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/-pRiOnPb9_c. GitHub: https://github.com/MPC-Berkeley/hmpc_raidnet

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——一种基于注意力机制的新型递归神经网络，通过拉格朗日对偶性沿MPC预测时域预测自动驾驶车辆与周围车辆之间的相关交互；2）一种精简的随机MPC问题，通过消除无关的碰撞避免约束来提升计算效率。我们在包含交互车辆的模拟交通路口场景中验证了该方法，在运动规划求解速度上实现了12倍的加速。展示该架构在多个复杂交通场景中应用的视频详见：https://youtu.be/-pRiOnPb9_c。GitHub仓库：https://github.com/MPC-Berkeley/hmpc_raidnet