高级搜索

智能网联交通系统的关键技术与发展

钱志鸿 田春生 郭银景 王雪

引用本文: 钱志鸿, 田春生, 郭银景, 王雪. 智能网联交通系统的关键技术与发展[J]. 电子与信息学报, doi: 10.11999/JEIT190787 shu
Citation:  Zhihong QIAN, Chunsheng TIAN, Yinjing GUO, Xue WANG. The Key Technology and Development of Intelligent and Connected Transportation System[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190787 shu

智能网联交通系统的关键技术与发展

    作者简介: 钱志鸿: 男,1957年生,教授,研究方向为无线网络通信技术,包括蓝牙、RFID,M2M,D2D、无线传感器网络及物联网等;
    田春生: 男,1993年生,博士生,研究方向为D2D通信技术与物联网;
    郭银景: 男,1966年生,教授,研究方向为网络通信、电磁兼容等;
    王雪: 女,1984年生,副教授,研究方向为5G通信中的关键技术,具体包括D2D通信的模式选择、同步技术,以及物联网技术
    通讯作者: 王雪,jluwangxue@163.com
  • 基金项目: 国家自然科学基金(61771219),吉林大学基础科研项目(SXGJQY2017-9, 2017TD-19),吉林大学研究生创新基金(101832018C022)

摘要: 该文梳理了国内外针对智能网联交通系统的相关研究,阐述了智能网联交通系统的架构和关键技术,分析了外部环境感知技术、车辆自主决策技术、控制执行技术以及车路协同技术等几个重点方向的研究进展。在分析总结已有文献的基础上,该文描述了未来智能网联交通系统的方案及其工作原理。未来智能网联交通系统应具备全程路径规划和精准定位功能,运用实时动态定位(RTK)技术和合成孔径雷达(SAR)技术,对运动或非运动物体(包括未装载GPS的物体)进行探测和定位,并保证在GPS信号弱或无信号(如隧道、室内)环境下和近距离、非可视情况下探测信号的连续性。系统还将运用移动边缘计算(MEC)理论,解决低时延、大规模网络接入等关键问题,运用大数据、云计算、物联网(IoTs)和移动通信技术,实现具有全局性、网络化的智能网联交通系统。

English

    1. [1]

      钱志鸿, 王义君. 面向物联网的无线传感器网络综述[J]. 电子与信息学报, 2013, 35(1): 215–227.
      QIAN Zhihong and WANG Yijun. Internet of things-oriented wireless sensor networks review[J]. Journal of Electronics &Information Technology, 2013, 35(1): 215–227.

    2. [2]

      钱志鸿, 王义君. 物联网技术与应用研究[J]. 电子学报, 2012, 40(5): 1023–1029.
      QIAN Zhihong and WANG Yijun. IoT technology and application[J]. Acta Electronica Sinica, 2012, 40(5): 1023–1029.

    3. [3]

      QIU Tie, CHEN Ning, LI Keqiu, et al. How can heterogeneous internet of things build our future: A survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 2011–2027. doi: 10.1109/COMST.2018.2803740

    4. [4]

      KAIWARTYA O, ABDULLAH A H, CAO Yue, et al. Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects[J]. IEEE Access, 2016, 4: 5356–5373. doi: 10.1109/ACCESS.2016.2603219

    5. [5]

      ZHENG Kan, ZHENG Qiang, CHATZIMISIOS P, et al. Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2377–2396. doi: 10.1109/COMST.2015.2440103

    6. [6]

      KU I, LU You, GERLA M, et al. Towards software-defined VANET: Architecture and services[C]. Proceedings of the 2014 13th Annual Mediterranean Ad Hoc Networking Workshop, Piran, Slovenia, 2014: 103–110. doi: 10.1109/MedHocNet.2014.6849111.

    7. [7]

      ZHENG Kan, HOU Lu, MENG Hanlin, et al. Soft-defined heterogeneous vehicular network: Architecture and challenges[J]. IEEE Network, 2016, 30(4): 72–80. doi: 10.1109/MNET.2016.7513867

    8. [8]

      DOS REIS FONTES R, CAMPOLO C, ROTHENBERG C, et al. From theory to experimental evaluation: Resource management in software-defined vehicular networks[J]. IEEE Access, 2017, 5: 3069–3076. doi: 10.1109/ACCESS.2017.2671030

    9. [9]

      CAMPOLO C, MOLINARO A, IERA A, et al. 5G network slicing for vehicle-to-everything services[J]. IEEE Wireless Communications, 2017, 24(6): 38–45. doi: 10.1109/MWC.2017.1600408

    10. [10]

      HE Jianhua, TANG Zuoyin, FAN Zhong, et al. Enhanced collision avoidance for distributed LTE vehicle to vehicle broadcast communications[J]. IEEE Communications Letters, 2018, 22(3): 630–633. doi: 10.1109/LCOMM.2018.2791399

    11. [11]

