Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions
<|reference_start|>Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions: 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<|reference_end|>
@article{kim2024scalable,
    title={Scalable Multi-modal Model Predictive Control via Duality-based
  Interaction Predictions},
    author={Hansung Kim, Siddharth H. Nair, Francesco Borrelli},
    journal={arXiv preprint arXiv:2402.01116},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01116},
    primaryClass={cs.RO cs.LG cs.SY eess.SY}
}