DRL‐based intelligent resource allocation for diverse QoS in 5G and toward 6G vehicular networks: a comprehensive survey
The vehicular network is taking great attention from both academia and industry to enable
the intelligent transportation system (ITS), autonomous driving, and smart cities. The system …
the intelligent transportation system (ITS), autonomous driving, and smart cities. The system …
Joint optimization of sensing, decision-making and motion-controlling for autonomous vehicles: A deep reinforcement learning approach
The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …
controlling, have been studied separately in most existing works on autonomous driving …
End-to-end autonomous driving with semantic depth cloud mapping and multi-agent
Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we
propose a novel deep learning model trained with end-to-end and multi-task learning …
propose a novel deep learning model trained with end-to-end and multi-task learning …
A security-by-design decision-making model for risk management in autonomous vehicles
M Abdel-Basset, A Gamal, N Moustafa… - IEEE …, 2021 - ieeexplore.ieee.org
Autonomous/self-driving vehicles have gained significant attention these days, as one of the
intelligent transportation systems. However, those vehicles have risks related to their …
intelligent transportation systems. However, those vehicles have risks related to their …
[HTML][HTML] Supervised-learning-based hour-ahead demand response for a behavior-based home energy management system approximating MILP optimization
The demand response (DR) program of a traditional home energy management system
(HEMS) usually controls or schedules appliances to monitor energy usage, minimize energy …
(HEMS) usually controls or schedules appliances to monitor energy usage, minimize energy …
Lexicographic actor-critic deep reinforcement learning for urban autonomous driving
Urban autonomous driving is a difficult task because of its complex road scenarios and the
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …
Cognitive conformal antenna array exploiting deep reinforcement learning method
A cognitive antenna array, which is designed by using deep reinforcement learning (DRL) is
proposed in this article to adapt to the complex electromagnetic environment. Specifically …
proposed in this article to adapt to the complex electromagnetic environment. Specifically …
Interval-valued fermatean fuzzy based risk assessment for self-driving vehicles
M Kirişci - Applied Soft Computing, 2024 - Elsevier
The decision-making (DM) processes used by autonomous vehicle driving systems are
separate from those of the users, allowing them to oversee and regulate the operations of …
separate from those of the users, allowing them to oversee and regulate the operations of …
Automatic control of computer application data processing system based on artificial intelligence
To shorten the travel time and improve comfort, the automatic train driving system is
considered to replace manual driving. In this article, an automatic control method of …
considered to replace manual driving. In this article, an automatic control method of …
Reinforcement learning in few-shot scenarios: A survey
Z Wang, Q Fu, J Chen, Y Wang, Y Lu, H Wu - Journal of Grid Computing, 2023 - Springer
Reinforcement learning has a demand for massive data in complex problems, which makes
it infeasible to be applied to real cases where sampling is difficult. The key to coping with …
it infeasible to be applied to real cases where sampling is difficult. The key to coping with …