      SHI Weisen, ZHOU Haibo, LI Junling, et al. Drone assisted vehicular networks: Architecture, challenges and opportunities[J]. IEEE Network, 2018, 32(3): 130–137. doi: 10.1109/MNET.2017.1700206

    12. [12]

      SU Zhou, HUI Yilong, and YANG Qing. The next generation vehicular networks: A content-centric framework[J]. IEEE Wireless Communications, 2017, 24(1): 60–66. doi: 10.1109/MWC.2017.1600195WC

    13. [13]

      BITAM S, MELLOUK A, and ZEADALLY S. VANET-cloud: A generic cloud computing model for vehicular ad hoc networks[J]. IEEE Wireless Communications, 2015, 22(1): 96–102. doi: 10.1109/MWC.2015.7054724

    14. [14]

      LI Wenjia and SONG Houbing. ART: An attack-resistant trust management scheme for securing vehicular ad hoc networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4): 960–969. doi: 10.1109/TITS.2015.2494017

    15. [15]

      MAHMUD K, TOWN G E, MORSALIN S, et al. Integration of electric vehicles and management in the internet of energy[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 4179–4203. doi: 10.1016/j.rser.2017.11.004

    16. [16]

      YANG Helin, XIE Xianzhong, and KADOCH M. Intelligent resource management based on reinforcement learning for ultra-reliable and low-latency IoV communication networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4157–4169. doi: 10.1109/TVT.2018.2890686

    17. [17]

      ZHENG Qiang, ZHENG Kan, CHATZIMISIOS P, et al. A novel link allocation method for vehicle-to-vehicle-based relaying networks[J]. Transactions on Emerging Telecommunications Technologies, 2016, 27(1): 64–73. doi: 10.1002/ett.2790

    18. [18]

      ZHENG Qiang, ZHENG Kan, ZHANG Haijun, et al. Delay-optimal virtualized radio resource scheduling in software-defined vehicular networks via stochastic learning[J]. IEEE Transactions on Vehicular Technology, 2016, 65(10): 7857–7867. doi: 10.1109/TVT.2016.2538461

    19. [19]

      YU Zhuyue, XIE Jiayou, TANG Yuliang, et al. SMDP based cross-area resource management for vehicular cloud networks[C]. Proceedings of the 2019 IEEE 89th Vehicular Technology Conference, Kuala Lumpur, Malaysia, 2019: 1–5. doi: 10.1109/VTCSpring.2019.8746421.

    20. [20]

      ZHANG Weishan, DUAN Pengcheng, GONG Wenjuan, et al. A load-aware pluggable cloud framework for real-time video processing[J]. IEEE Transactions on Industrial Informatics, 2016, 12(6): 2166–2176. doi: 10.1109/TII.2016.2560802

    21. [21]

      WU Yuan, NI Kejie, ZHANG Cheng, et al. NOMA-assisted multi-access mobile edge computing: A joint optimization of computation offloading and time allocation[J]. IEEE Transactions on Vehicular Technology, 2018, 67(12): 12244–12258. doi: 10.1109/TVT.2018.2875337

    22. [22]

      LIN Chuncheng, DENG D, and YAO C C. Resource allocation in vehicular cloud computing systems with heterogeneous vehicles and roadside units[J]. IEEE Internet of Things Journal, 2018, 5(5): 3692–3700. doi: 10.1109/JIOT.2017.2690961

    23. [23]

      HE Ying, ZHAO Nan, and YIN Hongxi. Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 44–55. doi: 10.1109/TVT.2017.2760281

    24. [24]

      ABANI N, BRAUN T, and GERLA M. Proactive caching with mobility prediction under uncertainty in information-centric networks[C]. Proceedings of the 4th ACM Conference on Information-Centric Networking, Berlin, 2017: 88–97. doi: 10.1145/3125719.3125728.

    25. [25]

      GREWE D, WAGNER M, and FREY H. PeRCeIVE: Proactive caching in ICN-based VANETs[C]. Proceedings of the 2016 IEEE Vehicular Networking Conference, Columbus, 2016: 1–8. doi: 10.1109/VNC.2016.7835962.

    26. [26]

      范茜莹, 黄传河, 朱钧宇, 等. 无人机辅助车联网环境下干扰感知的节点接入机制[J]. 通信学报, 2019, 40(6): 90–101.
      FAN Xiying, HUANG Chuanhe, ZHU Junyu, et al. Interference-aware node access scheme in UAV-aided VANET[J]. Journal on Communications, 2019, 40(6): 90–101.

    27. [27]

      GE Xiaohu, CHENG Hui, MAO Guoqiang, et al. Vehicular communications for 5G cooperative small-cell networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(10): 7882–7894. doi: 10.1109/TVT.2016.2539285

    28. [28]

      吴黎兵, 刘冰艺, 聂雷, 等. VANET-Cellular环境下安全消息广播中继选择方法研究[J]. 计算机学报, 2017, 40(4): 1004–1016. doi: 10.11897/SP.J.1016.2017.01004
      WU Libing, LIU Bingyi, NIE Lei, et al. Research on selection of safety message broadcast relay in VANET-Cellular[J]. Chinese Journal of Computers, 2017, 40(4): 1004–1016. doi: 10.11897/SP.J.1016.2017.01004

    29. [29]

      REZGUI J and CHERKAOUI S. An M2M access management scheme for electrical vehicles[C]. Proceedings of 2017 IEEE Global Communications Conference, Singapore, 2017: 1–6. doi: 10.1109/GLOCOM.2017.8253977.

