Research article Special Issues

A constrained optimal control framework for vehicle platoons with delayed communication

  • Received: 22 November 2021 Revised: 02 May 2022 Accepted: 11 January 2023 Published: 21 March 2023
  • 58F15, 58F17, 53C35

  • Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons at a highway on-ramp merging scenario. We present a single-level constrained optimal control framework that optimizes the fuel economy and travel time of the platoons while satisfying the state, control, and safety constraints. We also explore the effect of delayed communication among the CAV platoons and propose a robust coordination framework to enforce lateral and rear-end collision avoidance constraints in the presence of bounded delays. We provide a closed-form analytical solution to the optimal control problem with safety guarantees that can be implemented in real time. Finally, we validate the effectiveness of the proposed control framework using a high-fidelity commercial simulation environment.

    Citation: A M Ishtiaque Mahbub, Behdad Chalaki, Andreas A. Malikopoulos. A constrained optimal control framework for vehicle platoons with delayed communication[J]. Networks and Heterogeneous Media, 2023, 18(3): 982-1005. doi: 10.3934/nhm.2023043

    Related Papers:

  • Vehicle platooning using connected and automated vehicles (CAVs) has attracted considerable attention. In this paper, we address the problem of optimal coordination of CAV platoons at a highway on-ramp merging scenario. We present a single-level constrained optimal control framework that optimizes the fuel economy and travel time of the platoons while satisfying the state, control, and safety constraints. We also explore the effect of delayed communication among the CAV platoons and propose a robust coordination framework to enforce lateral and rear-end collision avoidance constraints in the presence of bounded delays. We provide a closed-form analytical solution to the optimal control problem with safety guarantees that can be implemented in real time. Finally, we validate the effectiveness of the proposed control framework using a high-fidelity commercial simulation environment.



    加载中


    [1] A. Al Alam, A. Gattami, K. H. Johansson, An experimental study on the fuel reduction potential of heavy duty vehicle platooning, 13th international IEEE conference on intelligent transportation systems, IEEE, Funchal, Portugal, (2010), 306–311. https://doi.org/10.1109/ITSC.2010.5625054
    [2] J. Alam A. Martensson, K. H. Johansson, Experimental evaluation of decentralized cooperative cruise control for heavy-duty vehicle platooning, Control Eng. Pract., 38 (2015), 11–25. https://doi.org/10.1016/j.conengprac.2014.12.009 doi: 10.1016/j.conengprac.2014.12.009
    [3] J. Alonso, V. Milanés, J. Pérez, E. Onieva, C. González, T. de Pedro, Autonomous vehicle control systems for safe crossroads, Transp. Res. Part C Emerg. Technol., 19 (2011), 1095–1110. https://doi.org/10.1016/j.trc.2011.06.002 doi: 10.1016/j.trc.2011.06.002
    [4] T. Ard, F. Ashtiani, A. Vahidi, H. Borhan, Optimizing gap tracking subject to dynamic losses via connected and anticipative mpc in truck platooning, American Control Conference (ACC), IEEE, Denver, CO, USA, (2020), 2300–2305. https://doi.org/10.23919/ACC45564.2020.9147849
    [5] M. Athans, A unified approach to the vehicle-merging problem, Transp. Res., 3 (1969), 123–133. https://doi.org/10.1016/0041-1647(69)90109-9 doi: 10.1016/0041-1647(69)90109-9
    [6] T. C. Au, P. Stone, Motion planning algorithms for autonomous intersection management, Bridging the gap between task and motion planning, AAAI press, (2010), 2–9. https://dl.acm.org/doi/abs/10.5555/2908515.2908516
    [7] H. Bang, B. Chalaki, A. A. Malikopoulos, Combined optimal routing and coordination of connected and automated vehicles, IEEE Control Syst. Lett., 6 (2022), 2749–2754. https://doi.org/10.1109/LCSYS.2022.3176594 doi: 10.1109/LCSYS.2022.3176594
    [8] L. E. Beaver, B. Chalaki, A. M. Mahbub, L. Zhao, R. Zayas, A. A. Malikopoulos, Demonstration of a time-efficient mobility system using a scaled smart city, Veh. Syst. Dyn., 58 (2020), 787–804. https://doi.org/10.1080/00423114.2020.1730412 doi: 10.1080/00423114.2020.1730412
    [9] L. E. Beaver, A. A. Malikopoulos, Constraint-driven optimal control of multi-agent systems: A highway platooning case study, IEEE Control Syst. Lett., 6 (2022), 1754–1759. https://doi.org/10.1109/LCSYS.2021.3133801 doi: 10.1109/LCSYS.2021.3133801
    [10] C. Bergenhem, S. Shladover, E. Coelingh, C. Englund, S. Tsugawa, Overview of platooning systems, Proceedings of the 19th ITS World Congress, Vienna, Austria, 2012.
    [11] B. Besselink, K. H. Johansson, String stability and a delay-based spacing policy for vehicle platoons subject to disturbances, IEEE Trans. Autom. Control, 62 (2017), 4376–4391. https://doi.org/10.1109/TAC.2017.2682421 doi: 10.1109/TAC.2017.2682421
    [12] A. K. Bhoopalam, N. Agatz, R. Zuidwijk, Planning of truck platoons: A literature review and directions for future research, Transp. Res. Part B Methodol., 107 (2018), 212–228. https://doi.org/10.1016/j.trb.2017.10.016 doi: 10.1016/j.trb.2017.10.016
    [13] A. E. Bryson, Y. C. Ho, Applied optimal control: optimization, estimation and control, CRC Press, 1975.
    [14] B. Chalaki, L. E. Beaver, A. M. I. Mahbub, H. Bang, A. A. Malikopoulos, A research and educational robotic testbed for real-time control of emerging mobility systems: From theory to scaled experiments, IEEE Control Syst. Mag., 42 (2022), 20–34. https://doi.org/10.1109/MCS.2022.3209056 doi: 10.1109/MCS.2022.3209056
    [15] B. Chalaki, L. E. Beaver, A. A. Malikopoulos, Experimental validation of a real-time optimal controller for coordination of cavs in a multi-lane roundabout, 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE, Las Vegas, NV, USA, (2020), 775–780. https://doi.org/10.1109/IV47402.2020.9304531
    [16] B. Chalaki, A. A. Malikopoulos, Time-optimal coordination for connected and automated vehicles at adjacent intersections, IEEE Trans. Intell. Transp. Syst., 23 (2022), 13330–13345. https://doi.org/10.1109/TITS.2021.3123479 doi: 10.1109/TITS.2021.3123479
    [17] B. Chalaki, A. A. Malikopoulos, Optimal control of connected and automated vehicles at multiple adjacent intersections, IEEE Trans. Control Syst. Technol., 30 (2022), 972–984. https://doi.org/10.1109/TCST.2021.3082306 doi: 10.1109/TCST.2021.3082306
    [18] B. Chalaki, A. A. Malikopoulos, A priority-aware replanning and resequencing framework for coordination of connected and automated vehicles, IEEE Control Syst. Lett., 6 (2022), 1772–1777. https://doi.org/10.1109/LCSYS.2021.3133416 doi: 10.1109/LCSYS.2021.3133416
    [19] B. Chalaki, A. A. Malikopoulos, Robust learning-based trajectory planning for emerging mobility systems, 2022 American Control Conference (ACC), IEEE, Atlanta, GA, USA, (2022), 2154–2159. https://doi.org/10.23919/ACC53348.2022.9867265
    [20] X. Chang, H. Li, J. Rong, X. Zhao, A. Li, Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles, Physica A, 557 (2020), 124829. https://doi.org/10.1016/j.physa.2020.124829 doi: 10.1016/j.physa.2020.124829
    [21] A. de La Fortelle, Analysis of reservation algorithms for cooperative planning at intersections, 13th International IEEE Conference on Intelligent Transportation Systems, IEEE, Funchal, Portugal, (2010), 445–449. https://doi.org/10.1109/ITSC.2010.5624978
    [22] K. Dresner, P. Stone, Multiagent traffic management: A reservation-based intersection control mechanism, in Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagents Systems, IEEE Computer Society, (2004), 530–537. https://dl.acm.org/doi/10.5555/1018410.1018799
    [23] K. Dresner, P. Stone, A multiagent approach to autonomous intersection management, J. Artif. Intell. Res., 31 (2008), 591–656. https://doi.org/10.1613/jair.2502 doi: 10.1613/jair.2502
    [24] D. J. Fagnant, K. M. Kockelman, The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios, Transp. Res. Part C Emerg. Technol., 40 (2014), 1–13. https://doi.org/10.1016/j.trc.2013.12.001 doi: 10.1016/j.trc.2013.12.001
    [25] M. Fellendorf, P. Vortisch, Microscopic traffic flow simulator vissim, Fundamentals of Traffic Simulation, International Series in Operations Research and Management Science, Springer, New York, NY, 145 (2010), 63–93.
    [26] S. Feng, Y. Zhang, S. E. Li, Z. Cao, H. X. Liu, L. Li, String stability for vehicular platoon control: Definitions and analysis methods, Annu. Rev. Control, 47 (2019), 81–97. https://doi.org/10.1016/j.arcontrol.2019.03.001 doi: 10.1016/j.arcontrol.2019.03.001
    [27] A. Ferrara, S. Sacone, S. Siri, Freeway Traffic Modeling and Control, Springer, Berlin, 2018. https://doi.org/10.1007/978-3-319-75961-6
    [28] J. Guanetti, Y. Kim, F. Borrelli, Control of connected and automated vehicles: State of the art and future challenges, Annu. Rev. Control, 45 (2018), 18–40. https://doi.org/10.1016/j.arcontrol.2018.04.011 doi: 10.1016/j.arcontrol.2018.04.011
    [29] S. V. D. Hoef, J. Mårtensson, D. V. Dimarogonas, K. H. Johansson, A predictive framework for dynamic heavy-duty vehicle platoon coordination, ACM Trans. Cyber-Phys. Syst., 4 (2019), 1–25. https://doi.org/10.1145/3299110 doi: 10.1145/3299110
    [30] S. Huang, A. Sadek, Y. Zhao, Assessing the mobility and environmental benefits of reservation-based intelligent intersections using an integrated simulator, IEEE Trans. Intell. Transp. Syst., 13 (2012), 1201–1214. https://doi.org/10.1109/TITS.2012.2186442 doi: 10.1109/TITS.2012.2186442
    [31] A. Johansson, E. Nekouei, K. H. Johansson, J. Mårtensson, Multi-fleet platoon matching: A game-theoretic approach, 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, Maui, HI, USA, 2018, 2980–2985. https://doi.org/10.1109/ITSC.2018.8569379
    [32] M. Kamal, M. Mukai, J. Murata, T. Kawabe, Model predictive control of vehicles on urban roads for improved fuel economy, IEEE Trans. Control Syst. Technol., 21 (2013), 831–841. https://doi.org/10.1109/TCST.2012.2198478 doi: 10.1109/TCST.2012.2198478
    [33] S. Karbalaieali, O. A. Osman, S. Ishak, A dynamic adaptive algorithm for merging into platoons in connected automated environments, IEEE Trans. Intell. Transp. Syst., 21 (2019), 4111–4122. https://doi.org/10.1109/TITS.2019.2938728 doi: 10.1109/TITS.2019.2938728
    [34] P. Kavathekar, Y. Chen, Vehicle platooning: A brief survey and categorization, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington, DC, USA, (2011), 829–845. https://doi.org/10.1115/DETC2011-47861
    [35] V. L. Knoop, H. J. Van Zuylen, S. P. Hoogendoorn, Microscopic traffic behaviour near accidents, Transportation and Traffic Theory 2009: Golden Jubilee, Springer, Boston, MA, 2009.
    [36] S. Kumaravel, A. A. Malikopoulos, R. Ayyagari, Decentralized cooperative merging of platoons of connected and automated vehicles at highway on-ramps, in 2021 American Control Conference (ACC), IEEE, New Orleans, LA, USA, (2021), 2055–2060. https://doi.org/10.23919/ACC50511.2021.9483390
    [37] S. Kumaravel, A. A. Malikopoulos, R. Ayyagari, Optimal coordination of platoons of connected and automated vehicles at signal-free intersections, IEEE Trans. Intell. Veh., 7 (2022), 186–197. https://doi.org/10.1109/TIV.2021.3096993 doi: 10.1109/TIV.2021.3096993
    [38] J. Larson, K. Y. Liang, K. H. Johansson, A distributed framework for coordinated heavy-duty vehicle platooning, IEEE Trans. Intell. Transp. Syst., 16 (2015), 419–429. https://doi.org/10.1109/TITS.2014.2320133 doi: 10.1109/TITS.2014.2320133
    [39] W. Levine, M. Athans, On the optimal error regulation of a string of moving vehicles, IEEE Trans. Autom. Control, 11 (1966), 355–361. https://doi.org/10.1109/TAC.1966.1098376 doi: 10.1109/TAC.1966.1098376
    [40] J. Lioris, R. Pedarsani, F. Y. Tascikaraoglu, P. Varaiya, Platoons of connected vehicles can double throughput in urban roads, Transp. Res. Part C Emerging Technol., 77 (2017), 292–305. https://doi.org/10.1016/j.trc.2017.01.023 doi: 10.1016/j.trc.2017.01.023
    [41] A. M. I. Mahbub, V. Karri, D. Parikh, S. Jade, A. A. Malikopoulos, A decentralized time- and energy-optimal control framework for connected automated vehicles: From simulation to field test, arXiv preprint, 2020. https://doi.org/10.48550/arXiv.1911.01380
    [42] A. M. I. Mahbub, V. A. Le, A. A. Malikopoulos, A safety-prioritized receding horizon control framework for platoon formation in a mixed traffic environment, arXiv preprint. https://doi.org/10.48550/arXiv.2205.10673
    [43] A. M. I. Mahbub, V. A. Le, A. A. Malikopoulos, Safety-aware and data-driven predictive control for connected automated vehicles at a mixed traffic signalized intersection, IFAC-PapersOnLine, 24 (2022), 51–56. https://doi.org/10.1016/j.ifacol.2022.10.261 doi: 10.1016/j.ifacol.2022.10.261
    [44] A. M. I. Mahbub, A. A. Malikopoulos, Concurrent optimization of vehicle dynamics and powertrain operation using connectivity and automation, arXiv preprint, 2019. https://doi.org/10.48550/arXiv.1911.03475
    [45] A. M. I. Mahbub, A. A. Malikopoulos, Conditions for state and control constraint activation in coordination of connected and automated vehicles, 2020 American Control Conference (ACC), IEEE, Denver, CO, USA, (2020), 436–441. https://doi.org/10.23919/ACC45564.2020.9147842
    [46] A. M. I. Mahbub, A. A. Malikopoulos, A platoon formation framework in a mixed traffic environment, IEEE Control Syst. Lett., 6 (2021), 1370–1375. https://doi.org/10.1109/LCSYS.2021.3092188 doi: 10.1109/LCSYS.2021.3092188
    [47] A. M. I. Mahbub, A. A. Malikopoulos, Conditions to provable system-wide optimal coordination of connected and automated vehicles, Automatica, 131 (2021), 109751. https://doi.org/10.1016/j.automatica.2021.109751 doi: 10.1016/j.automatica.2021.109751
    [48] A. M. I. Mahbub, A. A. Malikopoulos, Platoon formation in a mixed traffic environment: A model-agnostic optimal control approach, 2022 American Control Conference (ACC), IEEE, Atlanta, GA, USA, (2022), 4746–4751. https://doi.org/10.23919/ACC53348.2022.9867168
    [49] A. M. I. Mahbub, L. Zhao, D. Assanis, A. A. Malikopoulos, Energy-optimal coordination of connected and automated vehicles at multiple intersections, 2019 American Control Conference (ACC), IEEE, Philadelphia, PA, USA, (2019), 2664–2669. https://doi.org/10.23919/ACC.2019.8814877
    [50] A. I. Mahbub, A. A. Malikopoulos, L. Zhao, Decentralized optimal coordination of connected and automated vehicles for multiple traffic scenarios, Automatica, 117 (2020), 108958. https://doi.org/10.1016/j.automatica.2020.108958 doi: 10.1016/j.automatica.2020.108958
    [51] A. A. Malikopoulos, A duality framework for stochastic optimal control of complex systems, IEEE Trans. Autom. Control, 18 (2016), 780–789. https://doi.org/10.1109/TAC.2015.2504518 doi: 10.1109/TAC.2015.2504518
    [52] A. A. Malikopoulos, L. E. Beaver, I. V. Chremos, Optimal time trajectory and coordination for connected and automated vehicles, Automatica, 125 (2021), 109469. https://doi.org/10.1016/j.automatica.2020.109469 doi: 10.1016/j.automatica.2020.109469
