Abstract
Safety and efficiency have always been significant challenges to the development of road traffic. Detailed vehicle motion information is the prerequisite for achieving optimal control of the platoon and improving traffic safety and efficiency. The connected and automated vehicles (CAVs) system has offered unprecedented opportunities for the real-time collection and processing of these detailed vehicle motion data. Based on the model predictive control (MPC) framework and safety potential field (SPF) model, we developed an alternative CAVs platoon dynamic control method. The SPF model was applied to describe the road risk distribution under the complex driving environment and was embedded in the MPC framework to optimize the vehicle dynamics from the perspective of capacity, safety, and energy-saving. Also, some experiments were performed to verify the validity of our platoon control strategy. Compared with the fixed time-headway strategy, our proposed strategy can increase the traffic capacity by about 24.4%, while ensuring safety and improving fuel economy. The results indicate that the novel CAVs platoon control methodology proposed in this paper can be potentially applied to alleviate various traffic problems (e.g., traffic congestion, traffic accidents, and high emissions).
Similar content being viewed by others
References
Ahn K, Rakha H, Trani A, Van Aerde M (2002) Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering 128(2):182–190, DOI: https://doi.org/10.1061/(ASCE)0733-947X(2002)128:2(182)
Ali A, Garcia G, Martinet P (2015) The flatbed platoon towing model for safe and dense platooning on highways. IEEE Intelligent Transportation Systems Magazine 7(1):58–68, DOI: https://doi.org/10.1109/MITS.2014.2328670
Barth M, An F, Younglove T, Scora G, Levine C, Ross M, Wenzel T (2000) The development of a comprehensive modal emissions model. Final Report for NCHRP Project 25-11, National Cooperative Highway Research Program, Washington DC, USA
International Transport Forum (2019) Road safety annual report 2019. International Transport Forum, Retrieved February 2, 2021, https://www.itf-oecd.org/road-safety-annual-report-2019
Ganji B, Kouzani AZ, Khoo SY, Shams-Zahraei M (2014) Adaptive cruise control of a HEV using sliding mode control. Expert Systems with Applications 41(2):607–615, DOI: https://doi.org/10.1016/j.eswa.2013.07.085
Hausberger S, Rodler J, Sturm P, Rexeis M (2003) Emission factors for heavy-duty vehicles and validation by tunnel measurements. Atmospheric Environment 37(37):5237–5245, DOI: https://doi.org/10.1016/j.atmosenv.2003.05.002
James RM, Melson C, Hu J, Bared J (2019) Characterizing the impact of production adaptive cruise control on traffic flow. an investigation. Transportmetrica B 7(1):992–1012, DOI: https://doi.org/10.1080/21680566.2018.1540951
Jinquan G, Hongwen H, Jiankun P, Nana Z (2019) A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles. Energy 175:378–392, DOI: https://doi.org/10.1016/j.energy.2019.03.083
Li X, Cui J, An S, Parsafard M (2014) Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation. Transportation Research Part B: Methodological 70(1):319–339, DOI: https://doi.org/10.1016/j.trb.2014.09.014
Li L, Gan J, Ji X, Qu X, Ran B (2020a) Dynamic driving risk potential field model under the connected and automated vehicles environment and its application in car-following modeling. IEEE Transactions on Intelligent Transportation Systems 1–20, DOI: https://doi.org/10.1109/TITS.2020.3008284
Li LH, Gan J, Li WQ (2018) A separation strategy for connected and automated vehicles: Utilizing traffic light information for reducing idling at red lights and improving fuel economy. Journal of Advanced Transportation 2018(PT4):1–10, DOI: https://doi.org/10.1155/2018/5679064
Li L, Gan J, Qu X, Mao P, Ran B (2019) Car-following model based on safety potential field theory under connected and automated vehicle environment. China Journal of Highway and Transport 32(12):76–87
Li L, Gan J, Yi Z, Qu X, Ran B (2020b) Risk perception and the warning strategy based on safety potential field theory. Accident Analysis and Prevention 148:105805, DOI: https://doi.org/10.1016/j.aap.2020.105805
Li L, Gan J, Zhou K, Qu X, Ran B (2020c) A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory. Physica A: Statistical Mechanics and its Applications 559: 125039, DOI: https://doi.org/10.1016/j.physa.2020.125039
Liu H, Kan X, Shladover SE, Lu XY, Ferlis RE (2018) Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities. Transportation Research Part C: Emerging Technologies 95:261–279, DOI: https://doi.org/10.1016/j.trc.2018.07.027
Luo LH, Liu H, Li P, Wang H (2010) Model predictive control for adaptive cruise control with multi-objectives: Comfort, fuel-economy, safety and car-following. Journal of Zhejiang University: Science A 11(3):191–201, DOI: https://doi.org/10.1631/jzus.A0900374
Mahdinia I, Arvin R, Khattak AJ, Ghiasi A (2020) Safety, energy, and emissions impacts of adaptive cruise control and cooperative adaptive cruise control. Transportation Research Record: Journal of the Transportation Research Board 2674(6):253–267, DOI: https://doi.org/10.1177/0361198120918572
