  Safely interacting with other traffic participants is one of the core
requirements for autonomous driving, especially in intersections and
occlusions. Most existing approaches are designed for particular scenarios and
require significant human labor in parameter tuning to be applied to different
situations. To solve this problem, we first propose a learning-based
Interaction Point Model (IPM), which describes the interaction between agents
with the protection time and interaction priority in a unified manner. We
further integrate the proposed IPM into a novel planning framework,
demonstrating its effectiveness and robustness through comprehensive
simulations in highly dynamic environments.
