计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (12): 1-17.DOI: 10.3778/j.issn.1002-8331.2310-0310
李春,吴志周,曾广,赵鑫,杨志丹
出版日期:
2024-06-15
发布日期:
2024-06-14
LI Chun, WU Zhizhou, ZENG Guang, ZHAO Xin, YANG Zhidan
Online:
2024-06-15
Published:
2024-06-14
摘要: 车辆进行交会的区域被指定为上匝道合流区。如果主线和匝道交通流密度达到饱和,匝道合流区的交通效率就会急剧下降。智能网联技术作为当前的交通上的研究热点,依靠智能网联汽车(connected-automated vehicle, CAV)的高精度运动控制和高效率通信,可以显著地提高合流区的通行效率。针对三种不同的控制范式:反馈控制、最优控制和强化学习,对CAV使用的融合策略进行了评估。通过对现有研究的回顾,总结了三种方法在这种情况下的不足之处,并给出了具体的改进措施。此外,全面地总结了这一特定科学领域的最新发展和趋势。
李春, 吴志周, 曾广, 赵鑫, 杨志丹. 合流区智能网联汽车协同控制方法综述[J]. 计算机工程与应用, 2024, 60(12): 1-17.
LI Chun, WU Zhizhou, ZENG Guang, ZHAO Xin, YANG Zhidan. Review of Connected Autonomous Vehicle Cooperative Control at On-Ramp Merging Areas[J]. Computer Engineering and Applications, 2024, 60(12): 1-17.
[1] BISHOP R. A survey of intelligent vehicle applications worldwide[C]//Proceedings of the IEEE Intelligent Vehicles Symposium, 2000: 25-30. [2] FARRADYNE P. Vehicle infrastructure integration (VII): architecture and functional requirements[J]. Draft Version, 2005(1). [3] SHELL M. Final report of the European safety working group on road safety[EB/OL]. (2003-01) [2023-09-01]. http://www.esafetysupport.info/download/28_recommendations/28_Recommendations.pdf. [4] 中华人民共和国科技部. 国家高技术研究发展计划 (863计划)现代交通技术领域智能车路协同关键技术研究导则[EB/OL]. (2006-10)[2023-09-01].https://www.most.gov.cn/ztzl/swkjjh/kjjhjj/200610/t20061021_36375.html. Ministry of Science and Technology of the People’s Republic of China. National high technology research and development program (863 Program) key technology research guidelines for intelligent vehicle-road collaboration in the field of modern transportation technology[EB/OL]. (2006-10) [2023-09-01]. https://www.most.gov.cn/ztzl/swkjjh/kjjhjj/200610/t20061021_ 36375.html. [5] YURTSEVER E, LAMBERT J, CARBALLO A, et al. A survey of autonomous driving: common practices and emerging technologies[J]. IEEE Access, 2020, 8: 58443-58469. [6] 冉润东.基于深度强化学习的高速公路入口匝道控制方法研究[D].青岛: 山东科技大学, 2019. RAN R D. Research on freeway ramp metering method based on deep reinforcement learning[D]. Qingdao: Shandong University of Science and Technology, 2019. [7] ALI Y, ZHENG Z D, HAQUE M M. Modelling lane-changing execution behavior in a connected environment: agrouped random parameters with heterogeneity-in-means approach[J]. Communications in Transportation, 2021, 1: 100009. [8] 陆海亭, 张宁, 钱振东. 高速道路入口匝道控制方法及应用探索[J]. 公路, 2008(8): 180-186. LU H T, ZHANG N, QIAN Z D. Discussion about method and application of expressway entrance ramp metering[J]. Highway, 2008(8): 180-186. [9] 王景荣, 肖鹤, 解建光. 高速公路拥堵碳排放计算模型研究[J]. 现代交通技术, 2015, 12(2): 81-84. WANG J R, XIAO H, XIE J G. Research on carbon emission calculation model for expressway congestion[J]. Modern Transportation Technology, 2015, 12(2): 81-84. [10] MERGIA W Y, EUSTACE D, CHIMBA D, et al. Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio[J]. Accident Analysis & Prevention, 2013, 55: 202-210. [11] 洛玉乐. 匝道合流区网联车辆协同控制策略研究[D]. 哈尔滨: 哈尔滨工业大学, 2022. LUO Y L. Research on cooperative controistrategy of connected vehicles in ramp merging area[D]. Harbin: Harbin Institute of Technology, 2021. [12] 林仪, 揭春雁. 细算 “堵车账” 打通塞车道[N]. 人民政协报, 2016. LIN Y, JIE C Y. Calculate the “traffic congestion account” to open up the traffic jam[N]. CPPCC Daily, 2016. [13] 林祎. 智能网联环境下基于车辆行为优化的匝道管控策略研究[D]. 南京: 东南大学, 2022. LIN Y. Ramp control method based on vehicle behavior optimization under connected and automated vehicles environment[D]. Nanjing: Southeast University, 2022. [14] 任黎立.高速公路入口匝道控制方法综述[J].