Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
[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 …
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
Driving safety is the most important element that needs to be considered for autonomous
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …
recent years related to several topics as sensor technologies, V2X communications, safety …
Application of machine learning in intelligent fish aquaculture: A review
Among the background of developments in automation and intelligence, machine learning
technology has been extensively applied in aquaculture in recent years, providing a new …
technology has been extensively applied in aquaculture in recent years, providing a new …
Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs)
longitudinal control for a mixed connected and automated traffic environment based on deep …
longitudinal control for a mixed connected and automated traffic environment based on deep …
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …
learning (RL). To optimize driving performance, a reward function is developed by …
Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels
In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
[HTML][HTML] An autonomous path planning model for unmanned ships based on deep reinforcement learning
S Guo, X Zhang, Y Zheng, Y Du - Sensors, 2020 - mdpi.com
Deep reinforcement learning (DRL) has excellent performance in continuous control
problems and it is widely used in path planning and other fields. An autonomous path …
problems and it is widely used in path planning and other fields. An autonomous path …
[HTML][HTML] Trajectory data-based traffic flow studies: A revisit
In this paper, we review trajectory data-based traffic flow studies that have been conducted
over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest …
over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest …