Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning

Lane change in dense traffic is considered a challenging problem that typically requires the recognition of an opportune and appropriate opportunity 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 MPC in our framework is parameterized with augmented decision variables, where full-state references and regulatory factors concerning their relative importance are introduced. 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. Then the proposed framework is deployed to numerical simulations towards 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%.