    30. [30]

      CHOI J H, HAN Y H, and MIN S. A network-based seamless handover scheme for VANETs[J]. IEEE Access, 2018, 6: 56311–56322. doi: 10.1109/ACCESS.2018.2872795

    31. [31]

      ZENG Yong, ZHANG Rui, and LIM T J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges[J]. IEEE Communications Magazine, 2016, 54(5): 36–42. doi: 10.1109/MCOM.2016.7470933

    32. [32]

      ZENG Yong and ZHANG Rui. Energy-efficient UAV communication with trajectory optimization[J]. IEEE Transactions on Wireless Communications, 2017, 16(6): 3747–3760. doi: 10.1109/TWC.2017.2688328

    33. [33]

      OUBBATI O S, LAKAS A, ZHOU Fen, et al. Intelligent UAV-assisted routing protocol for urban VANETs[J]. Computer Communications, 2017, 107: 93–111. doi: 10.1016/j.comcom.2017.04.001

    34. [34]

      XIAO Liang, LU Xiaozhen, XU Dongjin, et al. UAV relay in VANETs against smart jamming with reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5): 4087–4097. doi: 10.1109/TVT.2018.2789466

    35. [35]

      DIKMEN M and BURNS C M. Autonomous driving in the real world: Experiences with Tesla Autopilot and Summon[C]. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ann Arbor, 2016: 225–228. doi: 10.1145/3003715.3005465.

    36. [36]

      DIKMEN M and BURNS C. Trust in autonomous vehicles: The case of Tesla Autopilot and Summon[C]. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, Banff, Canada, 2017: 1093–1098. doi: 10.1109/SMC.2017.8122757

    37. [37]

      GUANETTI J, KIM Y, and BORRELLI F. Control of connected and automated vehicles: State of the art and future challenges[J]. Annual Reviews in Control, 2018, 45: 18–40. doi: 10.1016/j.arcontrol.2018.04.011

    38. [38]

      ROSENBAND D L. Inside Waymo’s self-driving car: My favorite transistors[C]. Proceedings of 2017 Symposium on VLSI Circuits, Kyoto, Japan, 2017: C20–C22. doi: 10.23919/VLSIC.2017.8008500.

    39. [39]

      LI Yan, CAO Yiqing, QIU Hong, et al. Big wave of the intelligent connected vehicles[J]. China Communications, 2016, 13(2): 27–41. doi: 10.1109/CC.2016.7405720

    40. [40]

      IMRAN A, ZOHA A, and ABU-DAYYA A. Challenges in 5G: How to empower SON with big data for enabling 5G[J]. IEEE Network, 2014, 28(6): 27–33. doi: 10.1109/MNET.2014.6963801

    41. [41]

      BENNIS M, DEBBAH M, and POOR H V. Ultrareliable and low-latency wireless communication: Tail, risk, and scale[J]. Proceedings of the IEEE, 2018, 106(10): 1834–1853. doi: 10.1109/JPROC.2018.2867029

    42. [42]

      BOTTE M, PARIOTA L, D’ACIERNO L, et al. An overview of cooperative driving in the European Union: Policies and practices[J]. Electronics, 2019, 8(6): 616. doi: 10.3390/electronics8060616

    43. [43]

      Telefónica and Huawei: Complete joint 5G-V2X PoC test in their 5G joint innovation lab at Madrid[EB/OL]. https://news.europawire.eu/telefonica-and-huawei-complete-joint-5g-v2x-poc-test-in-their-5g-joint-innovation-lab-at-madrid-53202031254/eu-press-release/2018/02/08/, 2018.

    44. [44]

      中国汽车工程学会. 节能与新能源汽车技术路线图[M]. 北京: 机械工业出版社, 2016. (请补充页码信息)
      China-SAE. Technology Roadmap for Energy Saving and New Energy Vehicles[M]. Beijing: Mechanical Industry Press, 2016.

    45. [45]

      YANG Diange, JIANG Kun, ZHAO Ding, et al. Intelligent and connected vehicles: Current status and future perspectives[J]. Science China Technological Sciences, 2018, 61(10): 1446–1471. doi: 10.1007/s11431-017-9338-1

    46. [46]

      WEI Shangguan, YU Du, GUO Chailin, et al. Survey of connected automated vehicle perception mode: From autonomy to interaction[J]. IET Intelligent Transport Systems, 2019, 13(3): 495–505. doi: 10.1049/iet-its.2018.5239

    47. [47]

      ROSIQUE F, NAVARRO P J, FERNÁNDEZ C, et al. A systematic review of perception system and simulators for autonomous vehicles research[J]. Sensors, 2019, 19(3): 648. doi: 10.3390/s19030648

    48. [48]

      TILAKARATNA S B D, WATCHAREERUETAI U, SISSHICHAI S, et al. Image analysis algorithms for vehicle color recognition[C]. Proceedings of 2017 International Electrical Engineering Congress, Pattaya, Thailand, 2017: 1–4. doi: 10.1109/IEECON.2017.8075881.

