Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions

Abstract-- 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. While this approach showcases robust navigation to heterogeneous traffic agents: human-driven and autonomous capabilities in multi-modal traffic scenarios, it focuses on vehicles navigating and making their own decisions. The game theoretic approaches in In urban driving scenarios, the motion planning for autonomous (ii) are generally computationally intractable for traffic scenarios vehicles in the presence of uncertain, multi-modal with many vehicles/agents, which is further exacerbated human-driven and autonomous vehicles poses a significant when the games are multi-modal/mixed.