Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
[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 …
Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach
Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex
decision making behaviors through interacting with other traffic participants. However, many …
decision making behaviors through interacting with other traffic participants. However, many …
Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning
Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing
methods to address these problems are time-consuming and not adaptive to changing …
methods to address these problems are time-consuming and not adaptive to changing …
Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Highway decision-making and motion planning for autonomous driving via soft actor-critic
In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
[PDF][PDF] An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: Cooperative velocity and lane-changing control
H Ding, W Li, N Xu, J Zhang - Journal of Intelligent and …, 2022 - ieeexplore.ieee.org
Purpose-This study aims to propose an enhanced eco-driving strategy based on
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …
Convolutional neural network-based lane-change strategy via motion image representation for automated and connected vehicles
The lane-change decision-making module of automated and connected vehicles (ACVs) is
one of the most crucial and challenging issues to be addressed. Motivated by human beings' …
one of the most crucial and challenging issues to be addressed. Motivated by human beings' …