Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
This work addresses computational efficiency for autonomous vehicles in interactive traffic, though it appears incremental as it builds on existing MPC methods.
The paper tackles the challenge of scalable real-time Model Predictive Control in complex multi-modal traffic scenarios by proposing a hierarchical architecture with RAID-Net and a reduced Stochastic MPC, achieving a 12x speed-up in motion planning.
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