    49. [49]

      KIM H, LIU Bingbing, and MYUNG H. Road-feature extraction using point cloud and 3D LiDAR sensor for vehicle localization[C]. Proceedings of the 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, Jeju, South Korea, 2017: 891–892. doi: 10.1109/URAI.2017.7992858.

    50. [50]

      DUDÁS L, MICSKEI T, SELLER R, et al. Vehicle relative movement estimation using microwave sensor[C]. Proceedings of the 15th Conference on Microwave Techniques COMITE 2010, Brno, Czech Republic, 2010: 109–112. doi: 10.1109/COMITE.2010.5481862.

    51. [51]

      CHOI E and CHANG S. An adaptive tracking estimator for robust vehicular localization in shadowing areas[J]. IEEE Access, 2019, 7: 42436–42444. doi: 10.1109/ACCESS.2019.2907647

    52. [52]

      《中国公路学报》编辑部. 中国汽车工程学术研究综述•2017[J]. 中国公路学报, 2017, 30(6): 1–197.
      Editorial Department of China Journal of Highway and Transport. Review on China’s automotive engineering research progress: 2017[J]. China Journal of Highway and Transport, 2017, 30(6): 1–197.

    53. [53]

      SCHITO J and FABRIKANT S I. Exploring maps by sounds: Using parameter mapping sonification to make digital elevation models audible[J]. International Journal of Geographical Information Science, 2018, 32(5): 874–906. doi: 10.1080/13658816.2017.1420192

    54. [54]

      CHEN Maolin, ZHAN Xingqun, ZHANG Xin, et al. Localisation-based autonomous vehicle rear-end collision avoidance by emergency steering[J]. IET Intelligent Transport Systems, 2019, 13(7): 1078–1087. doi: 10.1049/iet-its.2018.5348

    55. [55]

      LIU Dongxu, DONG Hongzhao, LI Tiebei, et al. Vehicle scheduling approach and its practice to optimise public bicycle redistribution in Hangzhou[J]. IET Intelligent Transport Systems, 2018, 12(8): 976–985. doi: 10.1049/iet-its.2017.0274

    56. [56]

      WANG Hai, YU Yijie, CAI Yingfeng, et al. A comparative study of state-of-the-art deep learning algorithms for vehicle detection[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 11(2): 82–95. doi: 10.1109/MITS.2019.2903518

    57. [57]

      LUO Hengliang, YANG Yi, TONG Bei, et al. Traffic sign recognition using a multi-task convolutional neural network[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(4): 1100–1111. doi: 10.1109/TITS.2017.2714691

    58. [58]

      PADEN B, ČÁP M, YONG S Z, et al. A survey of motion planning and control techniques for self-driving urban vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2016, 1(1): 33–55. doi: 10.1109/TIV.2016.2578706

    59. [59]

      URMSON C, ANHALT J, BAGNELL D, et al. Autonomous driving in urban environments: Boss and the Urban Challenge[J]. Journal of Field Robotics, 2008, 25(8): 425–466. doi: 10.1002/rob.20255

    60. [60]

      MONTEMERLO M, BECKER J, BHAT S, et al. Junior: The Stanford entry in the urban challenge[J]. Journal of Field Robotics, 2008, 25(9): 569–597. doi: 10.1002/rob.20258

    61. [61]

      BACHA A, BAUMAN C, FARUQUE R, et al. Odin: Team VictorTango’s entry in the DARPA urban challenge[J]. Journal of Field Robotics, 2008, 25(8): 467–492. doi: 10.1002/rob.20248

    62. [62]

      BRECHTEL S, GINDELE T, and DILLMANN R. Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs[C]. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China, 2014: 392–399. doi: 10.1109/ITSC.2014.6957722.

    63. [63]

      LIU Wei, KIM S, PENDLETON S, et al. Situation-aware decision making for autonomous driving on urban road using online POMDP[C]. Proceedings of 2015 IEEE Intelligent Vehicles Symposium, Seoul, South Korea, 2015: 1126–1133. doi: 10.1109/IVS.2015.7225835.

    64. [64]

      WANG Tao and ZHU Zhigang. Multimodal and multi-task audio-visual vehicle detection and classification[C]. Proceedings of the 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, Beijing, China, 2012: 440–446. doi: 10.1109/AVSS.2012.47.

    65. [65]

      CHEN Zhilu and HUANG Xinming. End-to-end learning for lane keeping of self-driving cars[C]. Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, USA, 2017: 1856–1860. doi: 10.1109/IVS.2017.7995975.

