30 citations · 54 across the 9 of their papers we have counts for
11 papers
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…
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
Michael Ulrich, Claudius Gläser, Fabian Timm
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class informa…
Holistic Filter Pruning for Efficient Deep Neural Networks
Lukas Enderich, Fabian Timm, Wolfram Burgard
Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization. Consequently, trained DNNs have…
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…
Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving
Thomas Michalke, Di Feng, Claudius Gläser +1
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS). When focusing on low-cost, large s…
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…
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization
Lukas Enderich, Fabian Timm, Wolfram Burgard
Deep neural networks (DNNs) have been proven to outperform classical methods on several machine learning benchmarks. However, they have high computational complexity and require po…
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…
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
Di Feng, Lars Rosenbaum, Claudius Glaeser +2
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving. Recently, many methods have been proposed to model uncertainties in deep learning base…
Learning Multimodal Fixed-Point Weights using Gradient Descent
Lukas Enderich, Fabian Timm, Lars Rosenbaum +1
Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-poin…
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
Di Feng, Lars Rosenbaum, Fabian Timm +1
We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss funct…