60 citations · 268 across the 18 of their papers we have counts for
20 papers
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…
A Realism Metric for Generated LiDAR Point Clouds
Larissa T. Triess, Christoph B. Rist, David Peter +1
A considerable amount of research is concerned with the generation of realistic sensor data. LiDAR point clouds are generated by complex simulations or learned generative models. T…
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…
Cooperative Trajectory Planning in Uncertain Environments with Monte Carlo Tree Search and Risk Metrics
Philipp Stegmaier, Karl Kurzer, J. Marius Zöllner
Automated vehicles require the ability to cooperate with humans for smooth integration into today's traffic. While the concept of cooperation is well known, developing a robust and…
StarNet: Joint Action-Space Prediction with Star Graphs and Implicit Global Frame Self-Attention
Faris Janjoš, Maxim Dolgov, J. Marius Zöllner
In this work, we present a novel multi-modal multi-agent trajectory prediction architecture, focusing on map and interaction modeling using graph representation. For the purposes o…
A Survey on Deep Domain Adaptation for LiDAR Perception
Larissa T. Triess, Mariella Dreissig, Christoph B. Rist +1
Scalable systems for automated driving have to reliably cope with an open-world setting. This means, the perception systems are exposed to drastic domain shifts, like changes in we…
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…
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study
Larissa T. Triess, David Peter, Christoph B. Rist +1
Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep n…
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…
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
Karl Kurzer, Marcus Fechner, J. Marius Zöllner
Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own…
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…
CNN-based synthesis of realistic high-resolution LiDAR data
Larissa T. Triess, David Peter, Christoph B. Rist +2
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with…
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…
Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning
Holger Banzhaf, Paul Sanzenbacher, Ulrich Baumann +1
Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution…
Improved Semantic Stixels via Multimodal Sensor Fusion
Florian Piewak, Peter Pinggera, Markus Enzweiler +2
This paper presents a compact and accurate representation of 3D scenes that are observed by a LiDAR sensor and a monocular camera. The proposed method is based on the well-establis…
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States
Peter Wolf, Karl Kurzer, Tobias Wingert +2
Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model…
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Karl Kurzer, Florian Engelhorn, J. Marius Zöllner
Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whet…
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Karl Kurzer, Chenyang Zhou, J. Marius Zöllner
Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative pl…
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
Florian Piewak, Peter Pinggera, Manuel Schäfer +6
Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central compone…
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Florian Piewak, Timo Rehfeld, Michael Weber +1
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical appli…