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Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization

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  • Zhang, Hanyu
  • Du, Lili
  • Shen, Jinglai

Abstract

This study is devoted to developing a platoon-based cooperative lane-change control (PB-CLC). It coordinates the trajectories of a CAV platoon under a platoon-centered platooning control to accommodate the CAV lane-change requests from its adjacent lane, aiming to reduce the negative traffic impacts on the platoon resulting from lane-change maneuvers, on the premise of ensuring CAVs’ safety and mobility. Mathematically, the PB-CLC control is established using a hybrid model predictive control (MPC) system. The hybrid MPC system involves an MPC-based mixed integer nonlinear programming optimizer (MINLP-MPC) for optimal lane-change decisions, which considers multiple objectives such as traffic smoothness, driving comfort and lane-change response promptness subject to vehicle dynamics and safety constraints. To ensure the feasible lane-change, this study investigates and provides a lower bound of the lane-change time window by analyzing the MINLP-MPC model feasibility. Apart from the optimal lane-change decision consideration, the hybrid MPC system is well designed to ensure the control continuity and smoothness. In particular, the hybrid MPC system control feasibility and stability are proved to enable the platoon's back-and-forth state switchings between car-following and lane-change accommodation states. Next, we developed a machine learning aided distributed branch and bound algorithm (ML-DBB) to solve the MINLP-MPC model within a control sampling time interval (< 1 second). Specifically, built upon computer simulation and the c-LHS sampling technique, supervised machine learning models are developed offline to predict a reduced solution space of the integer variables, which is further integrated into the distributed branch and bound method to solve the MINLP-MPC model efficiently online. Extensive numerical experiments validate the effectiveness and applicability of the ML-DBB algorithm and the PB-CLC control.

Suggested Citation

  • Zhang, Hanyu & Du, Lili & Shen, Jinglai, 2022. "Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 104-142.
    • Handle: RePEc:eee:transb:v:159:y:2022:i:c:p:104-142
    DOI: 10.1016/j.trb.2021.10.006
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    References listed on IDEAS

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    1. Gong, Siyuan & Du, Lili, 2018. "Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 25-61.
    2. Riccardo Scarinci & Benjamin Heydecker, 2014. "Control Concepts for Facilitating Motorway On-ramp Merging Using Intelligent Vehicles," Transport Reviews, Taylor & Francis Journals, vol. 34(6), pages 775-797, November.
    3. Matthieu Petelet & Bertrand Iooss & Olivier Asserin & Alexandre Loredo, 2010. "Latin hypercube sampling with inequality constraints," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(4), pages 325-339, December.
    4. Gong, Siyuan & Du, Lili, 2016. "Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 1-30.
    5. Wang, Jian & Gong, Siyuan & Peeta, Srinivas & Lu, Lili, 2019. "A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 271-301.
    6. Gong, Siyuan & Shen, Jinglai & Du, Lili, 2016. "Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 314-334.
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    Cited by:

    1. Wang, Jian & Zhou, Anye & Liu, Zhiyuan & Peeta, Srinivas, 2024. "Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    2. Qiu, Jiahua & Du, Lili, 2023. "Cooperative trajectory control for synchronizing the movement of two connected and autonomous vehicles separated in a mixed traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    3. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part II: Theoretical analysis," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 199-216.
    4. Jiang, Yangsheng & Tan, Li & Xiao, Guosheng & Wu, Yunxia & Yao, Zhihong, 2024. "Platoon-aware cooperative lane-changing strategy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    5. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 174-198.

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