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Found 12 papers, 2 papers with code
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
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.
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
The proposed network exploits the specific characteristics of radar reflection data: It handles unordered lists of arbitrary length as input and it combines both extraction of local and global features.
Holistic Filter Pruning for Efficient Deep Neural Networks
Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization.
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving.
Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS).
Inferring Spatial Uncertainty in Object Detection
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.
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization
We propose SYMOG (symmetric mixture of Gaussian modes), which significantly decreases the complexity of DNNs through low-bit fixed-point quantization.
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
This work presents a probabilistic deep neural network that combines LiDAR point clouds and RGB camera images for robust, accurate 3D object detection.
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving.
Learning Multimodal Fixed-Point Weights using Gradient Descent
Due to their high computational complexity, deep neural networks are still limited to powerful processing units.
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving.
Robotics
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
We validate our method on the KITTI object detection benchmark.
Robotics
