Bolin Gao, Keke Wan, Qien Chen, Zhou Wang, Rui Li, Yu Jiang, Run Mei, Yinghui Luo, Keqiang Li. A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System. Machine Intelligence Research, vol. 20, no. 5, pp.614-639, 2023. https://doi.org/10.1007/s11633-022-1395-3
Citation: Bolin Gao, Keke Wan, Qien Chen, Zhou Wang, Rui Li, Yu Jiang, Run Mei, Yinghui Luo, Keqiang Li. A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System. Machine Intelligence Research, vol. 20, no. 5, pp.614-639, 2023. https://doi.org/10.1007/s11633-022-1395-3

A Review and Outlook on Predictive Cruise Control of Vehicles and Typical Applications Under Cloud Control System

doi: 10.1007/s11633-022-1395-3
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  • Author Bio:

    Bolin Gao received the B. Sc. and M. Sc. degrees in vehicle engineering from Jilin University, China in 2007 and 2009, respectively, and the Ph. D. degree in vehicle engineering from Tongji University, China in 2013. He is now an associate research professor at School of Vehicle and Mobility, Tsinghua University, China. His research directions include the theoretical research and engineering application of the dynamic design and control of intelligent and connected vehicles.His research interests include the collaborative perception and tracking method in cloud control system, intelligent predictive cruise control system on commercial trucks with cloud control mode, as well as the test and evaluation of intelligent vehicle driving system.E-mail: gaobolin@tsinghua.edu.cnORCID iD: 0000-0002-5582-7289

    Keke Wan received the B. Eng. degree in vehicle engineering from Henan University of Engineering, China in 2020. He is currently a master student in vehicle engineering at College of Engineering, China Agricultural University, China. He is engaged in research work in Intelligent and Connected Vehicle of Tsinghua (THICV) Group, School of Vehicle and Mobility, Tsinghua University, China, with a research focus on predictive cruise control and cloud control system.His research interests include cloud-based predictive cruise control, vehicle-road-cloud collaborative control and cloud control architecture.E-mail: wankeke@cau.edu.cn

    Qien Chen received the B. Eng. degree in industrial engineering from Shanghai Ocean University, China in 2020. Currently, he is a master student in vehicle engineering at School of Electro-mechanical Engineering, Guangdong University of Technology, China. Since 2021, he has engaged in research work in School of Vehicle and Mobility, Tsinghua University, China.His research interests include predictive cruise control and cloud control system.E-mail: 2112001475@mail2.gdut.edu.cn

    Zhou Wang received the B. Eng. degree in instrument science and technology from Wuhan University of Technology, China in 2019. Currently, he is a master student in instruments science and technology at School of Mechanical and Electrical Engineering, Wuhan University of Technology, China. He is engaged in research work in School of Vehicle and Mobility, Tsinghua University, China.His research interests include predictive cruise control of vehicle platoon and cloud control system.E-mail: wz2020@whut.edu.cn

    Rui Li received the B. Eng. degree in vehicle engineering from Shandong University of Technology, China in 2020. He is currently a master student in mechanical engineering at College of Engineering, China Agricultural University, China. He is engaged in research work in THICV Group, School of Vehicle and Mobility, Tsinghua University, China, with a research focus on adaptive cruise control.His research interests include cloud-based predictive adaptive cruise control and cloud control system.E-mail: lirui@cau.edu.cn

    Yu Jiang received the B. Eng. degree in vehicle engineering from Harbin Institute of Technology, China in 2021. Currently, he is a master student in mechanical engineering, College of Engineering, China Agricultural University, China. And he is engaged in research work in School of Vehicle and Mobility, Tsinghua University, China.His research interests include lane-changing decision and control, and cloud control system.E-mail: jiangyu@cau.edu.cn

    Run Mei received the B. Eng. degree in instrument science and technology from Wuhan University of Technology, China in 2020. Currently, he is a master student in instruments science and technology at School of Mechanical and Electrical Engineering, Wuhan University of Technology, China. He is engaged in research work in School of Vehicle and Mobility, Tsinghua University, China.His research interests include platoon lane-changing and cloud control system.E-mail: 1301076746@qq.com

    Yinghui Luo received the B. Eng. degree in engineering from Luoyang Institute of Science and Technology, China in 2020. He is currently a master student in vehicle engineering at College of Engineering, China Agricultural University, China.His research interests include intelligent controls and machine learning.E-mail: luoyinghui@cau.edu.cn

    Keqiang Li received the B. Sc. degree in mechanical engineeringfrom Tsinghua University, China in 1985, and the M. Sc. and Ph. D. degrees in mechanical engineering from Chongqing University, China in 1988 and 1995, respectively. He is an academician of the Chinese Academy of Engineering, and a professor at School of Vehicle and Mobility, Tsinghua University, China. He is also the director of State Key Laboratory of Automotive Safety and Energy and a senior member of SAE-China. He has authored over 200 articles and holds over 60 patents. His research interests include intelligent and connected vehicles, vehicle dynamics, and cloud control system.E-mail: likq@mail.tsinghua.edu.cn (Corresponding author)ORCID iD: 0000-0001-6223-5401

  • Received Date: 2022-08-11
  • Accepted Date: 2022-11-11
  • Publish Online: 2023-03-09
  • Publish Date: 2023-10-01
  • With the application of mobile communication technology in the automotive industry, intelligent connected vehicles equipped with communication and sensing devices have been rapidly promoted. The road and traffic information perceived by intelligent vehicles has important potential application value, especially for improving the energy-saving and safe-driving of vehicles as well as the efficient operation of traffic. Therefore, a type of vehicle control technology called predictive cruise control (PCC) has become a hot research topic. It fully taps the perceived or predicted environmental information to carry out predictive cruise control of vehicles and improves the comprehensive performance of the vehicle-road system. Most existing reviews focus on the economical driving of vehicles, but few scholars have conducted a comprehensive survey of PCC from theory to the status quo. In this paper, the methods and advances of PCC technologies are reviewed comprehensively by investigating the global literature, and typical applications under a cloud control system (CCS) are proposed. Firstly, the methodology of PCC is generally introduced. Then according to typical scenarios, the PCC-related research is deeply surveyed, including freeway and urban traffic scenarios involving traditional vehicles, new energy vehicles, intelligent vehicles, and multi-vehicle platoons. Finally, the general architecture and three typical applications of the cloud control system (CCS) on PCC are briefly introduced, and the prospect and future trends of PCC are proposed.

     

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  • [1]
    B. Asadi, A. Vahidi. Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Transactions on Control Systems Technology, vol. 19, no. 3, pp. 707–714, 2011. DOI: 10.1109/TCST.2010.2047860.
    [2]
    M. Wang, S. P. Hoogendoorn, W. Daamen, B. van Arem, R. Happee. Game theoretic approach for predictive lane-changing and car-following control. Transportation Research Part C:Emerging Technologies, vol. 58, pp. 73–92, 2015. DOI: 10.1016/j.trc.2015.07.009.
    [3]
    T. Van Keulen, G. Naus, B. De Jager, R. Van de Molengraft, M. Steinbuch, E. Aneke. Predictive cruise control in hybrid electric vehicles. World Electric Vehicle Journal, vol. 3, no. 3, pp. 494–504, 2009. DOI: 10.3390/wevj3030494.
    [4]
    F. Q. Zhang, X. S. Hu, R. Langari, D. P. Cao. Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook. Progress in Energy and Combustion Science, vol. 73, pp. 235–256, 2019. DOI: 10.1016/j.pecs.2019.04.002.
    [5]
    A. Hamednia, N. Murgovski, J. Fredriksson. Predictive velocity control in a hilly terrain over a long look-ahead horizon. IFAC-PapersOnLine, vol. 51, no. 31, pp. 485–492, 2018. DOI: 10.1016/j.ifacol.2018.10.107.
    [6]
    K. Q. Li, Y. F. Dai, S. B. Li, M. Y. Bian. State-of-the-art and technical trends of intelligent and connected vehicles. Journal of Automotive Safety and Energy, vol. 8, no. 1, pp. 1–14, 2017. (in Chinese)
    [7]
    H. B. Zhou, W. C. Xu, J. C. Chen, W. Wang. Evolutionary V2X technologies toward the Internet of vehicles: Challenges and opportunities. Proceedings of the IEEE, vol. 108, no. 2, pp. 308–323, 2020. DOI: 10.1109/JPROC.2019.2961937.
    [8]
    K. Q. Li, X. Y. Chang, J. W. Li, Q. Xu, B. L. Gao, J. Pan. Cloud control system for intelligent and connected vehicles and its application. Automotive Engineering, vol. 42, no. 12, pp. 1595–1605, 2020. DOI: 10.19562/j.chinasae.qcgc.2020.12.001. (in Chinese)
    [9]
    S. B. Li, S. B, Xu, W. J. Wang, B. Cheng. Overview of ecological driving technology and application for ground vehicles. Journal of Automotive Safety and Energy, vol. 5, no. 2, pp. 121–131, 2014. DOI: 10.3969/j.issn.1674-8484.2014.02.002. (in Chinese)
    [10]
    L. Yang, X. M. Zhao, G. Y. Wu, Z. G. Xu, B. Matthew, F. Hui, P. Hao, M. J. Han, Z. Q. Zhao, S. Fang, S. C. Jing. Review on connected and automated vehicles based cooperative eco-driving strategies. Journal of Traffic and Transportation Engineering, vol. 20, no. 5, pp. 58–72, 2020. DOI: 10.19818/j.cnki.1671-1637.2020.05.004. (in Chinese)
    [11]
    J. L. Hong, B. Z. Gao, S. Y. Dong, Y. F. Cheng, Y. H. Wang, H. Chen. Key problems and research progress of energy saving optimization for intelligent connected vehicles. China Journal of Highway and Transport, vol. 34, no. 11, pp. 306–334, 2021. DOI: 10.19721/j.cnki.1001-7372.2021.11.025. (in Chinese)
    [12]
    A. Vahidi, A. Sciarretta. Energy saving potentials of connected and automated vehicles. Transportation Research Part C:Emerging Technologies, vol. 95, pp. 822–843, 2018. DOI: 10.1016/j.trc.2018.09.001.
    [13]
    C. Yang, M. J. Zha, W. D. Wang, K. J. Liu, C. L. Xiang. Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: Review and recent advances under intelligent transportation system. IET Intelligent Transport Systems, vol. 14, no. 7, pp. 702–711, 2020. DOI: 10.1049/iet-its.2019.0606.
    [14]
    F. Lattemann, K. Neiss, S. Terwen, T. Connolly. The Predictive Cruise Control-A System to Reduce Fuel Consumption of Heavy Duty Trucks, SAE Technical Paper 2004-01-2616, SAE International, Warrendale, USA, 2004.
    [15]
    H. Q. Chu, L. L. Guo, B. Z. Gao, H. Chen, N. Bian, J. G. Zhou. Predictive cruise control using high-definition map and real vehicle implementation. IEEE Transactions on Vehicular Technology, vol. 67, no. 12, pp. 11377–11389, 2018. DOI: 10.1109/TVT.2018.2871202.
    [16]
    H. Chen, L. L. Guo, H. T. Ding, Y. Li, B. Z. Gao. Real-time predictive cruise control for eco-driving taking into account traffic constraints. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 2858–2868, 2019. DOI: 10.1109/TITS.2018.2868518.
    [17]
    W. J. Hong, P. Yang, K. Tang. Evolutionary computation for large-scale multi-objective optimization: A decade of progresses. International Journal of Automation and Computing, vol. 18, no. 2, pp. 155–169, 2021. DOI: 10.1007/s11633-020-1253-0.
    [18]
    S. Y. Li, K. K. Wan, B. L. Gao, R. Li, Y. Wang, K. Q. Li. Predictive cruise control for heavy trucks based on slope information under cloud control system. Journal of Systems Engineering and Electronics, vol. 33, no. 4, pp. 812–826, 2022. DOI: 10.23919/JSEE.2022.000081.
    [19]
    D. Y. Jia, H. B. Chen, Z. D. Zheng, D. Watling, R. Connors, J. B. Gao, Y. Li. An enhanced predictive cruise control system design with data-driven traffic prediction. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 8170–8183, 2022. DOI: 10.1109/TITS.2021.3076494.
    [20]
    S. E. Li, Q. Q. Guo, S. B. Xu, J. L. Duan, S. Li, C. J. Li, K. Su. Performance enhanced predictive control for adaptive cruise control system considering road elevation information. IEEE Transactions on Intelligent Vehicles, vol. 2, no. 3, pp. 150–160, 2017. DOI: 10.1109/TIV.2017.2736246.
    [21]
    M. M. Brugnolli, B. A. Angélico, A. A. M. Laganá. Predictive adaptive cruise control using a customized ECU. IEEE Access, vol. 7, pp. 55305–55317, 2019. DOI: 10.1109/ACCESS.2019.2907011.
    [22]
    B. HomChaudhuri, A. Vahidi, P. Pisu. Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions. IEEE Transactions on Control Systems Technology, vol. 25, no. 2, pp. 760–767, 2017. DOI: 10.1109/TCST.2016.2572603.
    [23]
    C. J. Zhai, F. Luo, Y. G. Liu, Z. Y. Chen. Ecological cooperative look-ahead control for automated vehicles travelling on freeways with varying slopes. IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1208–1221, 2019. DOI: 10.1109/TVT.2018.2886221.
    [24]
    R. K. A. Sakir, A. Rusdinar, S. Yuwono, A. S. Wibowo, Silvirianti, N. T. Jayanti. Movement control algorithm of weighted automated guided vehicle using fuzzy inference system. In Proceedings of the 2nd International Conference on Control and Robotics Engineering, IEEE, Bangkok, Thailand, pp. 135–139, 2017. DOI: 10.1109/ICCRE.2017.7935057.
    [25]
    Z. Fan, J. Ruan, W. J. Li, Y. G. You, X. Y. Cai, Z. L. Xu, Z. Yang, F. Z. Sun, Z. J. Wang, Y. T. Yuan, Z. C. Li, G. J. Zhu. A learning guided parameter setting for constrained multi-objective optimization. In Proceedings of the 1st International Conference on Industrial Artificial Intelligence, IEEE, Shenyang, China, 2019. DOI: 10.1109/ICIAI.2019.8850786.
    [26]
    S. Kanzi. Multi-objective optimisation technique for optimum allocation of DG in distribution systems using weight factors. In Proceedings of International Congress on Human-Computer Interaction, Optimization and Robotic Applications, IEEE, Ankara, Turkey, 2022. DOI: 10.1109/HORA55278.2022.9799991.
    [27]
    R. T. Marler, J. S. Arora. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, vol. 26, no. 6, pp. 369–395, 2004. DOI: 10.1007/s00158-003-0368-6.
    [28]
    Y. C. Lin, H. L. T. Nguyen, V. E. Balas, T. C. Lin, I. C. Kuo. Adaptive prediction-based control for an ecological cruise control system on curved and hilly roads. Journal of Intelligent &Fuzzy Systems, vol. 38, no. 5, pp. 6129–6144, 2020. DOI: 10.3233/JIFS-179696.
    [29]
    H. Yang, F. Almutairi, H. Rakha. Eco-driving at signalized intersections: A multiple signal optimization approach. IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 2943–2955, 2021. DOI: 10.1109/TITS.2020.2978184.
    [30]
    H. Wang, B. Lu, J. Li, T. Liu, Y. Xing, C. Lv, D. P. Cao, J. X. Li, J. W. Zhang, E. Hashemi. Risk assessment and mitigation in local path planning for autonomous vehicles with LSTM based predictive model. IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 2738–2749, 2022. DOI: 10.1109/TASE.2021.3075773.
    [31]
    J. H. Han, A. Sciarretta, L. L. Ojeda, G. De Nunzio, L. Thibault. Safe-driving and eco-driving control for connected and automated electric vehicles using analytical state-constrained optimal solution. IEEE Transactions on Intelligent Vehicles, vol. 3, no. 2, pp. 163–172, 2018. DOI: 10.1109/TIV.2018.2804162.
    [32]
    X. D. Zhang, T. Zhang, Y. Zou, G. D. Du, N. Y. Guo. Predictive eco-driving application considering real-world traffic flow. IEEE Access, vol. 8, pp. 82187–82200, 2020. DOI: 10.1109/ACCESS.2020.2991538.
    [33]
    H. X. Dong, W. C. Zhuang, B. L. Chen, G. D. Yin, Y. Wang. Enhanced eco-approach control of connected electric vehicles at signalized intersection with queue discharge prediction. IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5457–5469, 2021. DOI: 10.1109/TVT.2021.3075480.
    [34]
    V. Milanás, S E. Shladover. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C:Emerging Technologies, vol. 48, pp. 285–300, 2014. DOI: 10.1016/j.trc.2014.09.001.
    [35]
    L. L. Ojeda, J. H. Han, A. Sciarretta, G. De Nunzio, L. Thibault. A real-time eco-driving strategy for automated electric vehicles. In Proceedings of the 56th IEEE Annual Conference on Decision and Control, Melbourne, Australia, pp. 2768–2774, 2017. DOI: 10.1109/CDC.2017.8264061.
    [36]
    J. Leithon, S. Werner, V. Koivunen. Energy optimization through cooperative storage management: A calculus of variations approach. Renewable Energy, vol. 171, pp. 1357–1370, 2021. DOI: 10.1016/j.renene.2021.02.093.
    [37]
    M. Jafari-Nodoushan, A. Ejlali. An optimal analytical solution for maximizing expected battery lifetime using the calculus of variations. Integration, vol. 71, pp. 86–94, 2020. DOI: 10.1016/j.vlsi.2019.11.002.
    [38]
    N. Y. Guo, B. Lenzo, X. D. Zhang, Y. Zou, R. Q. Zhai, T. Zhang. A real-time nonlinear model predictive controller for yaw motion optimization of distributed drive electric vehicles. IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 4935–4946, 2020. DOI: 10.1109/TVT.2020.2980169.
    [39]
    H. T. Hao, T. L. Lu, J. W. Zhang, B. Zhou. A new control strategy of the filling phase for wet dual clutch transmission. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science, vol. 230, no. 12, pp. 2013–2027, 2016. DOI: 10.1177/0954406215590187.
    [40]
    H. Bouvier, G. Colin, Y. Chamaillard. Determination and comparison of optimal eco-driving cycles for hybrid electric vehicles. In Proceedings of European Control Conference, IEEE, Linz, Austria, pp. 142–147, DOI: 10.1109/ECC.2015.7330536.
    [41]
    C. Sun, J. Guanetti, F. Borrelli, S. J. Moura. Optimal eco-driving control of connected and autonomous vehicles through signalized intersections. IEEE Internet of Things Journal, vol. 7, no. 5, pp. 3759–3773, 2020. DOI: 10.1109/JIOT.2020.2968120.
    [42]
    J. Zhang, H. Jin. Optimized calculation of the economic speed profile for slope driving: Based on iterative dynamic programming. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3313–3323, 2022. DOI: 10.1109/TITS.2020.3035610.
    [43]
    G. Elnagar, M. A. Kazemi, M. Razzaghi. The pseudospectral Legendre method for discretizing optimal control problems. IEEE Transactions on Automatic Control, vol. 40, no. 10, pp. 1793–1796, 1995. DOI: 10.1109/9.467672.
    [44]
    H. E. Perez, X. S. Hu, S. Dey, S. J. Moura. Optimal charging of li-ion batteries with coupled electro-thermal-aging dynamics. IEEE Transactions on Vehicular Technology, vol. 66, no. 9, pp. 7761–7770, 2017. DOI: 10.1109/TVT.2017.2676044.
    [45]
    V. Winstead, I. V. Kolmanovsky. Estimation of road grade and vehicle mass via model predictive control. In Proceedings of IEEE Conference on Control Applications, Toronto, Canada, pp. 1588–1593, 2005. DOI: 10.1109/CCA.2005.1507359.
    [46]
    L. Han, H. X. Liu, J. W. Wang, S. S. Li, L. L. Ren. Optimization control of CVT clutch engagement based on MPC. International Journal of Automotive Technology, vol. 20, no. 6, pp. 1161–1171, 2019. DOI: 10.1007/s12239-019-0109-5.
    [47]
    E. Walraven, M. T. J. Spaan, B. Bakker. Traffic flow optimization: A reinforcement learning approach,. Engineering Applications of Artificial Intelligence, vol. 52, pp. 203–212, 2016. DOI: 10.1016/j.engappai.2016.01.001.
    [48]
    H. B. Gao, G. Y. Shi, G. T. Xie, B. Cheng. Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making. International Journal of Advanced Robotic Systems, vol. 15, no. 6, 2018. DOI: 10.1177/1729881418817162.
    [49]
    J. K. Yin, W. P. Fu. A hybrid path planning algorithm based on simulated annealing particle swarm for the self-driving car. In Proceedings of International Computers, Signals and Systems Conference, IEEE, Dalian, China, pp. 696–700, 2018. DOI: 10.1109/ICOMSSC45026.2018.8941726.
    [50]
    R. L. Qin, Y. C. Lu, J. J. Guan, C. Ji. Eco-driving speed optimization model of urban intelligent connected vehicle platoon considering driver′s comfort level. In Proceedings of the 2nd International Conference on Electronics, Communications and Information Technology, IEEE, Sanya, China, pp. 532–537, 2021. DOI: 10.1109/CECIT53797.2021.00100.
    [51]
    J. G. Xue, C. S. Yan, D. Wang, J. Wang, J. Wu, Z. H. Liao. Adaptive dynamic programming method for optimal battery management of battery electric vehicle. In Proceedings of the 9th IEEE Data Driven Control and Learning Systems Conference, Liuzhou, China, pp. 65–68, 2020. DOI: 10.1109/DDCLS49620.2020.9275259.
    [52]
    M. Heydar, E. Mardaneh, R. Loxton. Approximate dynamic programming for an energy-efficient parallel machine scheduling problem. European Journal of Operational Research, vol. 302, no. 1, pp. 363–380, 2022. DOI: 10.1016/j.ejor.2021.12.041.
    [53]
    Q. Gong, W. Kang, I. M. Ross. A pseudospectral method for the optimal control of constrained feedback linearizable systems. IEEE Transactions on Automatic Control, vol. 51, no. 7, pp. 1115–1129, 2006. DOI: 10.1109/TAC.2006.878570.
    [54]
    M. Önnheim, P. Andersson, E. Gustavsson, M. Jirstrand. Reinforcement learning informed by optimal control. In Proceedings of the 28th International Conference on Artificial Neural Networks, Springer, Munich, Germany, pp. 403–407, 2019. DOI: 10.1007/978-3-030-30493-5_40.
    [55]
    P. P. Mariño. Heuristic algorithms. Optimization of Computer Networks-Modeling and Algorithms: Hands-On Approach, A, P. P. Mariño, Ed., Hoboken, USA: John Wiley & Sons, Ltd., pp. 266–300, 2016. DOI: 10.1002/9781119114840.ch12.
    [56]
    E. Hellström, M. Ivarsson, J. Aslund, L. Nielsen. Look-ahead control for heavy trucks to minimize trip time and fuel consumption. Control Engineering Practice, vol. 17, no. 2, pp. 245–254, 2009. DOI: 10.1016/j.conengprac.2008.07.005.
    [57]
    E. Hellströem, J. Aslund, L. Nielsen. Design of an efficient algorithm for fuel-optimal look-ahead control. Control Engineering Practice, vol. 18, no. 11, pp. 1318–1327, 2010. DOI: 10.1016/j.conengprac.2009.12.008.
    [58]
    M. A. S. Kamal, M. Mukai, J. Murata, T. Kawabe. Ecological vehicle control on roads with up-down slopes. IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 783–794, 2011. DOI: 10.1109/TITS.2011.2112648.
    [59]
    L. L. Guo, B. Z. Gao, Y. Gao, H. Chen. Optimal energy management for HEVs in eco-driving applications using Bi-level MPC. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 8, pp. 2153–2162, 2017. DOI: 10.1109/TITS.2016.2634019.
    [60]
    E. Hallström. Explicit Use of Road Topography for Model Predictive Cruise Control in Heavy Trucks, Master dissertation, Linköpings University, Linköpings, Sweden, 2005.
    [61]
    M. Alzorgan. Look-ahead Information Based Optimization Strategy for Hybrid Electric Vehicles, Master dissertation, Arizona State University, Tempe, USA, 2016.
    [62]
    H. X. Liu, L. Han, Y. Cao. Improving transmission efficiency and reducing energy consumption with automotive continuously variable transmission: A model prediction comprehensive optimization approach. Applied Energy, vol. 274, Article number 115303, 2020. DOI: 10.1016/j.apenergy.2020.115303.
    [63]
    C. Sun, X. S. Hu, S. J. Moura, F. C. Sun. Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, vol. 23, no. 3, pp. 1197–1204, 2015. DOI: 10.1109/TCST.2014.2359176.
    [64]
    E. Ozatay, S. Onori, J. Wollaeger, U. Ozguner, G. Rizzoni, D. Filev, J. Michelini, S. Di Cairano. Cloud-based velocity profile optimization for everyday driving: A dynamic-programming-based solution. IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 6, pp. 2491–2505, 2014. DOI: 10.1109/TITS.2014.2319812.
    [65]
    J. Hou, Z. Y. Song. A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity. Applied Energy, vol. 257, Article number 113900, 2020. DOI: 10.1016/j.apenergy.2019.113900.
    [66]
    W. Pananurak, S. Thanok, M. Parnichkun. Adaptive cruise control for an intelligent vehicle. In Proceedings of IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, pp. 1794–1799, 2009. DOI: 10.1109/ROBIO.2009.4913274.
    [67]
    K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms, New York, USA: Wiley, 2001.
    [68]
    S. B. Li, K. Q. Li, R. Rajamani, J. Q. Wang. Model predictive multi-objective vehicular adaptive cruise control. IEEE Transactions on Control Systems Technology, vol. 19, no. 3, pp. 556–566, 2011. DOI: 10.1109/TCST.2010.2049203.
    [69]
    S. E. Li. Q. Q. Guo, L. Xin, B. Cheng, K. Q. Li. Fuel-saving servo-loop control for an adaptive cruise control system of road vehicles with step-gear transmission. IEEE Transactions on Vehicular Technology, vol. 66, no. 3, pp. 2033–2043, 2017. DOI: 10.1109/TVT.2016.2574740.
    [70]
    L. Han, Y. An, A. Sohel, X. L. Zhao. Clamping force control strategy of continuously variable transmission based on extremum seeking control of sliding mode. Journal of Mechanical Engineering, vol. 53, no. 4, pp. 105–113, 2017. DOI: 10.3901/JME.2017.04.105. (in Chinese)
    [71]
    L. H. Luo, H. Liu, P. Li, H. Wang. Model predictive control for adaptive cruise control with multi-objectives: Comfort, fuel-economy, safety and car-following. Journal of Zhejiang University SCIENCE A, vol. 11, no. 3, pp. 191–201, 2010. DOI: 10.1631/jzus.A0900374.
    [72]
    J. Zhang, P. A. Ioannou. Longitudinal control of heavy trucks in mixed traffic: Environmental and fuel economy considerations. IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 92–104, 2006. DOI: 10.1109/TITS.2006.869597.
    [73]
    T. Stanger, L. del Re. A model predictive cooperative adaptive cruise control approach. In Proceedings of American Control Conference, IEEE, Washington DC, USA, pp. 1374–1379, 2013. DOI: 10.1109/ACC.2013.6580028.
    [74]
    R. Schmied, H. Waschl, L. del Re. Extension and experimental validation of fuel efficient predictive adaptive cruise control. In Proceedings of American Control Conference, IEEE, Chicago, USA, pp. 4753–4758, 2015. DOI: 10.1109/ACC.2015.7172078.
    [75]
    D. Moser, R. Schmied, H. Waschl, L. del Re. Flexible spacing adaptive cruise control using stochastic model predictive control. IEEE Transactions on Control Systems Technology, vol. 26, no. 1, pp. 114–127, 2018. DOI: 10.1109/TCST.2017.2658193.
    [76]
    H. Khayyam, S. Nahavandi, S. Davis. Adaptive cruise control look-ahead system for energy management of vehicles. Expert Systems with Applications, vol. 39, no. 3, pp. 3874–3885, 2012. DOI: 10.1016/j.eswa.2011.08.169.
    [77]
    A. Weissmann, D. Görges, X. H. Lin. Energy-optimal adaptive cruise control combining model predictive control and dynamic programming. Control Engineering Practice, vol. 72, pp. 125–137, 2018. DOI: 10.1016/j.conengprac.2017.12.001.
    [78]
    V. Turri, O. Flärdh, J. Maartenssont, K. H. Johansson. Fuel-optimal look-ahead adaptive cruise control for heavy-duty vehicles. In Proceedings of Annual American Control Conference, IEEE, Milwaukee, USA, pp. 1841–1848, 2018. DOI: 10.23919/ACC.2018.8431494.
    [79]
    A. S. Kamal, K. Hashikura, T. Hayakawa, K. Yamada, J. I. Imura. Look-ahead driving schemes for efficient control of automated vehicles on urban roads. IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1280–1292, 2022. DOI: 10.1109/TVT.2021.3132936.
    [80]
    A. S. Kamal, M. Mukai, J. Murata, T. Kawabe. Model predictive control of vehicles on urban roads for improved fuel economy. IEEE Transactions on Control Systems Technology, vol. 21, no. 3, pp. 831–841, 2013. DOI: 10.1109/TCST.2012.2198478.
    [81]
    K. Huang, X. F. Yang, Y. Lu, C. C. Mi, P. Kondlapudi. Ecological driving system for connected/automated vehicles using a two-stage control hierarchy. IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 7, pp. 2373–2384, 2018. DOI: 10.1109/TITS.2018.2813978.
    [82]
    G. Cesari, G. Schildbach, A. Carvalho, F. Borrelli. Scenario model predictive control for lane change assistance and autonomous driving on highways. IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 3, pp. 23–35, 2017. DOI: 10.1109/MITS.2017.2709782.
    [83]
    A. S. Kamal, S. Taguchi, T. Yoshimura. Efficient driving on multilane roads under a connected vehicle environment. IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 9, pp. 2541–2551, 2016. DOI: 10.1109/TITS.2016.2519526.
    [84]
    Y. M. Zhang, B. Zhou, X. J. Wu, Q. J. Cui, T. Chai. Motion planning of high speed intelligent vehicle based on front vehicle trajectory prediction. Automotive Engineering, vol. 42, no. 5, pp. 574–580, 587, 2020. DOI: 10.19562/j.chinasae.qcgc.2020.05.002. (in Chinese)
    [85]
    Z. K. Luan, J. N. Zhang, W. Z. Zhao, C. Y. Wang. Trajectory tracking control of autonomous vehicle with random network delay. IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8140–8150, 2020. DOI: 10.1109/TVT.2020.2995408.
    [86]
    Y. B. Zhao, Z. Z. Han, K. Su, L. Guo, W. H. Yang. Anti-collision trajectory planning and tracking control based on MPC and fuzzy PID algorithm. In Proceedings of 4th CAA International Conference on Vehicular Control and Intelligence, IEEE, Hangzhou, China, pp. 613–618, 2020. DOI: 10.1109/CVCI51460.2020.9338447.
    [87]
    J. Suh, H. Chea, K. Yi. Stochastic model-predictive control for lane change decision of automated driving vehicles. IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 4771–4782, 2018. DOI: 10.1109/TVT.2018.2804891.
    [88]
    G. Guo, Q. Wang. Fuel-efficient en route speed planning and tracking control of truck platoons. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 3091–3103, 2019. DOI: 10.1109/TITS.2018.2872607.
    [89]
    C. J. Zhai, X. Y. Chen, C. G. Yan, Y. G. Liu, H. J. Li. Ecological cooperative adaptive cruise control for a heterogeneous platoon of heavy-duty vehicles with time delays. IEEE Access, vol. 8, pp. 146208–146219, 2020. DOI: 10.1109/ACCESS.2020.3015052.
    [90]
    V. Turri, B. Besselink, K. H. Johansson. Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning. IEEE Transactions on Control Systems Technology, vol. 25, no. 1, pp. 12–28, 2017. DOI: 10.1109/TCST.2016.2542044.
    [91]
    K. Q. Li, J. W. Li, X. Y. Chang, B. L. Gao, Q. Xu, S. B. Li. Principles and typical applications of cloud control system for intelligent and connected vehicles. Journal of Automotive Safety and Energy, vol. 11, no. 3, pp. 261–275, 2020. (in Chinese)
    [92]
    Y. Yang, F. W. Ma, J. W. Wang, S. Zhu, S. Y. Gelbal, O. Kavas-Torris, B. Aksun-Guvenc, L. Guvenc. Cooperative ecological cruising using hierarchical control strategy with optimal sustainable performance for connected automated vehicles on varying road conditions. Journal of Cleaner Production, vol. 275, Article number 123056, 2020. DOI: 10.1016/j.jclepro.2020.123056.
    [93]
    M. Maged, D. M. Mahfouz, O. M. Shehata, E. I. Morgan. Behavioral assessment of an optimized multi-vehicle platoon formation control for efficient fuel consumption. In Proceedings of the 2nd Novel Intelligent and Leading Emerging Sciences Conference, IEEE, Giza, Egypt, pp. 403–409, 2020. DOI: 10.1109/NILES50944.2020.9257911.
    [94]
    X. T. Yang, K. Huang, Z. H. Zhang, Z. A. Zhang, F. Lin. Eco-driving system for connected automated vehicles: Multi-objective trajectory optimization. IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 12, pp. 7837–7849, 2021. DOI: 10.1109/TITS.2020.3010726.
    [95]
    F. W. Ma, Y. Yang, J. W. Wang, X. C. Li, G. P. Wu, Y. Zhao, L. Wu, B. Aksun-Guvenc, L. Guvenc. Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections. Transportation Research Part D:Transport and Environment, vol. 92, Article number 102746, 2021. DOI: 10.1016/j.trd.2021.102746.
    [96]
    K. Katsaros, R. Kernchen, M. Dianat, D. Rieck. Performance study of a green light optimized speed advisory (GLOSA) application using an integrated cooperative ITS simulation platform. In Proceedings of the 7th International Wireless Communications and Mobile Computing Conference, IEEE, Istanbul, Turkey, pp. 918–923, 2011. DOI: 10.1109/IWCMC.2011.5982524.
    [97]
    M. Seredynski, W. Mazurczyk, D. Khadraoui. Multi-segment green light optimal speed advisory. In Proceedings of IEEE International Symposium on Parallel & Distributed Processing, Workshops and Ph.D. Forum, IEEE, Cambridge, USA, pp. 459–465, 2013. DOI: 10.1109/IPDPSW.2013.157.
    [98]
    J. J. Li, M. Dridi, A. El-Moudni. Multi-vehicles green light optimal speed advisory based on the augmented Lagrangian genetic algorithm. In Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China, pp. 2434–2439, 2014. DOI: 10.1109/ITSC.2014.6958080.
    [99]
    B. Xu, Z. Fang, J. Q. Wang, K. Q. Li. B&B algorithm-based green light optimal speed advisory applied to contiguous intersections. In Proceedings of the 15th COTA International Conference of Transportation Professionals, Beijing, China, pp. 24–27, 2015. DOI: 10.1061/9780784479292.033.
    [100]
    H. Rakha, R. K. Kamalanathsharma. Eco-driving at signalized intersections using V2I communication. In Proceedings of the 14th IEEE International Conference on Intelligent Transportation Systems, Washington DC, USA, pp. 341–346, 2011. DOI: 10.1109/ITSC.2011.6083084.
    [101]
    H. T. Xia, K. Boriboonsomsin, M. Barth. Dynamic eco-driving for signalized arterial corridors and its indirect network-wide energy/emissions benefits. Journal of Intelligent Transportation Systems, vol. 17, no. 1, pp. 31–41, 2013. DOI: 10.1080/15472450.2012.712494.
    [102]
    G. De Nunzio, C. C. de Wit, P. Moulin, D. Di Domenico. Eco-driving in urban traffic networks using traffic signals information. International Journal of Robust and Nonlinear Control, vol. 26, no. 6, pp. 1307–1324, 2016. DOI: 10.1002/rnc.3469.