    66. [66]

      SYDNEY N, PALEY D A, and SOFGE D. Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots[J]. Autonomous Robots, 2017, 41(1): 231–241. doi: 10.1007/s10514-015-9542-0

    67. [67]

      KALA R and WARWICK K. Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization[J]. Journal of Intelligent & Robotic Systems, 2013, 72(3/4): 559–590. doi: 10.1007/s10846-013-9817-7

    68. [68]

      BOHREN J, FOOTE T, KELLER J, et al. Little Ben: The ben franklin racing team’s entry in the 2007 DARPA urban challenge[J]. Journal of Field Robotics, 2008, 25(9): 598–614. doi: 10.1002/rob.20260

    69. [69]

      DECHTER R and PEARL J. Generalized best-first search strategies and the optimality of A[J]. Journal of the ACM, 1985, 32(3): 505–536. doi: 10.1145/3828.3830

    70. [70]

      LI Qianru, WEI Chen, WU Jiang, et al. Improved PRM method of low altitude penetration trajectory planning for UAVs[C]. Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China, 2014: 2651–2656. doi: 10.1109/CGNCC.2014.7007587.

    71. [71]

      CHIANG H T L and TAPIA L. COLREG-RRT: An RRT-based COLREGS-compliant motion planner for surface vehicle navigation[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 2024–2031. doi: 10.1109/LRA.2018.2801881

    72. [72]

      ZHANG Haojian, WANG Yunkuan, ZHENG Jun, et al. Path planning of industrial robot based on improved RRT algorithm in complex environments[J]. IEEE Access, 2018, 6: 53296–53306. doi: 10.1109/ACCESS.2018.2871222

    73. [73]

      QIAN Xiangjun, ALTCHÉ F, BENDER P, et al. Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective[C]. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, 2016: 205–210. doi: 10.1109/ITSC.2016.7795555.

    74. [74]

      LI Xiaohui, SUN Zhenping, CAO Dongpu, et al. Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles[J]. Mechanical Systems and Signal Processing, 2017, 87: 118–137. doi: 10.1016/j.ymssp.2015.10.021

    75. [75]

      VAHIDI A and ESKANDARIAN A. Research advances in intelligent collision avoidance and adaptive cruise control[J]. IEEE Transactions on Intelligent Transportation Systems, 2003, 4(3): 143–153. doi: 10.1109/TITS.2003.821292

    76. [76]

      ZHANG Hui and WANG Junmin. Vehicle lateral dynamics control through AFS/DYC and robust gain-scheduling approach[J]. IEEE Transactions on Vehicular Technology, 2016, 65(1): 489–494. doi: 10.1109/TVT.2015.2391184

    77. [77]

      LEFÈVRE S, CARVALHO A, and BORRELLI F. A learning-based framework for velocity control in autonomous driving[J]. IEEE Transactions on Automation Science and Engineering, 2016, 13(1): 32–42. doi: 10.1109/TASE.2015.2498192

    78. [78]

      GUANETTI J, KIM Y, and BORRELLI F. Control of connected and automated vehicles: State of the art and future challenges[J]. Annual Reviews in Control, 2018, 45: 18–40.(本条文献与第37条重复, 请核对 doi: 10.1016/j.arcontrol.2018.04.011

    79. [79]

      HAN Jingqing. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics, 2009, 56(3): 900–906. doi: 10.1109/TIE.2008.2011621

    80. [80]

      CHEN Long, CHEN Te, XU Xing, et al. Multi-objective coordination control strategy of distributed drive electric vehicle by orientated tire force distribution method[J]. IEEE Access, 2018, 6: 69559–69574. doi: 10.1109/ACCESS.2018.2877801

    81. [81]

      HUANG Jihua and TOMIZUKA M. LTV controller design for vehicle lateral control under fault in rear sensors[J]. IEEE/ASME Transactions on Mechatronics, 2005, 10(1): 1–7. doi: 10.1109/TMECH.2004.839044

    82. [82]

      TAGNE G, TALJ R, and CHARARA A. design and comparison of robust nonlinear controllers for the lateral dynamics of intelligent vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(3): 796–809. doi: 10.1109/TITS.2015.2486815

    83. [83]

      HU Chuan, WANG Rongrong, YAN Fengjun, et al. Output constraint control on path following of four-wheel independently actuated autonomous ground vehicles[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 4033–4043. doi: 10.1109/TVT.2015.2472975

    84. [84]

      LI Ye, GUO Hongda, GONG Hao, et al. The improved adaptive hybrid fuzzy control of AUV horizontal motion[C]. Proceedings of the 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, Chengdu, China, 2016: 408–414. doi: 10.1109/ICCWAMTIP.2016.8079883.

