Interactive Decision-Making Integrating Graph Neural Networks and Model Predictive Control for Autonomous Driving

Driving on public roads is inherently an interactive task, i.e., autonomous vehicles' (AVs) actions will influence nearby traffic participants' reactions, and vice versa. Decision-making for AVs in highly interactive driving scenarios (e.g., dense traffic) requires accurately forecasting the impact of the AVs' intention on nearby traffic participants' motion. To this end, a hierarchical decision-making framework (HDM) is proposed to navigate through interactive scenarios safely and efficiently. Specifically, the upper layer of the HDM serves as a coarse-level policy generator, which utilizes the plan-informed graph attention network (P-GAT) to provide interaction-aware guidance. The P-GAT predictor takes the historical states of nearby traffic participants, road structure information, and AVs' potential intentions as inputs. Subsequently, it predicts the motions of other traffic participants in response to potential actions of the AVs, which is then systematically evaluated to generate interactive guidance. Furthermore, the learned policy is utilized to guide the lower layer, which utilizes a fine-level model predictive control (MPC)-based planner to ensure safety and kinematic feasibility. Finally, to validate the effectiveness of HDM, both qualitative and quantitative experiments are carried out. More importantly, the hardware-in-the-loop (HiL) experiment is also implemented, including mandatory lane change in dense traffic flow and interaction with the human driver. The results demonstrate that the proposed HDM can improve driving safety and efficiency compared with baselines.