Molnár TG, Qin WB, Insperger T, Orosz G (2018) Application of predictor feedback to compensate time delays in connected cruise control. IEEE Transactions on Intelligent Transportation Systems 19(2):545–559, DOI: https://doi.org/10.1109/TITS.2017.2754240
Nikolaou M (2001) Model predictive controllers: A critical synthesis of theory and industrial needs. Advances in Chemical Engineering 26:131–204, DOI: https://doi.org/10.1016/S0065-2377(01)26003-7
Qin Y, Wang H (2019) Influence of the feedback links of connected and automated vehicle on rear-end collision risks with vehicle-to-vehicle communication. Traffic Injury Prevention 20(1):79–83, DOI: https://doi.org/10.1080/15389588.2018.1527469
Qin Y, Wang H, Ran B (2018) Stability analysis of connected and automated vehicles to reduce fuel consumption and emissions. Journal of Transportation Engineering, Part A: Systems 144(11):04018068, DOI: https://doi.org/10.1061/JTEPBS.0000196
Qin Y, Wang H, Ran B (2019) Impact of connected and automated vehicles on passenger comfort of traffic flow with vehicle-to-vehicle communications. KSCE Journal of Civil Engineering 23(2):821–832, DOI: https://doi.org/10.1007/s12205-018-1990-6
Ran B, Cheng Y, Li S, Ding F, Jin J, Chen X, Zhang Z (2018) Connected automated vehicle highway systems and methods. Google Patent Search, Retrieved February 2, 2021, https://patents.google.com/patent/US20180336780A1/en
Sun W, Zheng J, Liu HX (2017) A capacity maximization scheme for intersection management with automated vehicles. Transportation Research Procedia 23:121–136, DOI: https://doi.org/10.1016/j.trc.2017.12.006
Tuchner A, Haddad J (2017) Vehicle platoon formation using interpolating control: A laboratory experimental analysis. Transportation Research Part C: Emerging Technologies 84:21–47, DOI: https://doi.org/10.1016/j.trc.2017.06.019
Wang M (2018) Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings. Transportation Research Part C: Emerging Technologies 91:276–295, DOI: https://doi.org/10.1016/j.trc.2018.04.010
Wang M, Daamen W, Hoogendoorn SP, van Arem B (2014) Rolling horizon control framework for driver assistance systems. Part II. Cooperative sensing and cooperative control. Transportation Research Part C: Emerging Technologies 40:290–311, DOI: https://doi.org/10.1016/j.trc.2013.11.024
Wang M, Daamen W, Hoogendoorn SP, van Arem B (2016a) Cooperative car-following control: Distributed algorithm and impact on moving jam features. IEEE Transactions on Intelligent Transportation Systems 17(5):1459–1471, DOI: https://doi.org/10.1109/TITS.2015.2505674
Wang J, Gong S, Peeta S, Lu L (2019) A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles. Transportation Research Part B: Methodological 128:271–301, DOI: https://doi.org/10.1016/j.trb.2019.08.002
Wang J, Wu J, Li Y (2015) The driving safety field based on driver-vehicle-road interactions. IEEE Transactions on Intelligent Transportation Systems 16(4):2203–2214, DOI: https://doi.org/10.1109/TITS.2015.2401837
Wang J, Wu J, Zheng X, Ni D, Li K (2016b) Driving safety field theory modeling and its application in pre-collision warning system. Transportation Research Part C 72:306–324, DOI: https://doi.org/10.1016/j.trc.2016.10.003
Wu X, Zhao X, Song H, Xin Q, Yu S (2019) Effects of the prevision relative velocity on traffic dynamics in the ACC strategy. Physica A: Statistical Mechanics and its Applications 515:192–198, DOI: https://doi.org/10.1016/j.physa.2018.09.172
Zhang Z, Ding F, Tan H (2019) Intelligent road infrastructure system (iris): Systems and methods. Google Patent Search, Retrieved February 2, 2021, https://patents.google.com/patent/US20190096238A1/en
Zhou Y, Ahn S, Chitturi M, Noyce DA (2017) Rolling horizon stochastic optimal control strategy for ACC and CACC under uncertainty. Transportation Research Part C: Emerging Technologies 83:61–76, DOI: https://doi.org/10.1016/j.trc.2017.07.011
Zhou Y, Wang M, Ahn S (2019) Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability. Transportation Research Part B: Methodological 128:69–86, DOI: https://doi.org/10.1016/j.trb.2019.07.001
Acknowledgments
This research was supported by the National Key R&D Program in China (Grant No. 2018YFB1600600), the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 20YJAZH083), the Scientific Research Foundation of Graduate School of Southeast University (Grants No. YBPY1928), and the National Natural Science Foundation of China (Grant No. 51878161). Part of the research was conducted at the University of Wisconsin-Madison where the first author spent a year as a visiting student funded by the China Scholarship Council.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, L., Gan, J., Qu, X. et al. A Dynamic Control Method for Cavs Platoon Based on the MPC Framework and Safety Potential Field Model. KSCE J Civ Eng 25, 1874–1886 (2021). https://doi.org/10.1007/s12205-021-1585-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-021-1585-5