1.4k citations · 3k across the 64 of their papers we have counts for
13 papers · 1 filter
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…
Dynamic Occupancy Grid Mapping with Recurrent Neural Networks
Marcel Schreiber, Vasileios Belagiannis, Claudius Gläser +1
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 ro…
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…
Using Machine Learning to Detect Ghost Images in Automotive Radar
Florian Kraus, Nicolas Scheiner, Werner Ritter +1
Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or…
DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
Markus Horn, Nico Engel, Vasileios Belagiannis +2
This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping da…
Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic
Thomas Griebel, Johannes Müller, Michael Buchholz +1
Self-assessment is a key to safety and robustness in automated driving. In order to design safer and more robust automated driving functions, the goal is to self-assess the perform…
Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation
Andreas Pfeuffer, Klaus Dietmayer
Computer vision tasks such as semantic segmentation perform very well in good weather conditions, but if the weather turns bad, they have problems to achieve this performance in th…
Uncertainty depth estimation with gated images for 3D reconstruction
Stefanie Walz, Tobias Gruber, Werner Ritter +1
Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence. It has been shown that this technology…
Extended Existence Probability Using Digital Maps for Object Verification
Fabian Gies, Joachim Posselt, Michael Buchholz +1
A main task for automated vehicles is an accurate and robust environment perception. Especially, an error-free detection and modeling of other traffic participants is of great impo…
Inferring Spatial Uncertainty in Object Detection
Zining Wang, Di Feng, Yiyang Zhou +5
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-pro…
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
Di Feng, Yifan Cao, Lars Rosenbaum +2
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection. We explicitly model uncertai…