Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability.

翻译：密集交通中的车道变更通常需要识别合适的操作时机，这仍是自动驾驶领域的难题。本文提出一种基于课程强化学习的高层模型预测控制（MPC）机会感知车道变更策略。在所提出的框架中，通过神经策略生成全状态参考量以及反映MPC中各成本项相对重要性的调节因子。此外，为提升收敛速度并确保策略质量，我们设计并整合了含策略迁移与增强的阶段性强化学习（RL）框架中的有效课程。该框架在密集动态交通的数值仿真中进行了部署与评估。值得注意的是，即使面临狭窄变更机会，所提方法仍能生成高质量车道变更操作，使车辆以96%的高成功率并入车流。最终，该框架在高保真度模拟器中通过密集交通场景验证，展现了良好的实用性与泛化能力。