Cloud-Assisted Nonlinear Model Predictive Control with Deep Reinforcement Learning for Autonomous Vehicle Path Tracking

Yuxuan Zhang, Bing Chen, Yan Wang, Nan Li

Model Predictive Control (MPC) stands out as a prominent method for achieving optimal control in autonomous driving applications. However, the effectiveness of MPC approaches critically depends on the availability of accurate dynamic models and often necessitates substantial computational overhead for real-time optimization procedures at every iteration. Recently, the research community has been increasingly drawn to the concept of cloud-assisted MPC, which harnesses the capabilities of powerful cloud computing to provide users with on-demand computational resources and data storage services. Within these cloud-assisted MPC frameworks, control signals are merged with a cloud-based MPC, which leverages the substantial processing power of cloud infrastructure to determine optimal control actions using detailed nonlinear models for greater accuracy. Simultaneously, a local MPC runs on simplified linear models constrained by limited on-device computing resources, delivering prompt control responses at the cost of reduced model accuracy. To achieve an effective trade-off between rapid response and model fidelity, this work presents a new model-free deep reinforcement learning structure designed to merge cloud and local MPC outputs. Tests conducted on path-following scenarios show that the introduced method achieves superior control performance compared to existing reinforcement learning baselines and conventional rule-based fusion strategies.