交通标准化, 2006(5): 146-149. REN L L. A review of highway entrance ramp control methods[J]. Traffic Standardization, 2006(5): 146-149. [15] YANG H, RAKHA H. Reinforcement learning ramp metering control for weaving sections in a connected vehicle environment[C]//Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington, DC, 2017. [16] ZHU J, EASA S, GAO K. Merging control strategies of connected and autonomous vehicles at freeway on-ramps: a comprehensive review[J]. Journal of Intelligent and Connected Vehicles, 2022, 3(1): 1-23. [17] PUEBOOBPAPHAN R, LIU F, AREM B V. The impacts of a communication based merging assistant on traffic flows of manual and equipped vehicles at an on-ramp using traffic flow simulation[C]//Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010: 1468-1473. [18] RAN B, LEIGHT S, CHANG B. A microscopic simulation model for merging control on a dedicated-lane automated highway system[J]. Transportation Research Part C: Emerging Technologies, 1999, 7(6): 369-388. [19] WANG Z, KULIK L, RAMAMOHANARAO K. Proactive traffic merging strategies for sensor-enabled cars[C]//Proceedings of the 4th ACM International Workshop on Vehicular Ad Hoc Networks, 2007: 39-48. [20] DAAMEN W, AREM B V, BOUMA I. Microscopic dynamic traffic management: simulation of two typical situations[C]//Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, 2011: 1898-1903. [21] AWAL T, KULIK L, RAMAMOHANRAO K. Optimal traffic merging strategy for communication-and sensorenabled vehicles[C]//Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, 2013: 1468-1474. [22] XIE Y C, ZHANG H X, GARTNER N H, et al. Collaborative merging strategy for freeway ramp operations in a connected and autonomous vehicles environment[J]. Journal of Intelligent Transportation Systems, 2016, 21(2): 136-147. [23] XU L, LU J, RAN B, et al. Cooperative merging strategy for connected vehicles at highway on-ramps[J]. Journal of Transportation Engineering, 2019, 145(6): 04019022. [24] FANG Y K, MIN H G, WU X, et al. On-ramp merging strategies of connected and automated vehicles considering communication delay[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 15298-15312. [25] ZHAO C, CHU D, WANG R, et al. Consensus control of highway on-ramp merging with communication delays[J]. IEEE Transactions on Vehicular Technology, 2022, 71(9): 9127-9142. [26] TONG X L, SHI Y, ZHANG Q Y, et al. Adaptive on-ramp merging strategy under imperfect communication performance[J]. Vehicular Communications, 2023, 44: 2096-2214. [27] UNO A, SAKAGUCHI T, TSUGAWA S. A merging control algorithm based on inter-vehicle communication[C]//Proceedings of the IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, 1999: 783-787. [28] SAKAGUCHI T, UNO A, KATO S, et al. Cooperative driving of automated vehicles with inter-vehicle communications[C]//Proceedings of the 2000 IEEE Intelligent Vehicles Symposium, 2000: 516-521. [29] XU Q, SENGUPTA R. Simulation, analysis, and comparison of ACC and CACC in highway merging control[C]//Proceedings of the IEEE IV 2003 Intelligent Vehicles Symposium, 2003: 237-242. [30] CHOU F C, SHLADOVER S E, BANSAL G. Coordinated merge control based on V2V communication[C]//Proceedings of the 2016 IEEE Vehicular Networking Conference (VNC), 2016: 1-8. [31] LU X Y, TAN H S, SHLADOVER S E, et al. Modeling, design and implementation of longitudinal control algorithm for automated vehicle merging[J]. California PATH Research Report, 2000(9). [32] LU X Y, TAN H S, SHLADOVER S E, et al. Automated vehicle merging maneuver implementation for AHS[J]. Vehicle System Dynamics, 2004, 41(2): 85-107. [33] HUANG Z, ZHUANG W, YIN G, et al. Cooperative merging for multiple connected and automated vehicles at highway on-ramps via virtual platoon formation[C]//Proceedings of the 2019 Chinese Control Conference (CCC), 2019: 6709-6714. [34] MORLA R. Vision of congestion-free road traffic and cooperating objects[EB/OL]. (2005-01)[2023-09-01]. https://api.semanticscholar.org/CorpusID:18480990. [35] MARINESCU D, ?URN J, SLOT M, et al. An active approach to guaranteed arrival times based on traffic shaping[C]//Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010: 1711-1717. [36] MARINESCU D, ?URN J, BOUROCHE M, et al. On-ramp traffic merging using cooperative intelligent vehicles: a slot-based approach[C]//Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, 2012: 900-906. [37] MENG T C, HU Z Y, JIN H, et al. Collision-free control strategy for on-ramp merging: a spatial-dependent constraint following approach[J]. Journal of Physics: Conference Series, 2022, 2234: 012011. [38] 赵红专, 陈智振, 代静. 