · 1 filter
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…
Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment
Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner +1
A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly…
Robotic Control Using Model Based Meta Adaption
Karam Daaboul, Joel Ikels, Marius Zöllner
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning…
Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set condition…
Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
Daniel Bogdoll, Moritz Nekolla, Tim Joseph +1
Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strict…