    [53] A. A. Malikopoulos, C. G. Cassandras, Y. J. Zhang, A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections, Automatica, 93 (2018), 244–256. https://doi.org/10.1016/j.automatica.2018.03.056 doi: 10.1016/j.automatica.2018.03.056
    [54] A. A. Malikopoulos, L. Zhao, A closed-form analytical solution for optimal coordination of connected and automated vehicles, 2019 American Control Conference (ACC), IEEE, Philadelphia, PA, USA, (2019), 3599–3604. https://doi.org/10.23919/ACC.2019.8814759
    [55] A. A. Malikopoulos, L. Zhao, Optimal path planning for connected and automated vehicles at urban intersections, 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, Nice, France, (2019), 1261–1266. https://doi.org/10.1109/CDC40024.2019.9030093
    [56] R. Margiotta, D. Snyder, An agency guide on how to establish localized congestion mitigation programs, Technical report, U.S. Department of Transportation, Federal Highway Administration, 2011.
    [57] F. Morbidi, P. Colaneri, T. Stanger, Decentralized optimal control of a car platoon with guaranteed string stability, 2013 European Control Conference (ECC), IEEE, Zurich, Switzerland, (2013), 3494–3499. https://doi.org/10.23919/ECC.2013.6669336
    [58] G. J. Naus, R. P. Vugts, J. Ploeg, M. J. van De Molengraft, M. Steinbuch, String-stable cacc design and experimental validation: A frequency-domain approach, IEEE Trans. Veh. Technol., 59 (2010), 4268–4279. https://doi.org/10.1109/TVT.2010.2076320 doi: 10.1109/TVT.2010.2076320
    [59] I. A. Ntousakis, I. K. Nikolos, M. Papageorgiou, Optimal vehicle trajectory planning in the context of cooperative merging on highways, Transp. Res. Part C Emerging Technol., 71 (2016), 464–488. https://doi.org/10.1016/j.trc.2016.08.007 doi: 10.1016/j.trc.2016.08.007
    [60] M. Papageorgiou, A. Kotsialos, Freeway ramp metering: An overview, IEEE Trans. Intell. Transp. Syst., 3 (2002), 271–281. https://doi.org/10.1109/TITS.2002.806803 doi: 10.1109/TITS.2002.806803
    [61] H. Pei, S. Feng, Y. Zhang, D. Yao, A cooperative driving strategy for merging at on-ramps based on dynamic programming, IEEE Trans. Veh. Technol., 68 (2019), 11646–11656. https://doi.org/10.1109/TVT.2019.2947192 doi: 10.1109/TVT.2019.2947192
    [62] N. Pourmohammad Zia, F. Schulte, R. R. Negenborn, Platform-based platooning to connect two autonomous vehicle areas, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, Rhodes, Greece, (2020), 1–6. https://doi.org/10.1109/ITSC45102.2020.9294689
    [63] R. Rajamani, H. S. Tan, B. K. Law, W. B. Zhang, Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons, IEEE Trans. Control Syst. Technol., 8 (2000), 695–708. https://doi.org/10.1109/87.852914 doi: 10.1109/87.852914
    [64] J. Rios-Torres, A. A. Malikopoulos, A survey on coordination of connected and automated vehicles at intersections and merging at highway on-ramps, IEEE Trans. Intell. Transp. Syst., 18 (2017), 1066–1077. https://doi.org/10.1109/TITS.2016.2600504 doi: 10.1109/TITS.2016.2600504
    [65] J. Rios-Torres, A. A. Malikopoulos, Automated and cooperative vehicle merging at highway on-ramps, IEEE Trans. Intell. Transp. Syst., 18 (2017), 780–789. https://doi.org/10.1109/TITS.2016.2587582 doi: 10.1109/TITS.2016.2587582
    [66] B. Schrank, B. Eisele, T. Lomax, 2019 Urban Mobility Scorecard, Technical report, Texas A and M Transportation Institute, 2019.
    [67] M. Shida, T. Doi, Y. Nemoto, K. Tadakuma, A short-distance vehicle platooning system: 2nd report, evaluation of fuel savings by the developed cooperative control, in Proceedings of the 10th International Symposium on Advanced Vehicle Control (AVEC), KTH Royal Institute of Technology Loughborough, United Kingdom, (2010), 719–723.
    [68] S. E. Shladover, C. A. Desoer, J. K. Hedrick, M. Tomizuka, J. Walrand, W. B. Zhang, et al., Automated vehicle control developments in the PATH program, IEEE Trans. Veh. Technol., 40 (1991), 114–130. https://doi.org/10.1109/25.69979 doi: 10.1109/25.69979
    [69] S. Singh, Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey. (Traffic Safety Facts Crash Stats.), Technical Report, 2018.
    [70] K. Spieser, K. Treleaven, R. Zhang, E. Frazzoli, D. Morton, M. Pavone, Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in singapore, Road vehicle automation, Springer, Cham, (2014), 229–245.
