发表机构
KTH Royal Institute of Technology; Eindhoven University of Technology; Delft University of Technology（皇家理工学院; 埃因霍温理工大学; 代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种GNN加速的混合整数对偶MPC方法，用于多车道多对手交互驾驶，通过预测并固定机动决策，显著降低计算成本，同时保持最优性。
AI 中文摘要
在与不确定对手的交互中，对偶模型预测控制（MPC）可以通过信息寻求动作来减少对手行为的不确定性，从而提高性能。然而，其最近在自动驾驶中的应用仅限于单车道上的单一对手场景。本文提出了一种用于多车道道路上多个反应性对手的混合整数对偶MPC，联合优化整数型机动决策（车道变更和安全区域选择），以及在采样与对手可能交互的情景树上的连续运动。随着交互复杂性的增加，求解所得到的混合整数非线性规划变得越来越昂贵。为减轻这一计算负担，图神经网络（GNN）预测最优机动决策，并在求解简化问题之前固定高置信度的预测。仿真表明，在复杂的交互场景中出现了主动探测行为，且GNN引导固定了76.3%的整数决策，平均求解时间减少了2.5倍，而最优性损失可忽略不计。
英文摘要
In interactions with uncertain opponents, dual model predictive control (MPC) can improve performance through information-seeking actions that reduce uncertainty about opponents' behavior. Its recent applications to autonomous driving, however, are limited to scenarios involving a single opponent on a single lane. This paper presents a mixed-integer dual MPC for multiple reactive opponents on multi-lane roads, jointly optimizing integer-valued maneuver decisions (lane changes and safe-region selections), and continuous motion over a scenario tree that samples plausible interactions with the opponents. As interaction complexity increases, solving the resulting mixed-integer nonlinear program becomes increasingly expensive. To reduce this computational burden, a graph neural network (GNN) predicts the optimal maneuver decisions, and high-confidence predictions are fixed before the reduced problem is solved. Simulations show that active probing behavior emerges in complex interactive scenarios, and that GNN guidance fixes $76.3\%$ of the integer decisions and reduces the solve time by $2.5\times$ on average, with negligible degradation of optimality.
Comments9 pages, 8 figures
