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 …

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 …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

The reliability of data-driven techniques, such as deep reinforcement learning (DRL)
frequently diminishes in scenarios beyond their training environments. Despite DRL-based …

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 …

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 …

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 …

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 …

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 …

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 …