2 citations · 4 across the 3 of their papers we have counts for
8 papers · 1 filter
Context-Aware Scene Prediction Network (CASPNet)
Maximilian Schäfer, Kun Zhao, Markus Bühren +1
Predicting the future motion of surrounding road users is a crucial and challenging task for autonomous driving (AD) and various advanced driver-assistance systems (ADAS). Planning…
Polynomial Trajectory Predictions for Improved Learning Performance
Ido Freeman, Kun Zhao, Anton Kummert
The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short to mid-term trajectory prediction. Anticipating the unfolding path of…
On the Robustness of Active Learning
Lukas Hahn, Lutz Roese-Koerner, Peet Cremer +3
Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with. When applied correctly, it can be a v…
Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances
Lukas Hahn, Frederik Hasecke, Anton Kummert
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements. N…
FLIC: Fast Lidar Image Clustering
Frederik Hasecke, Lukas Hahn, Anton Kummert
Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products. With an ever growing number of differ…
A Statistical Defense Approach for Detecting Adversarial Examples
Alessandro Cennamo, Ido Freeman, Anton Kummert
Adversarial examples are maliciously modified inputs created to fool deep neural networks (DNN). The discovery of such inputs presents a major issue to the expansion of DNN-based s…