A survey of convolutional neural networks: analysis, applications, and prospects
A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …
learning field. Since CNN made impressive achievements in many areas, including but not …
[HTML][HTML] Autonomous driving architectures: insights of machine learning and deep learning algorithms
MR Bachute, JM Subhedar - Machine Learning with Applications, 2021 - Elsevier
Abstract Research in Autonomous Driving is taking momentum due to the inherent
advantages of autonomous driving systems. The main advantage being the disassociation …
advantages of autonomous driving systems. The main advantage being the disassociation …
Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection
This study addresses the issue of fusing infrared and visible images that appear differently
for object detection. Aiming at generating an image of high visual quality, previous …
for object detection. Aiming at generating an image of high visual quality, previous …
Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection
Autonomous driving faces great safety challenges for a lack of global perspective and the
limitation of long-range perception capabilities. It has been widely agreed that vehicle …
limitation of long-range perception capabilities. It has been widely agreed that vehicle …
Deep learning sensor fusion for autonomous vehicle perception and localization: A review
Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …
Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions
Z Liu, Y Cai, H Wang, L Chen, H Gao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Radar and camera information fusion sensing methods are used to solve the inherent
shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the …
shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the …
Mechanical neural networks: Architected materials that learn behaviors
RH Lee, EAB Mulder, JB Hopkins - Science Robotics, 2022 - science.org
Aside from some living tissues, few materials can autonomously learn to exhibit desired
behaviors as a consequence of prolonged exposure to unanticipated ambient loading …
behaviors as a consequence of prolonged exposure to unanticipated ambient loading …
Multi-sensor fusion in automated driving: A survey
Z Wang, Y Wu, Q Niu - Ieee Access, 2019 - ieeexplore.ieee.org
With the significant development of practicability in deep learning and the ultra-high-speed
information transmission rate of 5G communication technology will overcome the barrier of …
information transmission rate of 5G communication technology will overcome the barrier of …
Reconet: Recurrent correction network for fast and efficient multi-modality image fusion
Recent advances in deep networks have gained great attention in infrared and visible image
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …
fusion (IVIF). Nevertheless, most existing methods are incapable of dealing with slight …
A review of vehicle detection techniques for intelligent vehicles
Z Wang, J Zhan, C Duan, X Guan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Robust and efficient vehicle detection is an important task of environment perception of
intelligent vehicles, which directly affects the behavior decision-making and motion planning …
intelligent vehicles, which directly affects the behavior decision-making and motion planning …