一种基于双层模糊控制的匝道合流区协同控制方法[J]. 大众科技, 2022, 24(12): 9-13. ZHAO H Z, CHEN Z Z, DAI J. A coordinated control method of ramp confluence area based on two-layer fuzzy control[J]. Popular Science & Technology, 2022, 24(12): 9-13. [39] MILANES V, GODOY J, VILLAGRA J, et al. Automated on-ramp merging system for congested traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 500-508. [40] CHIANG T C, WANG W J. Highway on-ramp control using fuzzy decision making[J]. Journal of Vibration and Control, 2011, 17(2): 205-213. [41] XU J, ZHAO X, SRINIVASAN D. On optimal freeway local ramp metering using fuzzy logic control with particle swarm optimization[J]. Intelligent Transport Systems, 2013, 7: 95-104. [42] HUANG C P, JIANG M, CHAI G. Fuzzy control for ramp metering and variable speed limitation of freeway[J]. Computer Technology and Development, 2010, 20(12): 38-41. [43] LIANG X, LI Z. Freeway ramp control based on genetic PI and fuzzy logic[C]//Proceedings of IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China, 2008: 382-387. [44] XU J, ZHAO X, SRINIVASAN D. Fuzzy logic controller for freeway ramp metering with particle swarm optimization and PARAMICS simulation[C]//Proceedings of International Conference on Fuzzy Systems, Barcelona, Spain, 2010: 1-6. [45] RAMOS G, PINTO M, COELHO F, et al. Hybrid methodology based on computational vision and sensor fusion for assisting autonomous UAV on offshore messenger cable transfer operation[J]. Robotica, 2022, 40(8): 2786-2814. [46] LI A, CAO J, LI S, et al. Map construction and path planning method for a mobile robot based on multi-sensor information fusion[J]. Applied Sciences, 2022, 12: 2913. [47] HAIDER M H, WANG Z L, KHAN A A, et al. Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion[J]. Journal of King Saud University (Computer and Information Sciences), 2022, 34(10): 1319-1578. [48] SONG R, HEGDE A, SENEL N, et al. Edge-aided sensor data sharing in vehicular communication networks[C]//Proceedings of the IEEE 95th Vehicular Technology Conference (VTC2022-Spring), Helsinki, Finland, 2022: 1-7. [49] KOSURU V S R, VENKITARAMAN A K. A smart battery management system for electric vehicles using deep learning-based sensor fault detection[J].?World Electric Vehicle Journal, 2023, 14: 101. [50] FANG Y, MIN H, LEI X, et al. A self-fault diagnosis framework for sensors of connected and automated vehicles with dynamic environmental impact quantification[C]//Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 2022: 422-442. [51] ANDRADE E, MATOS F, SANTOS A. A virtual models-based CAVs platoon resilient to network and sensor attacks[J]. Ad Hoc Networks, 2023, 149: 103225. [52] MOUSAVI S S, BAHRAMI S, KOUVELAS A. Controller design for a mixed traffic system travelling at different desired speeds[J]. European Journal of Control, 2022, 68: 100698. [53] HUA Z, ZHAN J, ZHANG L. Decentralized tube model predictive control for arbitrarily mixed vehicle platoons with HDV uncertainties[C]//Proceedings of the 41st Chinese Control Conference, Hefei, China, 2022: 5429-5434. [54] SINGH N K, SAHA I. STL-based synthesis of feedback controllers using reinforcement learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2023: 15118-15126. [55] CHEN M, GUO J J, WEN C K, et al. Deep learning-based implicit CSI feedback in massive MIMO[J]. IEEE Transactions on Communications, 2022, 70(2): 935-950. [56] GONZáLEZ G W, MONTOYA O D. Active and reactive power conditioning using SMES devices with PMW-CSC: a feedback nonlinear control approach[J]. Ain Shams Engineering Journal, 2019, 10(2): 369-378. [57] AVEDISOV S S, HE C R, TAKáCS D, et al. Machine learning-based steering control for automated vehicles utilizing V2X communication[C]//Proceedings of the 2021 IEEE Conference on Control Technology and Applications (CCTA), San Diego, CA, USA, 2021: 253-258. [58] VALIENTE R, RAFTARI A, MAHJOUB H N. Context-aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems[J]. arXiv: 2212.12819, 2022. [59] ZHANG E, MASOUD N, BANDEGI M, et al. Predicting risky driving in a connected vehicle environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 17177-17188. [60] ZHANG H, RITCHIE S G, RECKER W W. Some general results on the optimal ramp control problem[J]. Transportation Research Part C, 1996, 4(2): 51-69. [61] YOSHINO T, SASAKI T, HASEGAWA T.?The traffic-control system on the hanshin expressway[J]. Interfaces, 1995, 25(1): 94-108. [62] HEGYI A, SCHUTTER B D, HEELENDOORN J. MPC-based optimal coordination of variable speed limits to suppress shock waves in freeway traffic[C]//Proceedings of the 2003 American Control Conference, 2003: 4083-4088. [63] LI L, WANG F. Cooperative driving at blind crossings using intervehicle communication[J]. IEEE Transactions on Vehicular Technology, 2006, 55(6):1712-1724. [64] MULLER E, CARLSON R, JUNIOR W. Intersection control for automated vehicles with MILP[J]. IFAC-Papers OnLine, 2016, 49(3):37-42. [65] FAYAZI S, VAHIDI A, LUCKOW A. Optimal scheduling of autonomous vehicle arrivals at intelligent intersections via MILP[C]//Proceedings of the 2017 American Control Conference (ACC), 2017: 4920-4925. [66] RIOS J, MALIKOPOULOS A, PISU P. Online optimal control of connected vehicles for efficient traffic flow at merging roads[C]//Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015: 2432-2437. [67] NTOUSAKIS A, NIKOLOS I, PAPAGEORGIOU M. Optimal vehicle trajectory planning in the context of cooperative merging on highways[J]. Transportation Research Part C: Emerging Technologies, 2016, 71:464-488. [68] JIN X F, YU X H, HU Y H, et al. Integrated control of internal boundary and ramp inflows for lane-free traffic of automated vehicles on freeways[C]//Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 2022:1234-1239. [69] VISHNOI S C, JI J Y. CAV Traffic control to mitigate the impact of congestion from bottlenecks: a linear quadratic regulator approach and microsimulation study[J]. arXiv:2306.10418, 2023. [70] ZHANG H M, RECKER W W. On optimal freeway ramp control policies for congested traffic corridors[J]. Transportation Research Part B: Methodological, 1999, 3370(6):417-436. [71] KOTSIALOS A, PAPAGEORGIOU M, MIDDELHAM F. Optimal coordinated ramp metering with advanced motorway optimal control[J]. Transportation Research Record Journal of the Transportation Research Board, 2001, 1748:55-65. [72] TIAN L P, ZHU H G, ZHU X D, et al. A vehicle-road cooperative speed limit method for highway ramp merge zone traffic efficiency[J]. Highway, 2019, 64(8): 310-316. [73] CHEN D, MO F, CHEN Y, et al. Optimization of ramp locations along freeways: a dynamic programming approach[J]. Sustainability, 2022, 14: 9718. [74] PEI H, FENG S, ZHANG Y, et al. A cooperative driving strategy for merging at on-ramps based on dynamic programming[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11646-11656. [75] AHN H, VECCHIO D. Safety verification and control for collision avoidance at road intersections[J]. IEEE Transactions on Automatic Control, 2018, 63(3):630-642. [76] ZOHDY I, RAKHA H. Intersection management via vehicle connectivity: the intersection cooperative adaptive cruise control system concept[J]. Journal of Intelligent Transportation Systems, 2014, 20(1):17-32. [77] BELLEMANS T, SCHUTTER B D, MOOR B D. Model predictive control with repeated model fitting for ramp metering[C]//Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, 2002: 236-241. [78] ZHOU Y, CHOLETTE M, BHASKAR A, et al. Optimal vehicle trajectory planning with control constraints and recursive implementation for automated on-ramp merging[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3409-3420. [79] MALIKOPOULOS A, LIU Z. A closed-form analytical solution for optimal coordination of connected and automated vehicles[C]//Proceedings of the 2019 American Control Conference (ACC), 2019: 3599-3604. [80] MALIKOPOULOS A, CASSANDRAS C, ZHANG Y. A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections[J]. Automatica, 2018, 93: 244-256. [81] XIAO W, CASSANDRAS C. Decentralized