Radars for autonomous driving: A review of deep learning methods and challenges
A Srivastav, S Mandal - IEEE Access, 2023 - ieeexplore.ieee.org
Radar is a key component of the suite of perception sensors used for safe and reliable
navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity …
navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity …
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 …
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 …
Toward ensuring safety for autonomous driving perception: standardization progress, research advances, and perspectives
Perception systems play a crucial role in autonomous driving by reading the sensory data
and providing meaningful interpretation of the operating environment for decision-making …
and providing meaningful interpretation of the operating environment for decision-making …
Uncertainty quantification of collaborative detection for self-driving
Sharing information between connected and autonomous vehicles (CAVs) fundamentally
improves the performance of collaborative object detection for self-driving. However, CAVs …
improves the performance of collaborative object detection for self-driving. However, CAVs …
Stabilizing multispectral pedestrian detection with evidential hybrid fusion
Multispectral pedestrian detection is an important task due to its critical role in a wide
spectrum of applications. Basically, the complementary information from color and thermal …
spectrum of applications. Basically, the complementary information from color and thermal …
Multivariate probabilistic monocular 3D object detection
In autonomous driving, monocular 3D object detection is an important but challenging task.
Towards accurate monocular 3D object detection, some recent methods recover the …
Towards accurate monocular 3D object detection, some recent methods recover the …
Quantification of uncertainty and its applications to complex domain for autonomous vehicles perception system
K Wang, Y Wang, B Liu, J Chen - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Over the last decades, deep neural networks (DNNs) have penetrated all fields of science
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …
[HTML][HTML] Generating evidential bev maps in continuous driving space
Safety is critical for autonomous driving, and one aspect of improving safety is to accurately
capture the uncertainties of the perception system, especially knowing the unknown …
capture the uncertainties of the perception system, especially knowing the unknown …
Safe motion planning for autonomous vehicles by quantifying uncertainties of deep learning-enabled environment perception
Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep
learning (DL) techniques that typically generate deterministic outputs without explicitly …
learning (DL) techniques that typically generate deterministic outputs without explicitly …