Curriculum Reinforcement Learning for Autonomous Planning in Unprotected Left Turn Scenarios

In complex urban scenarios like intersections without dedicated left-turn signals, the construction of planning systems that maximize efficiency while guarantee safety has been a significant challenge. In this paper, we propose a reinforcement learning approach based on curriculum learning using real world dataset, and we develop a partial end-to-end planning and control model capable of adapting to variable temporal and spatial dimensional state inputs, applying it to autonomous driving task. Our model is compared with mainstream reinforcement learning algorithms to validate that our proposed algorithm can effectively solve complex spatio-temporal planning problems. This significantly enhances the efficiency of passing while maintaining a certain level of safety.