1.4k citations · 3k across the 64 of their papers we have counts for
45 papers · 1 filter
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…
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…
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…
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…
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…
Anomaly Detection in Radar Data Using PointNets
Thomas Griebel, Dominik Authaler, Markus Horn +3
For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other han…
Fast Rule-Based Clutter Detection in Automotive Radar Data
Johannes Kopp, Dominik Kellner, Aldi Piroli +1
Automotive radar sensors output a lot of unwanted clutter or ghost detections, whose position and velocity do not correspond to any real object in the sensor's field of view. This…
GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies
Carsten Ditzel, Klaus Dietmayer
Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar…
Attention-based Vehicle Self-Localization with HD Feature Maps
Nico Engel, Vasileios Belagiannis, Klaus Dietmayer
We present a vehicle self-localization method using point-based deep neural networks. Our approach processes measurements and point features, i.e. landmarks, from a high-definition…
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
Markus Horn, Ole Schumann, Markus Hahn +2
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like ca…
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
Di Feng, Zining Wang, Yiyang Zhou +5
The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity…
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Di Feng, Ali Harakeh, Steven Waslander +1
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and ma…
Point Transformer
Nico Engel, Vasileios Belagiannis, Klaus Dietmayer
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and…
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
Di Feng, Lars Rosenbaum, Fabian Timm +1
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive prob…