A review on autonomous vehicles: Progress, methods and challenges
Vehicular technology has recently gained increasing popularity, and autonomous driving is
a hot topic. To achieve safe and reliable intelligent transportation systems, accurate …
a hot topic. To achieve safe and reliable intelligent transportation systems, accurate …
Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
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 …
[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
A survey of deep rl and il for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
A review on explainable artificial intelligence for healthcare: why, how, and when?
S Bharati, MRH Mondal… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …
medicine. Concerns have been raised about the explainability of the decisions that are …
Decentralized federated learning for extended sensing in 6G connected vehicles
Research on smart connected vehicles has recently targeted the integration of vehicle-to-
everything (V2X) networks with Machine Learning (ML) tools and distributed decision …
everything (V2X) networks with Machine Learning (ML) tools and distributed decision …
[HTML][HTML] Optimizing student engagement in edge-based online learning with advanced analytics
Abstract Edge-Based Online Learning (EBOL), a technique that combines the practical,
hands-on approach of EBOL with the convenience of Online Learning (OL), is growing in …
hands-on approach of EBOL with the convenience of Online Learning (OL), is growing in …
A simple approach to improve single-model deep uncertainty via distance-awareness
Accurate uncertainty quantification is a major challenge in deep learning, as neural
networks can make overconfident errors and assign high confidence predictions to out-of …
networks can make overconfident errors and assign high confidence predictions to out-of …
Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms
In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social
media platforms, healthcare systems, etc., there is a lot of data online today. Machine …
media platforms, healthcare systems, etc., there is a lot of data online today. Machine …