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Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions (2402.01116v4)
Published 2 Feb 2024 in cs.RO, cs.LG, cs.SY, and eess.SY
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. 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
- T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in 2020 ECCV. Springer, 2020.
- Y. Chen, U. Rosolia, W. Ubellacker, N. Csomay-Shanklin, and A. D. Ames, “Interactive multi-modal motion planning with branch model predictive control,” IEEE Robotics and Automation Letters, vol. 7, no. 2, 2022.
- S. H. Nair, H. Lee, E. Joa, Y. Wang, H. E. Tseng, and F. Borrelli, “Predictive control for autonomous driving with uncertain, multi-modal predictions,” arXiv preprint arXiv:2310.20561, 2023.
- J. L. V. Espinoza, A. Liniger, W. Schwarting, D. Rus, and L. Van Gool, “Deep interactive motion prediction and planning: Playing games with motion prediction models,” in Learning for Dynamics and Control Conference. PMLR, 2022.
- E. L. Zhu and F. Borrelli, “A sequential quadratic programming approach to the solution of open-loop generalized nash equilibria,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023.
- L. Peters, A. Bajcsy, C.-Y. Chiu, D. Fridovich-Keil, F. Laine, L. Ferranti, and J. Alonso-Mora, “Contingency games for multi-agent interaction,” IEEE Robotics and Automation Letters, 2024.
- E. Leurent and J. Mercat, “Social attention for autonomous decision-making in dense traffic,” 2019.
- Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, et al., “Planning-oriented autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
- T. Brüdigam, M. Olbrich, D. Wollherr, and M. Leibold, “Stochastic model predictive control with a safety guarantee for automated driving,” IEEE Transactions on Intelligent Vehicles, 2021.
- F. Baldini, R. Foust, A. Bacula, C. M. Chilan, S. J. Chung, S. Bandyopadhyay, A. Rahmani, J. P. De La Croix, and F. Y. Hadaegh, “Spacecraft trajectory planning using spherical expansion and sequential convex programming,” in AIAA/AAS Astrodynamics Specialist Conference, 2016. AIAA, 2016.
- S. H. Nair, E. H. Tseng, and F. Borrelli, “Collision avoidance for dynamic obstacles with uncertain predictions using model predictive control,” arXiv preprint arXiv:2208.03529, 2022.
- A. Ali, E. Wong, and J. Z. Kolter, “A semismooth newton method for fast, generic convex programming,” in International Conference on Machine Learning. PMLR, 2017.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” 2023.
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, 2014.
- J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” 2016.
- T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” 2021.
- B. Ivanovic, A. Elhafsi, G. Rosman, A. Gaidon, and M. Pavone, “Mats: An interpretable trajectory forecasting representation for planning and control,” 2021.
- K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” 2014.
- G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016.
- K. Kreutz and J. Eggert, “Analysis of the generalized intelligent driver model (gidm) for uncontrolled intersections,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021.
- D. Michie, M. Bain, and J. Hayes-Michie, “Cognitive models from subcognitive skills,” IEE control engineering series, vol. 44, 1990.
- J. A. E. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “CasADi – A software framework for nonlinear optimization and optimal control,” Mathematical Programming Computation, 2018.
- R. H. Byrd, M. E. Hribar, and J. Nocedal, “An interior point algorithm for large-scale nonlinear programming,” SIAM Journal on Optimization, vol. 9, no. 4, 1999.
- I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations, 2017.
- Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2023. [Online]. Available: https://www.gurobi.com
- H. Kim and F. Borrelli, “Facilitating cooperative and distributed multi-vehicle lane change maneuvers,” IFAC-PapersOnLine, vol. 56, no. 2, 2023, 22nd IFAC World Congress.
- Hansung Kim (23 papers)
- Siddharth H. Nair (19 papers)
- Francesco Borrelli (105 papers)