Scalable Multi-modal Model Predictive Control via Duality-based
  Interaction Predictions
Hansung Kim Siddharth H. Nair Francesco Borrelli
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/-TcMeolCLWc