    85. [85]

      CUI Rongxin, YANG Chenguang, LI Yang, et al. Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(6): 1019–1029. doi: 10.1109/TSMC.2016.2645699

    86. [86]

      郭景华, 李克强, 罗禹贡. 智能车辆运动控制研究综述[J]. 汽车安全与节能学报, 2016, 7(2): 151–159. doi: 10.3969/j.issn.1674-8484.2016.02.003
      GUO Jinghua, LI Keqiang, and LUO Yugong. Review on the research of motion control for intelligent vehicles[J]. Journal of Automotive Safety and Energy, 2016, 7(2): 151–159. doi: 10.3969/j.issn.1674-8484.2016.02.003

    87. [87]

      LIU Kai, GONG Jianwei, KURT A, et al. A model predictive-based approach for longitudinal control in autonomous driving with lateral interruptions[C]. Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, USA, 2017: 359–364. doi: 10.1109/IVS.2017.7995745.

    88. [88]

      GUO Jinghua, LUO Yugong, LI Keqiang, et al. A novel fuzzy-sliding automatic speed control of intelligent vehicles with adaptive boundary layer[J]. International Journal of Vehicle Design, 2017, 73(4): 300–318. doi: 10.1504/IJVD.2017.10004142

    89. [89]

      PETIT J, SCHAUB F, FEIRI M, et al. Pseudonym schemes in vehicular networks: A survey[J]. IEEE Communications Surveys & Tutorials, 2015, 17(1): 228–255. doi: 10.1109/COMST.2014.2345420

    90. [90]

      DANIEL A, PAUL A, AHMAD A, et al. Cooperative intelligence of vehicles for intelligent transportation systems (ITS)[J]. Wireless Personal Communications, 2016, 87(2): 461–484. doi: 10.1007/s11277-015-3078-7

    91. [91]

      HARTMAN K and STRASSER J. Saving lives through advanced vehicle safety technology: Intelligent vehicle initiative[R]. Publication No. FHWA-JPO-05-057, 2005. (请核对作者信息)

    92. [92]

      FARRADYNE P B. Vehicle infrastructure integration (VII): VII architecture and functional requirements[R]. 2005. (请补全编号信息)

    93. [93]

      BISHOP R. A survey of intelligent vehicle applications worldwide[C]. Proceedings of the IEEE Intelligent Vehicles Symposium 2000(Cat. No.00TH8511), Dearborn, USA, 2000: 25–30. doi: 10.1109/IVS.2000.898313.

    94. [94]

      EuroRAP AISBL. Final technical implementation report-European road safety atlas[EB/OL]. https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/projects_sources/euro_safety_atlas_final_report.pdf, 2011.

    95. [95]

      张毅, 姚丹亚. 基于车路协同的智能交通系统体系框架[M]. 北京: 电子工业出版社, 2015.
      ZHANG Yi and YAO Danya. Architecture for Intelligent Transportation Systems Based on Intelligent Vehicle-Infrastructure Cooperation Systems[M]. Beijing: Publishing House of Electronics Industry, 2015.

    96. [96]

      郭戈, 许阳光, 徐涛, 等. 网联共享车路协同智能交通系统综述[J]. 控制与决策, 2019, 34(11): 2375–2389. doi: 10.13195/j.kzyjc.2019.1316
      GUO Ge, XU Yangguang, XU Tao, et al. A survey of connected shared vehicle-road cooperative intelligent transportation systems[J]. Control and Decision, 2019, 34(11): 2375–2389. doi: 10.13195/j.kzyjc.2019.1316

    97. [97]

      CHEN Shanzhi, HU Jinling, SHI Yan, et al. Vehicle-to-everything (v2x) services supported by LTE-based systems and 5G[J]. IEEE Communications Standards Magazine, 2017, 1(2): 70–76. doi: 10.1109/MCOMSTD.2017.1700015

    98. [98]

      钱志鸿, 王雪. 面向5G通信网的D2D技术综述[J]. 通信学报, 2016, 37(7): 1–14. doi: 10.11959/j.issn.1000-436x.2016129
      QIAN Zhihong and WANG Xue. Reviews of D2D technology for 5G communication networks[J]. Journal on Communications, 2016, 37(7): 1–14. doi: 10.11959/j.issn.1000-436x.2016129

    99. [99]

      田春生, 钱志鸿, 阎双叶, 等. D2D通信中联合链路共享与功率分配算法研究[J]. 电子学报, 2019, 47(4): 769–774. doi: 10.3969/j.issn.0372-2112.2019.04.001
      TIAN Chunsheng, QIAN Zhihong, YAN Shuangye, et al. Research on joint link sharing and power allocation algorithm for device-to-device communications[J]. Acta Electronica Sinica, 2019, 47(4): 769–774. doi: 10.3969/j.issn.0372-2112.2019.04.001

    100. [100]

      SHEN Xuanfan, LIAO Yong, DAI Xuewu, et al. Joint channel estimation and decoding design for 5G-enabled V2V channel[J]. China Communications, 2018, 15(7): 39–46. doi: 10.1109/CC.2018.8424581

    101. [101]

      ANUSHYA D. Vehicle monitoring for traffic violation using V2I communication[C]. Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems, Madurai, India, 2018: 1665–1669. doi: 10.1109/ICCONS.2018.8663080.

