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Unsignalized intersections are typically considered as one of the mostrepresentative and challenging scenarios for self-driving vehicles. To tackleautonomous driving problems in such scenarios, this paper proposes a curriculumproximal policy optimization (CPPO) framework with stage-decaying clipping. Byadjusting the clipping parameter during different stages of training throughproximal policy optimization (PPO), the vehicle can first rapidly search for anapproximate optimal policy or its neighborhood with a large parameter, and thenconverges to the optimal policy with a small one. Particularly, the stage-basedcurriculum learning technology is incorporated into the proposed framework toimprove the generalization performance and further accelerate the trainingprocess. Moreover, the reward function is specially designed in view ofdifferent curriculum settings. A series of comparative experiments areconducted in intersection-crossing scenarios with bi-lane carriageways toverify the effectiveness of the proposed CPPO method. The results show that theproposed approach demonstrates better adaptiveness to different dynamic andcomplex environments, as well as faster training speed over baseline methods.

Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections