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Found 19 papers, 4 papers with code
FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View
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.
Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving
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.
Efficiency in Multiple-Type Housing Markets
For multiple-type housing markets with strict preferences, our characterization of bTTC constitutes the first characterization of an extension of the prominent TTC mechanism
Priors are Powerful: Improving a Transformer for Multi-camera 3D Detection with 2D Priors
Transfomer-based approaches advance the recent development of multi-camera 3D detection both in academia and industry.
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection.
Understanding the Domain Gap in LiDAR Object Detection Networks
In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world.
On the Asymptotic Performance of Affirmative Actions in School Choice
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.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.
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.
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets.
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.
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.
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
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Training a deep object detector for autonomous driving requires a huge amount of labeled data.
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
We validate our method on the KITTI object detection benchmark.
Robotics
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
Experimental results show that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion.