    102. [102]

      HUSSEIN A, GARCÍA F, ARMINGOL J M, et al. P2V and V2P communication for pedestrian warning on the basis of autonomous vehicles[C]. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, 2016: 2034–2039. doi: 10.1109/ITSC.2016.7795885.

    103. [103]

      MOLINA-MASEGOSA R and GOZALVEZ J. LTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to-everything communications[J]. IEEE Vehicular Technology Magazine, 2017, 12(4): 30–39. doi: 10.1109/MVT.2017.2752798

    104. [104]

      VILLARREAL-VASQUEZ M, BHARGAVA B, and ANGIN P. Adaptable safety and security in V2X systems[C]. Proceedings of 2017 IEEE International Congress on Internet of Things, Honolulu, USA, 2017: 17–24. doi: 10.1109/IEEE.ICIOT.2017.12.

    105. [105]

      HU Yan, FENG Jingjing, and CHEN Wenli. A LTE-Cellular-based V2X solution to future vehicular network[C]. Proceedings of the 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, Xi’an, China, 2018: 2658–2662. doi: 10.1109/IMCEC.2018.8469236.

    106. [106]

      DI Boya, SONG Lingyang, LI Yonghui, et al. V2X meets NOMA: Non-orthogonal multiple access for 5G-enabled vehicular networks[J]. IEEE Wireless Communications, 2017, 24(6): 14–21. doi: 10.1109/MWC.2017.1600414

    107. [107]

      DI Boya, SONG Lingyang, LI Yonghui, et al. Non-orthogonal multiple access for high-reliable and low-latency V2X communications in 5G systems[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(10): 2383–2397. doi: 10.1109/JSAC.2017.2726018

    108. [108]

      邵雯娟, 沈庆国. 软件定义的D2D和V2X通信研究综述[J]. 通信学报, 2019, 40(4): 179–194. doi: 10.11959/j.issn.1000-436x.2019075
      SHAO Wenjuan and SHEN Qingguo. Survey of software defined D2D and V2X communication[J]. Journal on Communications, 2019, 40(4): 179–194. doi: 10.11959/j.issn.1000-436x.2019075

    109. [109]

      PAK W. Fast packet classification for V2X services in 5G networks[J]. Journal of Communications and Networks, 2017, 19(3): 218–226. doi: 10.1109/JCN.2017.000039

    110. [110]

      STORCK C R and DUARTE-FIGUEIREDO F. A 5G V2X ecosystem providing internet of vehicles[J]. Sensors, 2019, 19(3): 550. doi: 10.3390/s19030550

    111. [111]

      魏志强, 毕海霞. 基于聚类识别的极化SAR图像分类[J]. 电子与信息学报, 2018, 40(12): 2795–2803. doi: 10.11999/JEIT180229
      WEI Zhiqiang and BI Haixia. PolSAR image classification based on discriminative clustering[J]. Journal of Electronics &Information Technology, 2018, 40(12): 2795–2803. doi: 10.11999/JEIT180229

    112. [112]

      SOLDIN R J. SAR target recognition with deep learning[C]. Proceedings of 2018 IEEE Applied Imagery Pattern Recognition Workshop, Washington, USA, 2018: 1–8. doi: 10.1109/AIPR.2018.8707419.

    113. [113]

      LI Tingli and DU Lan. SAR automatic target recognition based on attribute scattering center model and discriminative dictionary learning[J]. IEEE Sensors Journal, 2019, 19(12): 4598–4611. doi: 10.1109/JSEN.2019.2901050

    114. [114]

      WANG Zi, ZHAO Zhiwei, MIN Geyong, et al. User mobility aware task assignment for mobile edge computing[J]. Future Generation Computer Systems, 2018, 85: 1–8. doi: 10.1016/j.future.2018.02.014

    115. [115]

      LI Hongxing, SHOU Guochu, HU Yihong, et al. Mobile edge computing: Progress and challenges[C]. Proceedings of the 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, Oxford, UK, 2016: 83–84. doi: 10.1109/MobileCloud.2016.16.

    116. [116]

      HINTON G E, OSINDERO S, and THE Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527–1554. doi: 10.1162/neco.2006.18.7.1527

    1. [1]

      李煜, 陈杰, 张渊智. 合成孔径雷达海面溢油探测研究进展. 电子与信息学报,

    2. [2]

      张海波, 李虎, 陈善学, 贺晓帆. 超密集网络中基于移动边缘计算的任务卸载和资源优化. 电子与信息学报,

    3. [3]

      张海波, 荆昆仑, 刘开健, 贺晓帆. 车联网中一种基于软件定义网络与移动边缘计算的卸载策略. 电子与信息学报,

    4. [4]

      王汝言, 梁颖杰, 崔亚平. 车辆网络多平台卸载智能资源分配算法. 电子与信息学报,

    5. [5]

      邹虹, 高毅爽, 闫俊杰. 带有卸载时延感知的边缘云增强FiWi网络节能机制. 电子与信息学报,

    6. [6]

