Autonomous Vehicles (AVs) have attracted significant attention in recent years and
Reinforcement Learning (RL) has shown remarkable performance in improving the …

It is still a challenging topic to make reactive driving behaviors in complex urban
environments as road users' intentions are unknown. Model-based reinforcement learning …

The autonomous driving algorithm studied in this paper makes a ground vehicle capable of
sensing its environment via visual images and moving safely with little or no human input …

Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning
useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly …

Multimodal remote sensing data classification can enhance a model's ability to distinguish
land features through multimodal data fusion. In this context, how to help models understand …

Trajectory planning method is a research hotspot in autonomous driving. Existing
reinforcement learning-based trajectory planning methods suffer from unstable performance …

Research on the shared control system for human-in-the-loop cooperative driving has grown
steadily in the past decade. However, most proposed methodologies were focused on …

In modern model-based control frameworks such as model predictive control or model-
based reinforcement learning, machine learning has become a ubiquitous class of …

Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency
compared to model-free RL by utilizing a virtual environment model. However, it is …

Hierarchical reinforcement learning excels at dividing difficult task goals into easily
achievable subgoals. It provides an effective means to solve long-horizon planning tasks …