optimal merging control for connected and automated vehicles with safety constraint guarantees[J]. Automatica, 2021, 123: 109333. [82] ZHANG Y, CASSANDRAS C, MALIKOPOULOS A. Optimal control of connected and automated vehicles at urban traffic intersections: a feasibility enforcement analysis[C]//Proceedings of the 2017 American Control Conference (ACC), 2017: 3548-3553. [83] CAO W, MUKAI M, KAWABE K, et al. Cooperative vehicle path generation during merging using model predictive control with real-time optimization[J]. Control Engineering Practice, 2015, 34: 98-105. [84] XIAO X, CASSANDRAS C, BELTA C. Decentralized optimal control in multi-lane merging for connected and automated vehicles[C]//Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020: 1-6. [85] IOANNIS A, NTOUSAKIS, IOANNIS K, et al. Optimal vehicle trajectory planning in the context of cooperative merging on highways[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 464-488. [86] XIAO W, CASSANDRAS C G, BELTA C. Decentralized Optimal control in multi-lane merging for connected and automated vehicles[C]//Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020: 1-6. [87] LIU J, ZHAO W, XU C. An efficient on-ramp merging strategy for connected and automated vehicles in multi-lane traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5056-5067. [88] ZHOU Y, CHUNG E, BHASKAR A, et al. A state-constrained optimal control based trajectory planning strategy for cooperative freeway mainline facilitating and on-ramp merging maneuvers under congested traffic[J]. Transportation Research Part C: Emerging Technologies, 2019, 109: 321-342. [89] 柳祖鹏, 黄镓法, 王道斌. 基于合作博弈的合流区加速路段智能网联汽车协同控制[J].交通运输研究, 2023, 9(6): 34-43. LIU Z P, HUANG J F, WANG D B. Collaborative control of inteligent connected vehicles on acceleration section of merging area based on cooperative game[J]. Transport Research, 2023, 9(6):34-43. [90] GAO Z, WU Z, HAO W, et al. Optimal trajectory planning of connected and automated vehicles at on-ramp merging area[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 12675-12687. [91] LIANG J, GUAN T, LIU D, et al. An optimal trajectory planning for automated on-ramp merging[J]. IET Intelligent Transport Systems, 2023, 17: 835-847. [92] XUE Y J, ZHANG X K, CUI Z Y, et al. A platoon-based cooperative optimal control for connected autonomous vehicles at highway on-ramps under heavy traffic[J]. Transportation Research Part C: Emerging Technologies, 2023, 150:104083. [93] JING S, HUI F, ZHAO X, et al. Integrated longitudinal and lateral hierarchical control of cooperative merging of connected and automated vehicles at on-ramps[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 24248-24262. [94] LIU C, ZHENG F F, LI R J, et al. Robust optimal control of connected automated vehicles considering dynamic platoon size in mixed traffic environment[J]. SSRN Electronic Journal, 2022(1). [95] MA G Q, WANG B Q, GE S S. Robust optimal control of connected and automated vehicle platoons through improved particle swarm optimization[J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103488. [96] HUANG M, JIANG Z P, OZBAY K. Learning-based adaptive optimal control for connected vehicles in mixed traffic: robustness to driver reaction time[J]. IEEE Transactions on Cybernetics, 2022, 52(6): 5267-5277. [97] OZAKI N, CAMPAGNOL S, FUNASE R. Tube stochastic optimal control for nonlinear constrained trajectory optimization problems[J]. Journal of Guidance, Control, and Dynamics, 2020, 43(4): 645-655. [98] MALANOWSKI K, BüSKENS C, MAURER H. Convergence of approximations to nonlinear optimal control problems[J]. Mathematical Programming with Data Perturbations, 2020(9): 253-284. [99] MERKT W X, IVAN V, DINEV T, et al. Memory clustering using persistent homology for multimodality-and discontinuity-sensitive learning of optimal control warm-starts[J]. IEEE Transactions on Robotics, 2021, 37(5): 1649-1660. [100] TANG G, HAUSER K. A data-driven indirect method for nonlinear optimal control[J]. Astrodyn, 2019, 4(3): 345-359. [101] FURUKAWA