1.4k citations · 3k across the 64 of their papers we have counts for
81 papers
Group Regression for Query Based Object Detection and Tracking
Felicia Ruppel, Florian Faion, Claudius Gläser +1
Group regression is commonly used in 3D object detection to predict box parameters of similar classes in a joint head, aiming to benefit from similarities while separating highly d…
User Feedback and Sample Weighting for Ill-Conditioned Hand-Eye Calibration
Markus Horn, Thomas Wodtko, Michael Buchholz +1
Hand-eye calibration is an important and extensively researched method for calibrating rigidly coupled sensors, solely based on estimates of their motion. Due to the geometric stru…
Joint Out-of-Distribution Detection and Uncertainty Estimation for Trajectory Prediction
Julian Wiederer, Julian Schmidt, Ulrich Kressel +2
Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we…
Advancing Frame-Dropping in Multi-Object Tracking-by-Detection Systems Through Event-Based Detection Triggering
Matti Henning, Michael Buchholz, Klaus Dietmayer
With rising computational requirements modern automated vehicles (AVs) often consider trade-offs between energy consumption and perception performance, potentially jeopardizing the…
Energy-based Detection of Adverse Weather Effects in LiDAR Data
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliabili…
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
Adrian Holzbock, Achyut Hegde, Klaus Dietmayer +1
Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is nor…
RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation
Markus Schön, Michael Buchholz, Klaus Dietmayer
Panoptic segmentation is one of the most challenging scene parsing tasks, combining the tasks of semantic segmentation and instance segmentation. While much progress has been made,…
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
Julian Schmidt, Pascal Huissel, Julian Wiederer +3
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estim…
The Impact of Frame-Dropping on Performance and Energy Consumption for Multi-Object Tracking
Matti Henning, Michael Buchholz, Klaus Dietmayer
The safety of automated vehicles (AVs) relies on the representation of their environment. Consequently, state-of-the-art AVs employ potent sensor systems to achieve the best possib…
Exploring Navigation Maps for Learning-Based Motion Prediction
Julian Schmidt, Julian Jordan, Franz Gritschneder +2
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 (H…
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht +5
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to enco…
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Felicia Ruppel, Florian Faion, Claudius Gläser +1
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving. One unique characteristic of these models is that attention…
A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories
Julian Wiederer, Julian Schmidt, Ulrich Kressel +2
Human intuition allows to detect abnormal driving scenarios in situations they never experienced before. Like humans detect those abnormal situations and take countermeasures to pr…
Situation-Aware Environment Perception for Decentralized Automation Architectures
Matti Henning, Michael Buchholz, Klaus Dietmayer
Advances in the field of environment perception for automated agents have resulted in an ongoing increase in generated sensor data. The available computational resources to process…
Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds
Aldi Piroli, Vinzenz Dallabetta, Marc Walessa +3
LiDAR sensors used in autonomous driving applications are negatively affected by adverse weather conditions. One common, but understudied effect, is the condensation of vehicle gas…
Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception
Matti Henning, Jan Strohbeck, Michael Buchholz +1
The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by t…
MotionMixer: MLP-based 3D Human Body Pose Forecasting
Arij Bouazizi, Adrian Holzbock, Ulrich Kressel +2
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 b…
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
Markus Schön, Michael Buchholz, Klaus Dietmayer
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: Pan…
Self-Assessment for Single-Object Tracking in Clutter Using Subjective Logic
Thomas Griebel, Johannes Müller, Paul Geisler +4
Reliable tracking algorithms are essential for automated driving. However, the existing consistency measures are not sufficient to meet the increasing safety demands in the automot…
MEAT: Maneuver Extraction from Agent Trajectories
Julian Schmidt, Julian Jordan, David Raba +2
Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction…
Transformers for Multi-Object Tracking on Point Clouds
Felicia Ruppel, Florian Faion, Claudius Gläser +1
We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and…
Robust 3D Object Detection in Cold Weather Conditions
Aldi Piroli, Vinzenz Dallabetta, Marc Walessa +3
Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditio…
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Adrian Holzbock, Alexander Tsaregorodtsev, Youssef Dawoud +2
Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, key…
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention
Julian Schmidt, Julian Jordan, Franz Gritschneder +1
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…