no code implementations • 5 Mar 2024 • Jiawei Hou, Xiaoyan Li, Wenhao Guan, Gang Zhang, Di Feng, Yuheng Du, xiangyang xue, Jian Pu
In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird's-eye view (BEV) semantic segmentation.
no code implementations • 13 Oct 2023 • Feng Jiang, Chaoping Tu, Gang Zhang, Jun Li, Hanqing Huang, Junyu Lin, Di Feng, Jian Pu
LiDAR and camera are two critical sensors for multi-modal 3D semantic segmentation and are supposed to be fused efficiently and robustly to promise safety in various real-world scenarios.
no code implementations • 29 Aug 2023 • Di Feng
For multiple-type housing markets with strict preferences, our characterization of bTTC constitutes the first characterization of an extension of the prominent TTC mechanism
no code implementations • 31 Jan 2023 • Di Feng, Francesco Ferroni
Transfomer-based approaches advance the recent development of multi-camera 3D detection both in academia and industry.
no code implementations • 26 Sep 2022 • Florian Drews, Di Feng, Florian Faion, Lars Rosenbaum, Michael Ulrich, Claudius Gläser
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection.
no code implementations • 21 Apr 2022 • Jasmine Richter, Florian Faion, Di Feng, Paul Benedikt Becker, Piotr Sielecki, Claudius Glaeser
In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world.
no code implementations • 8 Feb 2022 • Di Feng, Yun Liu
Given the possible preference manipulations under the IAM, we characterize the asymptotically equivalent sets of Nash equilibrium outcomes of the IAM with these two affirmative actions.
1 code implementation • 6 Mar 2021 • Di Feng, Yiyang Zhou, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.
no code implementations • 18 Dec 2020 • Di Feng, Zining Wang, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer, Masayoshi Tomizuka, Wei Zhan
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
1 code implementation • 20 Nov 2020 • Di Feng, Ali Harakeh, Steven Waslander, Klaus Dietmayer
Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets.
no code implementations • 10 Aug 2020 • Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving.
1 code implementation • 16 Apr 2020 • Thomas Michalke, Di Feng, Claudius Gläser, Fabian Timm
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS).
no code implementations • 7 Mar 2020 • Zining Wang, Di Feng, Yiyang Zhou, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer, Masayoshi Tomizuka, Wei Zhan
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
no code implementations • 1 Feb 2020 • Di Feng, Yifan Cao, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection.
no code implementations • 26 Sep 2019 • Di Feng, Lars Rosenbaum, Claudius Glaeser, Fabian Timm, Klaus Dietmayer
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving.
1 code implementation • 21 Feb 2019 • Di Feng, Christian Haase-Schuetz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck, Klaus Dietmayer
This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving.
Robotics
no code implementations • 29 Jan 2019 • Di Feng, Xiao Wei, Lars Rosenbaum, Atsuto Maki, Klaus Dietmayer
Training a deep object detector for autonomous driving requires a huge amount of labeled data.
no code implementations • 14 Sep 2018 • Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
We validate our method on the KITTI object detection benchmark.
Robotics
no code implementations • 13 Apr 2018 • Di Feng, Lars Rosenbaum, Klaus Dietmayer
Experimental results show that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion.