Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …
order to achieve robust and accurate scene understanding, autonomous vehicles are …
A review and comparative study on probabilistic object detection in autonomous driving
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 …
recent years, deep learning has become the de-facto approach for object detection, and …
Bounding box regression with uncertainty for accurate object detection
Large-scale object detection datasets (eg, MS-COCO) try to define the ground truth
bounding boxes as clear as possible. However, we observe that ambiguities are still …
bounding boxes as clear as possible. However, we observe that ambiguities are still …
Lasernet: An efficient probabilistic 3d object detector for autonomous driving
In this paper, we present LaserNet, a computationally efficient method for 3D object
detection from LiDAR data for autonomous driving. The efficiency results from processing …
detection from LiDAR data for autonomous driving. The efficiency results from processing …
Fusion of 3D LIDAR and camera data for object detection in autonomous vehicle applications
It is vital that autonomous vehicles acquire accurate and real-time information about objects
in their vicinity, which fully guarantees the safety of the passengers and vehicle in various …
in their vicinity, which fully guarantees the safety of the passengers and vehicle in various …
Bayesod: A bayesian approach for uncertainty estimation in deep object detectors
A Harakeh, M Smart… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
When incorporating deep neural networks into robotic systems, a major challenge is the lack
of uncertainty measures associated with their output predictions. Methods for uncertainty …
of uncertainty measures associated with their output predictions. Methods for uncertainty …
Uncertainties in onboard algorithms for autonomous vehicles: Challenges, mitigation, and perspectives
K Yang, X Tang, J Li, H Wang, G Zhong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
Confidence-aware fusion using dempster-shafer theory for multispectral pedestrian detection
Multispectral pedestrian detection is an important and valuable task in many applications,
which could provide a more accurate and reliable pedestrian detection result by using the …
which could provide a more accurate and reliable pedestrian detection result by using the …
Glenet: Boosting 3d object detectors with generative label uncertainty estimation
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by
occlusions, signal missing, or manual annotation errors, can confuse deep 3D object …
occlusions, signal missing, or manual annotation errors, can confuse deep 3D object …
Uncertainty-aware learning for zero-shot semantic segmentation
Zero-shot semantic segmentation (ZSS) aims to classify pixels of novel classes without
training examples available. Recently, most ZSS methods focus on learning the visual …
training examples available. Recently, most ZSS methods focus on learning the visual …