Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …

Real-world problems often exhibit complex relationships and dependencies, which can be
effectively captured by graph learning systems. Graph attention networks (GATs) have …

With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

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 …

Self evolution refers to the ability of a system to evolve autonomously towards a better
performance, which is a potential trend for autonomous driving systems based on self …

On highways, the interaction with surrounding vehicles is very crucial for the decision-
making and planning of autonomous vehicles. However, the multi-modal driving intentions …

In complex urban traffic scenarios, autonomous vehicles face significant challenges in
adapting to diverse and dynamic traffic conditions. Reward-based reinforcement learning …

In this article, a motion planning for autonomous driving on highway is studied. A high-level
motion planning controller with discrete action space is designed based on deep Q network …

End-to-end autonomous driving has attracted great attentions in recent years. Compared to
traditional modular methods, end-to-end methods are more scalable in complex traffic …