    [103]
    M. Barth, S. Mandava, K. Boriboonsomsin, H. T. Xia. Dynamic ECO-driving for arterial corridors. In Proceedings of IEEE Forum on Integrated and Sustainable Transportation Systems, Vienna, Austria, pp. 182–188, 2011. DOI: 10.1109/FISTS.2011.5973594.
    [104]
    H. T. Xia, K. Boriboonsomsin, F. Schweizer, A. Winckler, K. Zhou, W. B. Zhang, M. Barth. Field operational testing of ECO-approach technology at a fixed-time signalized intersection. In Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, USA, pp. 188–193, 2012. DOI: 10.1109/ITSC.2012.6338888.
    [105]
    S. E. Li, S. B. Xu, X. Y. Huang, B. Cheng, H. Peng. Eco-departure of connected vehicles with V2X communication at signalized intersections. IEEE Transactions on Vehicular Technology, vol. 64, no. 12, pp. 5439–5449, 2015. DOI: 10.1109/TVT.2015.2483779.
    [106]
    S. Mandava, K. Boriboonsomsin, M. Barth. Arterial velocity planning based on traffic signal information under light traffic conditions. In Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, USA, pp. 1–6, 2009. DOI: 10.1109/ITSC.2009.5309519.
    [107]
    P. Hao, G. Y. Wu, K. Boriboonsomsin, M. J. Barth. Eco-approach and departure (EAD) application for actuated signals in real-world traffic. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 30–40, 2019. DOI: 10.1109/TITS.2018.2794509.
    [108]
    G. Mahler, A. Winckler, S. A. Fayazi, M. Filusch, A. Vahidi. Cellular communication of traffic signal state to connected vehicles for arterial eco-driving. In Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, Yokohama, Japan, 2017. DOI: 10.1109/ITSC.2017.8317591.
    [109]
    M. Miyatake, M. Kuriyama, Y. Takeda. Theoretical study on ECO-driving technique for an electric vehicle considering traffic signals. In Proceedings of IEEE Ninth International Conference on Power Electronics and Drive Systems, Singapore, Singapore, pp. 5–8, 2011. DOI: 10.1109/PEDS.2011.6147334.
    [110]
    R. K. Kamalanathsharma, H. A. Rakha. Multi-stage dynamic programming algorithm for eco-speed control at traffic signalized intersections. In Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, Netherlands, pp. 2094–2099, 2013. DOI: 10.1109/ITSC.2013.6728538.
    [111]
    Q. Jin, G. Y. Wu, K. Boriboonsomsin, M. J. Barth. Power-based optimal longitudinal control for a connected eco-driving system. IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 10, pp. 2900–2910, 2016. DOI: 10.1109/TITS.2016.2535439.
    [112]
    G. Mahler, A. Vahidi. An optimal velocity-planning scheme for vehicle energy efficiency through probabilistic prediction of traffic-signal timing. IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 6, pp. 2516–2523, 2014. DOI: 10.1109/TITS.2014.2319306.
    [113]
    Y. G. Luo, S. Li, S. W. Zhang, Z. B. Qin, K. Q. Li. Green light optimal speed advisory for hybrid electric vehicles. Mechanical Systems and Signal Processing, vol. 87, pp. 30–44, 2017. DOI: 10.1016/j.ymssp.2016.04.016.
    [114]
    Y. Zheng, S. E. Li, B. Xu, K. Q. Li, J Q. Wang. Complexity analysis of green light optimal velocity problem: An NP-complete result for binary speed choices. In Proceedings of the 14th Intelligent Transportation Systems Asia Pacific Forum, Nanjing, China, pp. 1–6, 2015.
    [115]
    C. Zhi, Y. L. Zhang, J. P. Lv, Y. J. Zou. Model for optimization of ecodriving at signalized intersections. Transportation Research Record:Journal of the Transportation Research Board, vol. 2427, no. 1, pp. 54–62, 2014. DOI: 10.3141/2427-06.
    [116]
    B. Liu, A. El Kamel. V2X-based decentralized cooperative adaptive cruise control in the vicinity of intersections. IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 3, pp. 644–658, 2016. DOI: 10.1109/TITS.2015.2486140.
    [117]
    N. F. Wan, A. Vahidi, A. Luckow. Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic. Transportation Research Part C:Emerging Technologies, vol. 69, pp. 548–563, 2016. DOI: 10.1016/j.trc.2016.01.011.
    [118]
    H. F. Jiang, J. Hu, S. An, M. Wang, B. B. Park. Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment. Transportation Research Part C:Emerging Technologies, vol. 79, pp. 290–307, 2017. DOI: 10.1016/j.trc.2017.04.001.
    [119]
    P. Schuricht, O. Michler, B. Bäker. Efficiency-increasing driver assistance at signalized intersections using predictive traffic state estimation. In Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, Washington DC, USA, 2011. DOI: 10.1109/ITSC.2011.6083111.
    [120]
    H. Yang, H. Rakha, M. V. Ala. Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1575–1585, 2017. DOI: 10.1109/TITS.2016.2613740.
    [121]
    X. Z. He, H. X. Liu, X. B. Liu. Optimal vehicle speed trajectory on a signalized arterial with consideration of queue. Transportation Research Part C:Emerging Technologies, vol. 61, pp. 106–120, 2015. DOI: 10.1016/j.trc.2015.11.001.
    [122]
    X. K. Wu, X. Z. He, G. Z. Yu, A. Harmandayan, Y. P. Wang. Energy-optimal speed control for electric vehicles on signalized arterials. IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2786–2796, 2015. DOI: 10.1109/TITS.2015.2422778.
    [123]
    S. Y. Dong, H. Chen, B. Z. Gao, L. L. Guo, Q. F. Liu. Hierarchical energy-efficient control for CAVs at multiple signalized intersections considering queue effects. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11643–11653, 2022. DOI: 10.1109/TITS.2021.3105964.
    [124]
    H. J. Cui, J. K. Xing, X. Li, M. Q. Zhu. Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection. Modern Physics Letters B, vol. 32, no. 32, Article number 1850396, 2018. DOI: 10.1142/S0217984918503967.
    [125]
    R. K. Kamalanathsharma, H. Rakha. Agent-based modeling of Eco-Cooperative Adaptive Cruise Control systems in the vicinity of intersections. In Proceedings of the 15th IEEE International Conference on Intelligent Transportation Systems, Anchorage, USA, pp. 840–845, 2012. DOI: 10.1109/ITSC.2012.6338643.
    [126]
    Y. Du, W. Shangguan, L. G. Chai. A coupled vehicle-signal control method at signalized intersections in mixed traffic environment. IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2089–2100, 2021. DOI: 10.1109/TVT.2021.3056457.
    [127]
    H. Liu, X. Y. Lu, S. E. Shladover. Traffic signal control by leveraging cooperative adaptive cruise control (CACC) vehicle platooning capabilities. Transportation Research Part C:Emerging Technologies, vol. 104, pp. 390–407, 2019. DOI: 10.1016/j.trc.2019.05.027.
    [128]
    H. J. Günther, V. V. Kumar, S. Hussain, K. Sommerwerk, D. Bondarenko. Optimizing vehicle approach strategies for connected signalized intersections. In Proceedings of IEEE Vehicular Networking Conference, Los Angeles, USA, 2019. DOI: 10.1109/VNC48660.2019.9062810.
    [129]
    C. Lazar, A. Tiganasu, C. F. Caruntu. Arterial intersection improvement by using vehicle platooning and coordinated start. IFAC-PapersOnLine, vol. 51, vol. 9, pp. 136–141, 2018. DOI: 10.1016/j.ifacol.2018.07.023.
    [130]
    Y. W. Bie, T. Z. Qiu. Connected vehicle-cooperative adaptive cruise control algorithm to divide and reform connected vehicle platoons at signalized intersections to improve traffic throughput and safety. Transportation Research Record:Journal of the Transportation Research Board, vol. 2675, no. 9, pp. 995–1005, 2021. DOI: 10.1177/03611981211005456.
    [131]
    Y. P. Wang, W. J. E, W. Z. Tang, D. X. Tian, G. Q. Lu, G. Z. Yu. Automated on-ramp merging control algorithm based on Internet-connected vehicles. IET Intelligent Transport Systems, vol. 7, no. 4, pp. 371–379, 2013. DOI: 10.1049/iet-its.2011.0228.
    [132]
    J. Rios-Torres, A. A. Malikopoulos. A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, pp. 1066–1077, 2017. DOI: 10.1109/TITS.2016.2600504.
    [133]
    S. C. Jing, F. Hui, X. M. Zhao, J. Rios-Torres, A. J. Khattak. Cooperative game approach to optimal merging sequence and on-ramp merging control of connected and automated vehicles. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 11, pp. 4234–4244, 2019. DOI: 10.1109/TITS.2019.2925871.
    [134]
    Z. R. Wang, G. Y. Wu, K. Boriboonsomsin, M. J. Barth, K. Han, B. Kim, P. Tiwari. Cooperative ramp merging system: Agent-based modeling and simulation using game engine. SAE International Journal of Connected and Automated Vehicles, vol. 2, no. 2, pp. 115–128, 2019. DOI: 10.4271/12-02-02-0008.
    [135]
    J. Rios-Torres, A. A. Malikopoulos. Automated and cooperative vehicle merging at highway on-ramps. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 4, pp. 780–789, 2017. DOI: 10.1109/TITS.2016.2587582.
    [136]
    H. X. Pei, S. Feng, Y. Zhang, D. Y. Yao. A cooperative driving strategy for merging at on-ramps based on dynamic programming. IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 11646–11656, 2019. DOI: 10.1109/TVT.2019.2947192.
    [137]
    J. S. Y. Ding, L. Li, H. Peng, Y. Zhang. A rule-based cooperative merging strategy for connected and automated vehicles. IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 8, pp. 3436–3446, 2020. DOI: 10.1109/TITS.2019.2928969.
    [138]
    X. S. Liao, Z. R. Wang, X. P. Zhao, K. Han, P. Tiwari, M. J. Barth, G. Y. Wu. Cooperative ramp merging design and field implementation: A digital twin approach based on vehicle-to-cloud communication. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, pp. 4490–4500, 2022. DOI: 10.1109/TITS.2020.3045123.
    [139]
    Z. R. Wang, G. Y. Wu, M. Barth. Distributed Consensus-Based Cooperative Highway On-Ramp Merging Using V2X Communications, SAE Technical Paper 2018-01-1177, SAE, Detroit, USA, 2018. DOI: 10.4271/2018-01-1177.
    [140]
    X. S. Liao, X. P. Zhao, G. Y. Wu, M. Barth, Z. R. Wang, K. Han, P. Tiwari. A game theory based ramp merging strategy for connected and automated vehicles in the mixed traffic: A unity-SUMO integrated platform. [Online], Available: https://arxiv.org/abs/2101.11237, 2021.
    [141]
    I. A. Ntousakis, I. K. Nikolos, M. Papageorgiou. Optimal vehicle trajectory planning in the context of cooperative merging on highways. Transportation Research Part C:Emerging Technologies, vol. 71, pp. 464–488, 2016. DOI: 10.1016/j.trc.2016.08.007.
    [142]
    W. Xiao, C. G. Cassandras. Decentralized optimal merging control for Connected and Automated Vehicles with safety constraint guarantees. Automatica, vol. 123, Article number 109333, 2021. DOI: 10.1016/j.automatica.2020.109333.
    [143]
    D. Marinescu, J. Čurn, M. Bouroche, V. Cahill. On-ramp traffic merging using cooperative intelligent vehicles: A slot-based approach. In Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, USA, pp. 900–906, 2012. DOI: 10.1109/ITSC.2012.6338779.
    [144]
    P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y. P. Flötteröd, R. Hilbrich, L. Löcken, J. Rummel, P. Wagner, E. Wiessner. Microscopic traffic simulation using SUMO. In Proceedings of the 21st International Conference on Intelligent Transportation Systems, IEEE, Maui, USA, pp. 2575–2582, 2018. DOI: 10.1109/ITSC.2018.8569938.
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