  Coping with intensively interactive scenarios is one of the significant
challenges in the development of autonomous driving. Reinforcement learning
(RL) offers an ideal solution for such scenarios through its self-evolution
mechanism via interaction with the environment. However, the lack of sufficient
safety mechanisms in common RL leads to the fact that agent often find it
difficult to interact well in highly dynamic environment and may collide in
pursuit of short-term rewards. Much of the existing safe RL methods require
environment modeling to generate reliable safety boundaries that constrain
agent behavior. Nevertheless, acquiring such safety boundaries is not always
feasible in dynamic environments. Inspired by the driver's behavior of acting
when uncertainty is minimal, this study introduces the concept of action timing
to replace explicit safety boundary modeling. We define "actor" as an agent to
decide optimal action at each step. By imaging the actor take opportunity to
act as a timing-dependent gradual process, the other agent called "timing
taker" can evaluate the optimal action execution time, and relate the optimal
timing to each action moment as a dynamic safety factor to constrain the
actor's action. In the experiment involving a complex, unsignaled intersection
interaction, this framework achieved superior safety performance compared to
all benchmark models.
