[HTML][HTML] Machine learning-based approach: Global trends, research directions, and regulatory standpoints
R Pugliese, S Regondi, R Marini - Data Science and Management, 2021 - Elsevier
The field of machine learning (ML) is sufficiently young that it is still expanding at an
accelerating pace, lying at the crossroads of computer science and statistics, and at the core …
accelerating pace, lying at the crossroads of computer science and statistics, and at the core …
[HTML][HTML] A systematic study on reinforcement learning based applications
K Sivamayil, E Rajasekar, B Aljafari, S Nikolovski… - Energies, 2023 - mdpi.com
We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
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 …
Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning
Today's self-driving vehicles have achieved impressive driving capabilities, but still suffer
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …
Uncertainty-aware decision-making for autonomous driving at uncontrolled intersections
Reinforcement learning (RL) has been widely used in the decision-making of autonomous
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …
PNNUAD: Perception neural networks uncertainty aware decision-making for autonomous vehicle
Most environment perception methods in autonomous vehicles rely on deep neural
networks because of their impressive performance. However, neural networks have black …
networks because of their impressive performance. However, neural networks have black …
System and experiments of model-driven motion planning and control for autonomous vehicles
This article presents a model-based motion planning and control system for autonomous
vehicles and its experimental validation. The system consists of four modules: 1) global …
vehicles and its experimental validation. The system consists of four modules: 1) global …
Graph attention mechanism based reinforcement learning for multi-agent flocking control in communication-restricted environment
J Xiao, G Yuan, J He, K Fang, Z Wang - Information Sciences, 2023 - Elsevier
To solve the poor performance of reinforcement learning (RL) in the multi-agent flocking
cooperative control under the communication-restricted environments, we propose a multi …
cooperative control under the communication-restricted environments, we propose a multi …
Robust multiagent reinforcement learning toward coordinated decision-making of automated vehicles
Automated driving is essential for developing and deploying intelligent transportation
systems. However, unavoidable sensor noises or perception errors may cause an …
systems. However, unavoidable sensor noises or perception errors may cause an …