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 …
Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving
To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
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 …
A comprehensive survey on vehicular networking: Communications, applications, challenges, and upcoming research directions
Nowadays, advanced communication technologies are being utilized to develop intelligent
transportation management and driving assistance. Through the ability to exchange traffic …
transportation management and driving assistance. Through the ability to exchange traffic …
Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer
End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …
(AVs) decision-making. However, the deployment of these technologies is usually …
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …
driving. However, the low sample efficiency and difficulty of designing reward functions for …
Federated learning in vehicular networks
Machine learning (ML) has recently been adopted in vehicular networks for applications
such as autonomous driving, road safety prediction and vehicular object detection, due to its …
such as autonomous driving, road safety prediction and vehicular object detection, due to its …
A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon
This paper proposes an innovative distributed longitudinal control strategy for connected
automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven …
automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven …