activity
20172022
most citedAn Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios

11 citations · 11 across the 6 of their papers we have counts for

collaborators

13 papers

cs.CV2022

Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization

Abhishek Vivekanandan, Niels Maier, J. Marius Zoellner

Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the sys…

cs.SE202211 cited

An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios

Barbara Schütt, Marc Heinrich, Sonja Marahrens +2

Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated driving is scenario…

cs.RO2020

Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets

Simon T. Isele, Marcel P. Schilling, Fabian E. Klein +2

Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is…

cs.LG2020

Parallelization of Monte Carlo Tree Search in Continuous Domains

Karl Kurzer, Christoph Hörtnagl, J. Marius Zöllner

Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari. Previous research has developed parallel versions of MC…

cs.CV2019

Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling

Florian Piewak, Peter Pinggera, Marius Zöllner

State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network arch…

cs.CV2019

Automated Focal Loss for Image based Object Detection

Michael Weber, Michael Fürst, J. Marius Zöllner

Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced…