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
@article{arxiv.2402.01116,
title = {Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions},
author = {Hansung Kim and Siddharth H. Nair and Francesco Borrelli},
journal= {arXiv preprint arXiv:2402.01116},
year = {2025}
}
Comments
Accepted at IEEE Intelligent Vehicles Symposium 2024