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：我们提出一种层次化架构，旨在实现复杂多模态交通场景下的可扩展实时模型预测控制。该架构包含两个关键组件：1) RAID-Net，一种基于注意力机制的循环神经网络，利用拉格朗日对偶性在模型预测控制预测时域内预测自动驾驶车辆与周围车辆的相关交互；2) 简化随机模型预测控制问题，通过消除无关避碰约束提升计算效率。该方法在模拟交互交通路口场景中验证，可使运动规划求解速度提升12倍。展示该架构在多个复杂交通场景中的视频参见：https://youtu.be/-pRiOnPb9_c。GitHub仓库：https://github.com/MPC-Berkeley/hmpc_raidnet