Lane change in dense traffic is considered a challenging problem that typically requires the recognization of an opportune and appropriate time for maneuvers. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). The embodied high-level MPC in our proposed framework is parameterized with augmented decision variables, where full-state references and regulatory factors concerning their importance are introduced. In this sense, improved adaptiveness to dense and dynamic environments with high complexity is exhibited. Furthermore, to improve the convergence speed and ensure a high-quality policy, effective curriculum design is integrated into the reinforcement learning (RL) framework with policy transfer and enhancement. With comprehensive experiments towards the chance-aware lane-change scenario, accelerated convergence speed and improved reward performance are demonstrated through comparisons with representative baseline methods. It is noteworthy that, given a narrow chance in the dense and dynamic traffic flow, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate.

翻译：密集交通中的换道被认为是一项具有挑战性的问题，通常需要识别出机动操作的最佳时机。本文通过课程强化学习，提出了一种结合高层模型预测控制的感知机会换道策略。所提框架中的高层模型预测控制通过引入带增广决策变量的参数化设计，并加入全状态参考及其重要性相关的调节因子，从而在高度复杂、密集且动态的环境中展现出更强的适应性。此外，为了提升收敛速度并确保策略质量，我们在强化学习框架中集成了有效的课程设计，并辅以策略迁移与增强。针对感知机会换道场景的综合实验表明，与代表性基线方法相比，所提方法具有更快的收敛速度和更优的奖励性能。值得注意的是，在密集动态交通流中仅存在狭窄机会的情况下，所提方法能够生成高质量的换道操作，使车辆以较高的成功率并入交通流。