Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
Deep learning-based complete coverage path planning with re-joint and obstacle fusion paradigm
With the introduction of autonomy into the precision agriculture process, environmental
exploration, disaster response, and other fields, one of the global demands is to navigate …
exploration, disaster response, and other fields, one of the global demands is to navigate …
Visual detection of road cracks for autonomous vehicles based on deep learning
I Meftah, J Hu, MA Asham, A Meftah, L Zhen, R Wu - Sensors, 2024 - mdpi.com
Detecting road cracks is essential for inspecting and assessing the integrity of concrete
pavement structures. Traditional image-based methods often require complex …
pavement structures. Traditional image-based methods often require complex …
A maneuver-based urban driving dataset and model for cooperative vehicle applications
Short-term future of automated driving can be imagined as a hybrid scenario in which both
automated and human-driven vehicles co-exist in the same environment. In order to address …
automated and human-driven vehicles co-exist in the same environment. In order to address …
Deep CNN-based autonomous system for safety measures in logistics transportation
The careless activity of drivers in logistics transportation is a primary reason inside the
vehicle during road accidents. This research aims to reduce the number of accidents caused …
vehicle during road accidents. This research aims to reduce the number of accidents caused …
Towards learning generalizable driving policies from restricted latent representations
Training intelligent agents that can drive autonomously in various urban and highway
scenarios has been a hot topic in the robotics society within the last decades. However, the …
scenarios has been a hot topic in the robotics society within the last decades. However, the …
Deep learning based 3d brain tumor segmentation with multispectral mri
With recent advancements in computer vision and in deep learning, the medical image
processing research has gained a momentum in many medical image applications. One …
processing research has gained a momentum in many medical image applications. One …
Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model
In the evolving field of Explainable AI (XAI), interpreting the decisions of deep neural
networks (DNNs) in computer vision tasks is an important process. While pixel-based XAI …
networks (DNNs) in computer vision tasks is an important process. While pixel-based XAI …
Segmentation of covid-19 infected lung area in ct scans with deep algorithms
COVID-19 announced as the pandemic on January 2020 has infected millions of people
worldwide ever since. Caused by SARS-CoV-2, COVID-19 infection can cause inflammation …
worldwide ever since. Caused by SARS-CoV-2, COVID-19 infection can cause inflammation …
Using different loss functions with YOLACT++ for real-time instance segmentation
S Koles, S Karakas, AP Ndigande… - 2023 46th International …, 2023 - ieeexplore.ieee.org
In this paper, we study and analyze the performance of various loss functions on a recently
proposed real-time instance segmentation algorithm, YOLACT++. In particular, we study the …
proposed real-time instance segmentation algorithm, YOLACT++. In particular, we study the …