J I, MORIMOTO J. Composing an assistive control strategy based on linear bellman combination from estimated user’s motor goal[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1051-1058. [102] ZHAO J, LV Y, ZENG Q, et al. Online policy learning based output-feedback optimal control of continuous-time systems[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 71(2): 652-656. [103] JI Q L, CHEN M, WANG X V, et al. Optimal shape morphing control of 4D printed shape memory polymer based on reinforcement learning[J]. Robotics and Computer-Integrated Manufacturing, 2022, 73: 102209. [104] WU Y, ZHANG J, SHEN T. A logical network approximation to optimal control on a continuous domain and its application to HEV control[J]. Science China Information Sciences, 2022, 65: 212203. [105] BESTEHORN F, KIRCHES C. The integrated control deviation of mixed-integer optimal control problems with vanishing constraints[J]. Proceedings in Applied Mathematics and Mechanics, 2021, 20: e202000022. [106] HAN J H, VAHIDI A. Antonio sciarretta,fundamentals of energy efficient driving for combustion engine and electric vehicles: an optimal control perspective[J]. Automatica, 2019, 103: 558-572. [107] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518: 529-533. [108] HAYDARI A, YILMAZ Y. Deep reinforcement learning for intelligent transportation systems: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(1): 11-32. [109] LIN L J. Reinforcement learning for robots using neural networks[D]. Pennsylvania: Carnegie Mellon University, 1993. [110] QIAO L, BAO H, XUAN Z X, et al. Driverless ramp convergence model based on reinforcement earning[J]. Computer Engineering, 2018, 44(7): 2024-2031. [111] WANG H J, GAO H B, YUAN S H, et al. Interpretable decision-making for autonomous vehicles at highway on-ramps with latent space reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2021, 70(9): 8707-8719. [112] TOMOKI N, PRASHANT D, DANIL V. Prokhorov merging in congested freeway traffic using multipolicy decision making and passive actor-critic learning[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(2): 287-297. [113] WANG P, CHAN C. Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge[C]//Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, Japan, 2017: 1-6. [114] LIU J, ZHAO W, XU C. An efficient on-ramp merging strategy for connected and automated vehicles in multi-lane traffic[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5056-5067. [115] LIN Y, MCPHEE J, AZAD N. Anti-jerk on-ramp merging using deep reinforcement learning[C]//Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), 2020:7-14. [116] HU Y, NAKHAEI A, TOMIZUKA M, et al. Interaction-aware decision making with adaptive strategies under merging scenarios[C]//Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019: 151-158. [117] CHEN T Y, WANG M, GONG S Y, et al. Connected and automated vehicle distributed control for on-ramp merging scenario: a virtual rotation approach[J]. Transportation Research Part C: Emerging Technologies, 2021, 133:103451. [118] ANDERSSON O, HEINTZ F, DOHERTY P. Model-based reinforcement learning in continuous environments using real-time constrained optimization[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2015: 2497-2503. [119] KAMTHE S, DEISENROTH M P. Data-efficient reinforcement learning with probabilistic model predictive control[J]. arXiv:1706.06491,2017. [120] LEE G, SRINIVASA S S, MASON M Y. Data-driven robust optimal control for uncertain nonlinear dynamical systems[J]. arXiv:1705.05344, 2017. [121] DEISENROTH M P, FOX D, RASMUSSEN C E. Gaussian processes for data-efficient learning in robotics and control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(2): 408-423. [122] LUO F M, XU T, LAI H, et al. A survey on model-based reinforcement learning[J]. arXiv:2206.09328, 2022. [123] LEE H, KIM K, KIM N, et al. Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning[J]. Applied Energy, 2022, 313: 118460. [124] NIEKERK B V, DAMIANOU A, ROSMAN B. Online constrained model-based reinforcement learning[J]. arXiv:2004.03499,2020. [125] ZHANG W, WANG J X, XU Z Y. A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning[J]. Energy, 2022, 260:124849. [126] ZHOU S, ZHUANG W, YIN G, et al. Cooperative on-ramp merging control of connected and automated vehicles: distributed multi-agent deep reinforcement learning approach[C]//Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 2022: 402-408. [127] MAHATTHANAJATUPHAT C, SRISOMBOON K, LEE W, et al. Investigation of multi-agent reinforcement learning on merge ramp for avoiding car crash on highway[C]//Proceedings of the 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Phuket, Thailand, 2022: 1050-1053. [128] LE N. Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways[J]. Journal of Information and Telecommunication, 2023, 14(6): 348. [129] LIU Q, LI Z, LI X, et al. Graph convolution-based deep reinforcement learning for multi-agent decision-making in interactive traffic scenarios[C]//Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 2022: 4074-4081. [130] ZHANG H, ZHANG S. Multi-agent reinforcement learning[J]. Deep Reinforcement Learning, 2020 (1): 335-346. [131] AJAO L A, APEH S T. Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning[J]. Intelligent Systems with Applications, 2023, 18: 200216. [132] CHEN H Y, ZHANG Y, BHATTI U A, et al. Safe decision controller for autonomous drivingbased on deep reinforcement learning in nondeterministic environment[J]. Sensors, 2023, 23: 1198. [133] HARRIS A, VALADE T, TEIL T, et al. Generation of spacecraft operations procedures using deep reinforcement learning[J]. Journal of Spacecraft and Rockets, 2022, 59(2): 611-626. [134] HU J, WANG L, HU T, et al. Autonomous maneuver decision making of dual-UAV cooperative air combat based on deep reinforcement learning[J]. Electronics, 2022, 11: 467. [135] JIN C, YANG Z R, WANG Z R, et al. Provably efficient reinforcement learning with linear function approximation[J]. arXiv:1907.05388, 2019. [136] HU P, CHEN Y, HUANG L B. Nearly minimax optimal reinforcement learning with linear function approximation[J]. arXiv:2206.11489, 2022. [137] ZHAN G, ZHANG X, LI Z, et al. Multiple-UAV reinforcement learning algorithm based on improved ppo in ray framework[J]. Drones, 2022, 6: 166. [138] LIU B, DING Z. A distributed deep reinforcement learning method for traffic light control[J]. Neurocomputing, 2022, 490: 390-399. [139] WANG H C, HUANG S C, HUANG P J, et al. Curriculum reinforcement learning from avoiding collisions to navigating among movable obstacles in diverse environments[J]. IEEE Robotics and Automation Letters, 2023, 8(5): 2740-2747. [140] WANG B, XIE J, ATANASOV N. DARL1N: distributed multi-agent reinforcement learning with one-hop neighbors[C]//Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022: 9003-9010. [141] FU Y, LI C, YU F R, et al. A selective federated reinforcement learning strategy for autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 1655-1668. [142] LUO S, KASAEI H, SCHOMAKER L. Accelerating reinforcement learning for reaching using continuous curriculum learning[C]//Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020: 1-8. [143] SONG Y, LIN H, KAUFMANN E, et al. Autonomous overtaking in gran turismo sport using curriculum reinforcement learning[C]//Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021: 9403-9409. [144] GUPTA K, MUKHERJEE D, NAJJARAN H. Extending the capabilities of reinforcement learning through curriculum: a review of methods and applications[J]. SN Computer Science, 2022, 3(1): 28. [145] HUANG Z, WU J, LV C. Efficient deep reinforcement learning with imitative expert priors for autonomous driving[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 7391-7403. [146] 章程, 赵靖, 杨晓光, 等. 城市快速路入口匝道控制研究综述[J]. 上海理工大学学报, 2023, 45(4): 332-344. ZHANG C, ZHAO J, YANG X G, et al. A review of ramp metering for urban expressway[J]. Journal of University of Shanghai for Science and Technology, 2023, 45(4): 332-344. |
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