We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key components: 1) RAID-Net, a novel attention-based Recurrent Neural Network that predicts relevant interactions along the MPC prediction horizon between the autonomous vehicle and the surrounding vehicles using Lagrangian duality, and 2) a reduced Stochastic MPC problem that eliminates irrelevant collision avoidance constraints, enhancing computational efficiency. Our approach is demonstrated in a simulated traffic intersection with interactive surrounding vehicles, showcasing a 12x speed-up in solving the motion planning problem. A video demonstrating the proposed architecture in multiple complex traffic scenarios can be found here: https://youtu.be/-TcMeolCLWc

翻译：我们提出了一种面向复杂多模态交通场景中可扩展实时模型预测控制（MPC）的分层架构。该架构包含两个关键组件：1）RAID-Net——一种新型基于注意力机制的循环神经网络，利用拉格朗日对偶性在MPC预测时域内预测自车与周围车辆之间的相关交互；2）精简后的随机MPC问题，通过移除无关碰撞避免约束从而提升计算效率。我们在包含交互性周围车辆的模拟交通路口验证了该方法，展示了运动规划求解速度提升12倍的效果。展示该架构在多种复杂交通场景中表现的视频可参见：https://youtu.be/-TcMeolCLWc