161 citations · 220 across the 27 of their papers we have counts for
9 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…
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Daniel Bogdoll, Jing Qin, Moritz Nekolla +3
Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. C…
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…
Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label Noise
Svetlana Pavlitskaya, Joël Oswald, J. Marius Zöllner
Although numerous methods to reduce the overfitting of convolutional neural networks (CNNs) exist, it is still not clear how to confidently measure the degree of overfitting. A met…
Ad-datasets: a meta-collection of data sets for autonomous driving
Daniel Bogdoll, Felix Schreyer, J. Marius Zöllner
Autonomous driving is among the largest domains in which deep learning has been fundamental for progress within the last years. The rise of datasets went hand in hand with this dev…