A review of vision-based traffic semantic understanding in ITSs
J Chen, Q Wang, HH Cheng, W Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …
situations and emergencies more accurately and provide a more accurate basis for anomaly …
Towards deep radar perception for autonomous driving: Datasets, methods, and challenges
With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …
The next generation of 4D radar can achieve imaging capability in the form of high …
Milestones in autonomous driving and intelligent vehicles: Survey of surveys
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …
due to the convenience, safety, and economic benefits. Although a number of surveys have …
Domain adaptive object detection for autonomous driving under foggy weather
Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …
distribution between training and testing data, which is not always the case when weathers …
An improved algorithm for small object detection based on YOLO v4 and multi-scale contextual information
SJ Ji, QH Ling, F Han - Computers and Electrical Engineering, 2023 - Elsevier
In real life, object detection is widely applied and plays a significant part in the field of
computer vision. However, when detecting small objects, the advanced You Only Look Once …
computer vision. However, when detecting small objects, the advanced You Only Look Once …
Monte Carlo DropBlock for modeling uncertainty in object detection
SH Yelleni, D Kumari, PK Srijith - Pattern Recognition, 2024 - Elsevier
With the advancements made in deep learning, computer vision problems have seen a great
improvement in performance. However, in many real-world applications such as …
improvement in performance. However, in many real-world applications such as …
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 …
Monte carlo dropblock for modelling uncertainty in object detection
K Deepshikha, SH Yelleni, PK Srijith… - arXiv preprint arXiv …, 2021 - arxiv.org
With the advancements made in deep learning, computer vision problems like object
detection and segmentation have seen a great improvement in performance. However, in …
detection and segmentation have seen a great improvement in performance. However, in …
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 …