12 citations · 21 across the 16 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…
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach
Maximilian Zipfl, Moritz Jarosch, J. Marius Zöllner
Ensuring validation for highly automated driving poses significant obstacles to the widespread adoption of highly automated vehicles. Scenario-based testing offers a potential solu…
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…
Inverse Universal Traffic Quality -- a Criticality Metric for Crowded Urban Traffic Scenes
Barbara Schütt, Maximilian Zipfl, J. Marius Zöllner +1
An essential requirement for scenario-based testing the identification of critical scenes and their associated scenarios. However, critical scenes, such as collisions, occur compar…
A Comprehensive Review on Ontologies for Scenario-based Testing in the Context of Autonomous Driving
Maximilian Zipfl, Nina Koch, J. Marius Zöllner
The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a pro…