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：本文提出了一种分层架构，专为复杂多模态交通场景中可扩展的实时模型预测控制而设计。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，利用拉格朗日对偶性预测自主车辆与周围车辆在MPC预测时域内的相关交互；2) 一个简化的随机模型预测控制问题，通过消除无关的避碰约束以提升计算效率。我们在模拟的交叉路口场景中验证了所提方法，其中包含具有交互行为的周围车辆，实验结果表明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仓库：https://github.com/MPC-Berkeley/hmpc_raidnet