12 citations · 14 across the 9 of their papers we have counts for
3 papers · 1 filter
Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph
Ferdinand Mütsch, Maximilian Zipfl, Nikolai Polley +1
Scenario-based testing is an indispensable instrument for the comprehensive validation and verification of automated vehicles (AVs). However, finding a manageable and finite, yet r…
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…