      赵星, 彭建华, 游伟. 基于Lyapunov优化的隐私感知计算卸载方法. 电子与信息学报,

    7. [7]

      吴元. 一种基于参数更新的机载SAR图像目标定位方法. 电子与信息学报,

    8. [8]

      贺丰收, 何友, 刘准钆, 徐从安. 卷积神经网络在雷达自动目标识别中的研究进展. 电子与信息学报,

    9. [9]

      刘亚波, 刘霖, 童智勇, 喻忠军. S波段高分辨宽幅SAR辐射定标及误差分析方法. 电子与信息学报,

    10. [10]

      王玉莹, 张志敏, 李宁, 范怀涛, 赵庆超. 高分宽幅SAR系统下的方位多通道运动目标成像算法研究. 电子与信息学报,

    11. [11]

      王超, 王岩飞, 王琦, 詹学丽. 基于回波序列最小二乘拟合的高分辨率SAR运动目标速度估计. 电子与信息学报,

    12. [12]

      潘洁, 王帅, 李道京, 卢晓春. 基于方向图和多普勒相关系数的天基阵列SAR通道相位误差补偿方法. 电子与信息学报,

    13. [13]

      闫贺, 王珏, 黄佳, 王旭东. 基于二维速度搜索的星载SAR运动目标聚焦算法研究. 电子与信息学报,

    14. [14]

      杨磊, 李埔丞, 李慧娟, 方澄. 稳健高效通用SAR稀疏特征增强算法. 电子与信息学报,

    15. [15]

      孟智超, 卢景月, 谢朋飞, 张磊, 王虹现. 无人机载多普勒分集前视合成孔径雷达成像方法. 电子与信息学报,

    16. [16]

      孙光才, 王裕旗, 高昭昭, 江帆, 邢孟道, 保铮. 一种基于短合成孔径的双星干涉精确定位方法. 电子与信息学报,

    17. [17]

      汪海波, 黄文华, 巴涛, 姜悦. 短脉冲非相参雷达的逆合成孔径成像及其稀疏恢复成像技术. 电子与信息学报,

    18. [18]

      殷礼胜, 唐圣期, 李胜, 何怡刚. 基于整合移动平均自回归和遗传粒子群优化小波神经网络组合模型的交通流预测. 电子与信息学报,

    19. [19]

      代美玲, 刘周斌, 郭少勇, 邵苏杰, 邱雪松. 基于终端能耗和系统时延最小化的边缘计算卸载及资源分配机制. 电子与信息学报,

    20. [20]

      王亚涛, 曾小东, 周龙建. 雷达间歇辐射对测向交叉定位性能的影响分析. 电子与信息学报,

  • 图 1  智能网联交通系统结构示意图

    图 2  直接式纵向结构控制

    图 3  分层式纵向结构控制

    图 4  V2X通信场景

    图 5  SD-V2X通信基本结构

    图 6  智能网联交通系统未来发展架构

    图 7  智能网联交通移动边缘计算体系结构

    表 1  3种不同感知技术对比

    感知技术优点缺点感知范围
    视觉感知实时性好,能耗较低,获取的信息量丰富感知结果易受外界环境影响,3维物体
    识别精度较低
    最远可实现250 m范围内物体的感知
    激光感知可精准识别3维物体距离信息,感知结果
    不易受外界环境影响
    体积大,价格昂贵,无法完成无距离
    差异平面内物体感知
    可完成300 m范围内直径1 cm物体的感知
    微波感知可精准识别3维物体距离信息,感知结果
    不易受外界环境影响
    无法完成无距离差异平面内物体感知取决于传感器的波长,一般可完成8~
    10 m内物体的感知
    下载: 导出CSV

    表 2  不同控制执行技术的对比

    控制执行技术优点缺点
    横向
    控制
    经典控制理论PID结构简单,可操作性好线性模型,在多变量以及时变控制系统中
    具有局限性
    现代控制理论最优控制可使系统性能达到最优对数学模型的依赖性较高
    滑模控制非线性模型,系统鲁棒性好,响应速度较快控制结果受外界不确定性影响较大
    自适应控制对外部环境变化具有较强的鲁棒性方法实时性相对较差
    模糊控制无需借助精确的数学模型,对外部环境变化
    具有较强的鲁棒性
    需借助研究人员的经验设置模糊规则
    纵向
    控制
    直接式结构控制系统集成度高过于依赖系统状态信息,模型非线性度较高
    分层式结构控制结构简单,易于实现,开发难度较低忽略了参数不确定性以模型误差的影响,
    建模准确性相对较低
    下载: 导出CSV
  • 加载中
图(7)表(2)
计量
  • PDF下载量:  13
  • 文章访问数:  66
  • HTML全文浏览量:  53
文章相关
  • 通讯作者:  王雪, jluwangxue@163.com
  • 收稿日期:  2019-10-16
  • 网络出版日期:  2019-11-30
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

/

返回文章