[HTML][HTML] Autonomous vehicle decision-making and control in complex and unconventional scenarios—a review
The development of autonomous vehicles (AVs) is becoming increasingly important as the
need for reliable and safe transportation grows. However, in order to achieve level 5 …
need for reliable and safe transportation grows. However, in order to achieve level 5 …
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 …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Confidence-aware reinforcement learning for energy management of electrified vehicles
The reliability of data-driven techniques, such as deep reinforcement learning (DRL)
frequently diminishes in scenarios beyond their training environments. Despite DRL-based …
frequently diminishes in scenarios beyond their training environments. Despite DRL-based …
Deep reinforcement learning for smart grid operations: Algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
Personalized car-following control based on a hybrid of reinforcement learning and supervised learning
D Song, B Zhu, J Zhao, J Han… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of intelligent vehicles, more research has focused on achieving
human-like driving. As an important component of intelligent vehicle control, car-following …
human-like driving. As an important component of intelligent vehicle control, car-following …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
Efficient reinforcement learning for autonomous driving with parameterized skills and priors
When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …
diverse driving situations. Many manually designed driving policies are difficult to scale to …
Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving
Decision-making for urban autonomous driving is challenging due to the stochastic nature of
interactive traffic participants and the complexity of road structures. Although reinforcement …
interactive traffic participants and the complexity of road structures. Although reinforcement …
Recoat: A deep learning-based framework for multi-modal motion prediction in autonomous driving application
This paper proposes a novel deep learning framework for multi-modal motion prediction.
The framework consists of three parts: recurrent neural network to process target agent's …
The framework consists of three parts: recurrent neural network to process target agent's …