AIConfPaper
← Search

Klaus Dietmayer

23 accepted papers

2024

Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions

Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Alexander Meißner, Klaus Dietmayer
RA-L 2024

Adverse weather conditions can severely affect the performance of LiDAR sensors by introducing unwanted noise in the measurements. Therefore, differentiating between noise and valid points is crucial for the reliable use of these sensors. Current approaches for detecting adverse weather points requi

Cited by 6Source ↗Scholar ↗
2024

Multimodal Object Query Initialization for 3D Object Detection

Mathijs R. Van Geerenstein, Felicia Ruppel, Klaus Dietmayer, Dariu M. Gavrila
ICRA 2024poster

3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network architecture used for this task, in which so-called object queries act as candidate objects. Initializing these object que…

Cited by 2Source ↗Scholar ↗
2023

Energy-Based Detection of Adverse Weather Effects in LiDAR Data

Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Alexander Meissner, Klaus Dietmayer
RA-L 2023

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach

Cited by 26Source ↗code ↗Scholar ↗
2023

Exploring Navigation Maps for Learning-Based Motion Prediction

Julian Schmidt, Julian Jordan, Franz Gritschneder, Thomas Monninger, Klaus Dietmayer
ICRA 2023poster

The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (HD) maps for learning-based motion prediction. Navigation maps provide topological and geometrical information on road-le…

Cited by 6Source ↗code ↗Scholar ↗
2023

Joint Out-of-Distribution Detection and Uncertainty Estimation for Trajectory Prediction

Julian Wiederer, Julian Schmidt, Ulrich Kressel, Klaus Dietmayer, Vasileios Belagiannis
IROS 2023poster

Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we propose an approach that covers two sources of error, namely novel situations with out-of-distribution (OOD) detection a…

Cited by 6Source ↗code ↗Scholar ↗
2023

SCENE: Reasoning About Traffic Scenes Using Heterogeneous Graph Neural Networks

Thomas Monninger, Julian Schmidt, Jan Rupprecht, David Raba, Julian Jordan, Daniel Frank +2
RA-L 2023

Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Ne

Cited by 43Source ↗code ↗Scholar ↗
2023

Tackling Clutter in Radar Data - Label Generation and Detection Using PointNet++

Johannes Kopp, Dominik Kellner, Aldi Piroli, Klaus Dietmayer
ICRA 2023poster

Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel…

Cited by 16Source ↗code ↗Scholar ↗
2022

CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention

Julian Schmidt, Julian Jordan, Franz Gritschneder, Klaus Dietmayer
ICRA 2022poster

Predicting the motion of surrounding vehicles is essential for autonomous vehicles, as it governs their own motion plan. Current state-of-the-art vehicle prediction models heavily rely on map information. In reality, however, this information is not always available. We therefore propose CRAT-Pred,…

Cited by 72Source ↗code ↗Scholar ↗
2022

MotionMixer: MLP-based 3D Human Body Pose Forecasting

Arij Bouazizi, Adrian Holzbock, Ulrich Kressel, Klaus Dietmayer, Vasileios Belagiannis
IJCAI 2022poster

In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP e…

Cited by 83Source ↗PDF ↗code ↗Scholar ↗
2021

Dynamic Occupancy Grid Mapping with Recurrent Neural Networks

Marcel Schreiber, Vasileios Belagiannis, Claudius Gläser, Klaus Dietmayer
ICRA 2021poster

Modeling and understanding the environment is an essential task for autonomous driving. In addition to the detection of objects, in complex traffic scenarios the motion of other road participants is of special interest. Therefore, we propose to use a recurrent neural network to predict a dynamic occ…

Cited by 52Source ↗Scholar ↗
2021

MGNet: Monocular Geometric Scene Understanding for Autonomous Driving

Markus Schön, Michael Buchholz, Klaus Dietmayer
ICCV 2021poster

We introduce MGNet, a multi-task framework for monocular geometric scene understanding. We define monocular geometric scene understanding as the combination of two known tasks: Panoptic segmentation and self-supervised monocular depth estimation. Panoptic segmentation captures the full scene not onl…

Cited by 51PDF ↗code ↗Scholar ↗
2021

Online Extrinsic Calibration Based on Per-Sensor Ego-Motion Using Dual Quaternions

Markus Horn, Thomas Wodtko, Michael Buchholz, Klaus Dietmayer
RA-L 2021

In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globally optimal and a fast local approach along with a

Cited by 35Source ↗code ↗Scholar ↗
2021

The Radar Ghost Dataset – An Evaluation of Ghost Objects in Automotive Radar Data

Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer
IROS 2021poster

Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasoned by their high robustness against meteorological effects, such as rain, snow, or fog, and the radar’s ability to measur…

Cited by 23Source ↗code ↗Scholar ↗
2020

Inferring Spatial Uncertainty in Object Detection

Zining Wang, Di Feng, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer +2
IROS 2020poster

The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-prone annotation process or sensor observation noises, current object detection datasets only provide deterministic annotat…

Cited by 34Source ↗Scholar ↗
2020

Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks

Marcel Schreiber, Vasileios Belagiannis, Claudius Gläser, Klaus Dietmayer
ICRA 2020poster

In this work, we tackle the problem of modeling the vehicle environment as dynamic occupancy grid map in complex urban scenarios using recurrent neural networks. Dynamic occupancy grid maps represent the scene in a bird's eye view, where each grid cell contains the occupancy prob-ability and the two…

Cited by 27Source ↗Scholar ↗
2020

Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt +5
CVPR 2020poster

Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (NLOS) methods try to recover such hidden objects from their indirect reflections - faint signal components, traditionall…

Cited by 163PDF ↗code ↗Scholar ↗
2020

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer +1
CVPR 2020poster

The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fai…

Cited by 577PDF ↗code ↗Scholar ↗
2019

Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks

Marcel Schreiber, Stefan Hoermann, Klaus Dietmayer
ICRA 2019poster

We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs). A bird's eye view of the scene, including occupancy and velocity, is fed as a sequence to a RNN which is trained to predict futu…

Cited by 100Source ↗Scholar ↗
2018

Disparity Sliding Window: Object Proposals from Disparity Images

Julian Müller, Andreas Fregin, Klaus Dietmayer
IROS 2018poster

Sliding window approaches have been widely used for object recognition tasks in recent years [19], [4], [5], [18]. They guarantee an investigation of the entire input image for the object to be detected and allow a localization of that object. Despite the current trend towards deep neural networks,…

Cited by 16Source ↗code ↗Scholar ↗
2018

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Stefan Hoermann, Martin Bach, Klaus Dietmayer
ICRA 2018poster

Long-term situation prediction plays a crucial role for intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challe…

Cited by 223Source ↗Scholar ↗
2018

Fast Trajectory Planning for Automated Vehicles Using Gradient-Based Nonlinear Model Predictive Control

Franz Gritschneder, Knut Graichen, Klaus Dietmayer
IROS 2018poster

Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be so…

Cited by 21Source ↗Scholar ↗
2018

The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets

Andreas Fregin, Julian Muller, Ulrich Krebel, Klaus Dietmayer
ICRA 2018poster

Autonomous driving is a topic in computer vision which has captured a great deal of attention in recent years. One key problem is the detection and state analysis of traffic lights. Even over time, very few datasets for research in this topic have been published and they vary widely in quantity and…

Cited by 81Source ↗Scholar ↗
2017

Vehicle tracking using extended object methods: An approach for fusing radar and laser

Alexander Scheel, Stephan Reuter, Klaus Dietmayer
ICRA 2017poster

Combining data from heterogeneous sensors allows to enhance tracking systems by increasing the field of view, incorporating redundancy, and improving the performance by exploiting complementary sensor characteristics. This paper proposes a new vehicle tracking approach for vehicle environment percep…

Cited by 22Source ↗Scholar ↗