In the realm of autonomous vehicular systems, there has been a notable increase in end-to-
end algorithms designed for complete self-navigation. Researchers are increasingly …

In this paper, we explore the challenges associated with navigating complex T-intersections
in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms …

In this paper, we propose an approach for making hierarchical reinforcement learning
practical for autonomous driving on multi-lane highway or urban structured roads. While this …

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …

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

Reinforcement learning-based techniques, empowered by deep-structured neural nets,
have demonstrated superiority over rule-based methods in terms of making high-level …

Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …

Recent revolutionary advances in cognitive science using the learning principles of
biological brains and human cognition have fuelled artificial intelligence (AI), in particular …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …

Although deep reinforcement learning (DRL) has shown promising results for autonomous
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …