  Autonomous driving at intersections is one of the most complicated and
accident-prone traffic scenarios, especially with mixed traffic participants
such as vehicles, bicycles and pedestrians. The driving policy should make safe
decisions to handle the dynamic traffic conditions and meet the requirements of
on-board computation. However, most of the current researches focuses on
simplified intersections considering only the surrounding vehicles and
idealized traffic lights. This paper improves the integrated decision and
control framework and develops a learning-based algorithm to deal with complex
intersections with mixed traffic flows, which can not only take account of
realistic characteristics of traffic lights, but also learn a safe policy under
different safety constraints. We first consider different velocity models for
green and red lights in the training process and use a finite state machine to
handle different modes of light transformation. Then we design different types
of distance constraints for vehicles, traffic lights, pedestrians, bicycles
respectively and formulize the constrained optimal control problems (OCPs) to
be optimized. Finally, reinforcement learning (RL) with value and policy
networks is adopted to solve the series of OCPs. In order to verify the safety
and efficiency of the proposed method, we design a multi-lane intersection with
the existence of large-scale mixed traffic participants and set practical
traffic light phases. The simulation results indicate that the trained decision
and control policy can well balance safety and tracking performance. Compared
with model predictive control (MPC), the computational time is three orders of
magnitude lower.
