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:
this https URL

我们提出了一种用于复杂多模态交通场景中可扩展实时模型预测控制（MPC）的分层体系结构。该体系结构包括两个关键组件：1）RAID-Net，一种基于注意力机制的新型循环神经网络，使用拉格朗日对偶性在 MPC 预测范围内预测自动车辆与周围车辆之间的相关交互；2）一个简化的随机 MPC 问题，通过消除无关的避撞约束，提高计算效率。我们的方法在一个模拟的交通路口中演示了交互式周围车辆，展示了在解决运动规划问题时的 12 倍加速。可以在此链接中找到展示该体系结构在多个复杂交通场景中的视频。