  In urban environments, the complex and uncertain intersection scenarios are
challenging for autonomous driving. To ensure safety, it is crucial to develop
an adaptive decision making system that can handle the interaction with other
vehicles. Manually designed model-based methods are reliable in common
scenarios. But in uncertain environments, they are not reliable, so
learning-based methods are proposed, especially reinforcement learning (RL)
methods. However, current RL methods need retraining when the scenarios change.
In other words, current RL methods cannot reuse accumulated knowledge. They
forget learned knowledge when new scenarios are given. To solve this problem,
we propose a hierarchical framework that can autonomously accumulate and reuse
knowledge. The proposed method combines the idea of motion primitives (MPs)
with hierarchical reinforcement learning (HRL). It decomposes complex problems
into multiple basic subtasks to reduce the difficulty. The proposed method and
other baseline methods are tested in a challenging intersection scenario based
on the CARLA simulator. The intersection scenario contains three different
subtasks that can reflect the complexity and uncertainty of real traffic flow.
After offline learning and testing, the proposed method is proved to have the
best performance among all methods.
