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A CAV Platoon Control Method for Isolated Intersections: Guaranteed Feasible Multi-Objective Approach with Priority

Author

Listed:
  • Chen Wang

    (Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China)

  • Yulu Dai

    (Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China)

  • Jingxin Xia

    (Intelligent Transportation Systems Research Center, Southeast University, Nanjing 211189, China)

Abstract

This paper proposed a multi-objective guaranteed feasible connected and autonomous vehicle (CAV) platoon control method for signalized isolated intersections with priorities. Specifically, we prioritized the intersection throughput and traffic efficiency under a pre-defined signal cycle, based on which we minimized fuel consumption and emissions for CAV platoons. Longitudinal safety was also considered as a necessary condition. To handle the aforementioned targets, we firstly designed a vehicular sub-platoon splitting algorithm based on Farkas lemma to accommodate a maximum number of vehicles for each signal green time phase. Secondly, the CAV optimal trajectories control algorithm was designed as a centralized cooperative model predictive control (MPC). Moreover, the optimal control problem was formulated as discrete linear quadratic control problems with constraints with receding predictive horizons, which can be efficiently solved by quadratic programming after reformulation. For rigor, the proofs of the recursive feasibility and asymptotic stability of our proposed predictive control model were provided. For evaluation, the performance of the control algorithm was compared against a non-cooperative distributed CAV control through simulation. It was found that the proposed method can significantly enhance both traffic efficiency and energy efficiency with ensured safety for CAV platoons at urban signalized intersections.

Suggested Citation

  • Chen Wang & Yulu Dai & Jingxin Xia, 2020. "A CAV Platoon Control Method for Isolated Intersections: Guaranteed Feasible Multi-Objective Approach with Priority," Energies, MDPI, vol. 13(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:625-:d:315387
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    References listed on IDEAS

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    1. Montanino, Marcello & Punzo, Vincenzo, 2015. "Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 82-106.
    2. Ma, Jiaqi & Li, Xiaopeng & Zhou, Fang & Hu, Jia & Park, B. Brian, 2017. "Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 421-441.
    3. Zhou, Fang & Li, Xiaopeng & Ma, Jiaqi, 2017. "Parsimonious shooting heuristic for trajectory design of connected automated traffic part I: Theoretical analysis with generalized time geography," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 394-420.
    4. Cassidy, Michael J. & Rudjanakanoknad, Jittichai, 2005. "Increasing the capacity of an isolated merge by metering its on-ramp," Transportation Research Part B: Methodological, Elsevier, vol. 39(10), pages 896-913, December.
    5. Xu, Chengcheng & Wang, Yong & Liu, Pan & Wang, Wei & Bao, Jie, 2018. "Quantitative risk assessment of freeway crash casualty using high-resolution traffic data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 299-311.
    6. Akcelik, Rahmi, 1989. "Efficiency and drag in the power-based model of fuel consumption," Transportation Research Part B: Methodological, Elsevier, vol. 23(5), pages 376-385, October.
    7. Li, Xiaopeng & Cui, Jianxun & An, Shi & Parsafard, Mohsen, 2014. "Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 319-339.
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    Citations

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    Cited by:

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    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. Taeyoon Kim & Taewon Song & Sangheon Pack, 2020. "An Energy Efficient Message Dissemination Scheme in Platoon-Based Driving Systems," Energies, MDPI, vol. 13(15), pages 1-23, August.
    4. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Dilshad Mohammed & Balázs Horváth, 2024. "Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption," Energies, MDPI, vol. 17(7), pages 1-19, March.

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