Vision-based robust control framework based on deep reinforcement learning applied to autonomous ground vehicles
GAP de Morais, LB Marcos, JNAD Bueno… - Control Engineering …, 2020 - Elsevier
Given the recent advances in computer vision, image processing and control systems, self-
driving vehicles has been one of the most promising and challenging research topics …
driving vehicles has been one of the most promising and challenging research topics …
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …
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 …
Driving decision and control for automated lane change behavior based on deep reinforcement learning
To fulfill high-level automation, an automated vehicle needs to learn to make decisions and
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
Machine learning techniques in ADAS: A review
A Moujahid, MEA Tantaoui, MD Hina… - … on Advances in …, 2018 - ieeexplore.ieee.org
What machine learning (ML) technique is used for system intelligence implementation in
ADAS (advanced driving assistance system)? This paper tries to answer this question. This …
ADAS (advanced driving assistance system)? This paper tries to answer this question. This …
A survey of deep learning applications to autonomous vehicle control
Designing a controller for autonomous vehicles capable of providing adequate performance
in all driving scenarios is challenging due to the highly complex environment and inability to …
in all driving scenarios is challenging due to the highly complex environment and inability to …
[HTML][HTML] A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles
P Mei, HR Karimi, H Xie, F Chen, C Huang… - … Applications of Artificial …, 2023 - Elsevier
Considering the importance of the energy management strategy for hybrid electric vehicles,
this paper is aiming at addressing the energy optimization control issue using reinforcement …
this paper is aiming at addressing the energy optimization control issue using reinforcement …
A deep reinforcement learning strategy for UAV autonomous landing on a moving platform
The use of multi-rotor UAVs in industrial and civil applications has been extensively
encouraged by the rapid innovation in all the technologies involved. In particular, deep …
encouraged by the rapid innovation in all the technologies involved. In particular, deep …
Deep reinforcement learning enabled self-learning control for energy efficient driving
To address the air pollution problems and reduce greenhouse gas emissions (GHG), plug-in
hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The …
hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The …
Human-like autonomous vehicle speed control by deep reinforcement learning with double Q-learning
Y Zhang, P Sun, Y Yin, L Lin… - 2018 IEEE intelligent …, 2018 - ieeexplore.ieee.org
Autonomous driving has become a popular research project. How to control vehicle speed is
a core problem in autonomous driving. Automatic decision-making approaches, such as …
a core problem in autonomous driving. Automatic decision-making approaches, such as …