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：本文提出一种专为复杂多模态交通场景中可扩展实时模型预测控制（MPC）设计的层次化架构。该架构包含两个核心组件：1）RAID-Net——一种基于注意力机制的新型循环神经网络，通过拉格朗日对偶方法预测自动驾驶车辆与周围车辆在MPC预测时域内的相关交互；2）简化的随机MPC问题，通过消除无关的碰撞规避约束以提升计算效率。我们在包含交互式周围车辆的模拟交通交叉口场景中验证了所提方法，其运动规划问题的求解速度提升了12倍。展示该架构在多种复杂交通场景中运行效果的视频可见：https://youtu.be/-pRiOnPb9_c。GitHub代码仓库：https://github.com/MPC-Berkeley/hmpc_raidnet