    [71] S. S. Stanković, M. J. Stanojević, D. D. Šiljak, Decentralized suboptimal lqg control of platoon of vehicles, Proc. 8th IFAC/IFIP/IFORS Symp. Transp. Syst., 1 (1997) 83–88.
    [72] C. Tang, Y. Li, Consensus-based platoon control for non-lane-discipline connected autonomous vehicles considering time delays, 2018 37th Chinese Control Conference (CCC), IEEE, Wuhan, (2018), 7713–7718. https://doi.org/10.23919/ChiCC.2018.8484016
    [73] S. Tsugawa, An overview on an automated truck platoon within the energy its project, IFAC Proc. Volumes, 46 (2013), 41–46. https://doi.org/10.3182/20130904-4-JP-2042.00110 doi: 10.3182/20130904-4-JP-2042.00110
    [74] A. Tuchner, J. Haddad, Vehicle platoon formation using interpolating control, IFAC-PapersOnLine, 48 (2015), 414–419. https://doi.org/10.1016/j.ifacol.2015.09.492 doi: 10.1016/j.ifacol.2015.09.492
    [75] A. Valencia, A. M. I. Mahbub, A. A. Malikopoulos, Performance analysis of optimally coordinated connected automated vehicles in a mixed traffic environment, 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, Vouliagmeni, Greece, (2022), 1053–1058. https://doi.org/10.1109/MED54222.2022.9837281
    [76] S. Van De Hoef, K. H. Johansson, D. V. Dimarogonas, Fuel-efficient en route formation of truck platoons, IEEE Trans. Intell. Transp. Syst., 19 (2017), 102–112. https://doi.org/10.1109/TITS.2017.2700021 doi: 10.1109/TITS.2017.2700021
    [77] P. Varaiya, Smart cars on smart roads: Problems of control, IEEE Trans. Autom. Control, 38 (1993), 195–207. https://doi.org/10.1109/9.250509 doi: 10.1109/9.250509
    [78] Z. Wadud, D. MacKenzie, P. Leiby, Help or hindrance? the travel, energy and carbon impacts of highly automated vehicles, Transp. Res. Part A Policy Pract., 86 (2016), 1–18. https://doi.org/10.1016/j.tra.2015.12.001 doi: 10.1016/j.tra.2015.12.001
    [79] Z. Wang, G. Wu, P. Hao, K. Boriboonsomsin, M. Barth, Developing a platoon-wide eco-cooperative adaptive cruise control (cacc) system, 2017 ieee intelligent vehicles symposium (iv), IEEE, Los Angeles, CA, USA, (2017), 1256–1261. https://doi.org/10.1109/IVS.2017.7995884
    [80] R. Wiedemann, Simulation des Strassenverkehrsflusses, PhD thesis, Universität Karlsruhe, Karlsruhe, 1974.
    [81] W. Xiao, C. G. Cassandras, Decentralized optimal merging control for connected and automated vehicles with safety constraint guarantees, Automatica, 123 (2021), 109333. https://doi.org/10.1016/j.automatica.2020.109333 doi: 10.1016/j.automatica.2020.109333
    [82] X. Xiong, E. Xiao, L. Jin, Analysis of a stochastic model for coordinated platooning of heavy-duty vehicles, 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, Nice, France, (2019), 3170–3175. https://doi.org/10.1109/CDC40024.2019.9029179
    [83] F. Xu, T. Shen, Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles, IEEE Trans. Intell. Transp. Syst.. https://doi.org/10.1109/TITS.2021.3054903
    [84] L. Xu, W. Zhuang, G. Yin, C. Bian, H. Wu, Modeling and robust control of heterogeneous vehicle platoons on curved roads subject to disturbances and delays, IEEE Trans. Veh. Technol., 68 (2019), 11551–11564. https://doi.org/10.1109/TVT.2019.2941396 doi: 10.1109/TVT.2019.2941396
    [85] S. Yao, B. Friedrich, Managing connected and automated vehicles in mixed traffic by human-leading platooning strategy: A simulation study, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, Auckland, New Zealand, (2019), 3224–3229. https://doi.org/10.1109/ITSC.2019.8917007
    [86] L. Zhang, F. Chen, X. Ma, X. Pan, Fuel economy in truck platooning: A literature overview and directions for future research, J. Adv. Transp., 2020 (2020). https://doi.org/10.1155/2020/2604012 doi: 10.1155/2020/2604012
    [87] Y. Zhang, C. G. Cassandras, Decentralized optimal control of connected automated vehicles at signal-free intersections including comfort-constrained turns and safety guarantees, Automatica, 109 (2019), 108563. https://doi.org/10.1016/j.automatica.2019.108563 doi: 10.1016/j.automatica.2019.108563
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(893) PDF downloads(61) Cited by(0)

Article outline

Figures and Tables

Figures(5)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog