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Lu, N. Cheng, N. Zhang, X. Shen, and J.W. Mark, \u201cConnected vehicles: Solutions and challenges,\u201d IEEE Internet Things J., vol.1, no.4, pp.289-299, May 2014. 10.1109\/jiot.2014.2327587","DOI":"10.1109\/JIOT.2014.2327587"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] M. Gerla, E.K. Lee, G. Pau, and U. Lee, \u201cInternet of vehicles: From intelligent grid to autonomous cars and vehicular clouds,\u201d Proc. IEEE World Forum on Internet of Things (WF-IoT), pp.241-246, March 2014. 10.1109\/wf-iot.2014.6803166","DOI":"10.1109\/WF-IoT.2014.6803166"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] V. Va, T. Shimizu, G. Bansal, and R.W. Heath, Jr., \u201cMillimeter wave vehicular communications: A survey,\u201d Foundations and Trends in Networking, vol.10, no.1, pp.1-113, June 2016. 10.1561\/1300000054","DOI":"10.1561\/1300000054"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] M. Gerla, \u201cVehicular cloud computing,\u201d 2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp.152-155, June 2012. 10.1109\/medhocnet.2012.6257116","DOI":"10.1109\/MedHocNet.2012.6257116"},{"key":"5","unstructured":"[5] V. Semkin, U. Virk, A. Karttunen, K. Haneda, and A.V. R\u00e4is\u00e4nen, \u201cE-band propagation channel measurements in an urban street canyon,\u201d 9th European Conference on Antennas and Propagation (EuCAP), pp.1-4, IEEE, April 2015."},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] A. Yamamoto, K. Ogawa, T. Horimatsu, A. Kato, and M. Fujise, \u201cPath-loss prediction models for intervehicle communication at 60GHz,\u201d IEEE Trans. Veh. Technol., vol.57, no.1, pp.65-78, Jan. 2008. 10.1109\/tvt.2007.901890","DOI":"10.1109\/TVT.2007.901890"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] C. Wu, T. Yoshinaga, and Y. Ji, \u201cCooperative content delivery in vehicular networks with integration of sub-6GHz and mmWave,\u201d Globecom Workshops, 2017 IEEE, pp.1-6, IEEE, Dec. 2017. 10.1109\/glocomw.2017.8269050","DOI":"10.1109\/GLOCOMW.2017.8269050"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] H. Zhou, B. Liu, T.H. Luan, F. Hou, L. Gui, Y. Li, Q. Yu, and X. Shen, \u201cChaincluster: Engineering a cooperative content distribution framework for highway vehicular communications,\u201d IEEE Trans. Intell. Transp. Syst., vol.15, no.6, pp.2644-2657, Dec. 2014. 10.1109\/tits.2014.2321293","DOI":"10.1109\/TITS.2014.2321293"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] A. Benslimane, T. Taleb, and R. Sivaraj, \u201cDynamic clustering-based adaptive mobile gateway management in integrated VANET-3G heterogeneous wireless networks,\u201d IEEE J. Sel. Areas Commun., vol.29, no.3, pp.559-570, March 2011. 10.1109\/jsac.2011.110306","DOI":"10.1109\/JSAC.2011.110306"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] P. Salvo, F. Cuomo, A. Baiocchi, and A. Bragagnini, \u201cRoad side unit coverage extension for data dissemination in VANETs,\u201d Wireless On-demand Network Systems and Services (WONS), 2012 9th Annual Conference on, pp.47-50, IEEE, Jan. 2012. 10.1109\/wons.2012.6152235","DOI":"10.1109\/WONS.2012.6152235"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] M. Amoozadeh, H. Deng, C.N. Chuah, H.M. Zhang, and D. Ghosal, \u201cPlatoon management with cooperative adaptive cruise control enabled by VANET,\u201d Vehicular Communications, vol.2, no.2, pp.110-123, April 2015. 10.1016\/j.vehcom.2015.03.004","DOI":"10.1016\/j.vehcom.2015.03.004"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] P. Fernandes and U. Nunes, \u201cPlatooning with IVC-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow,\u201d IEEE Trans. Intell. Transp. Syst., vol.13, no.1, pp.91-106, March 2012. 10.1109\/tits.2011.2179936","DOI":"10.1109\/TITS.2011.2179936"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] J.A. Fax and R.M. Murray, \u201cInformation flow and cooperative control of vehicle formations,\u201d IEEE Trans. Autom. Control, vol.49, no.9, pp.1465-1476, Sept. 2004. 10.1109\/tac.2004.834433","DOI":"10.1109\/TAC.2004.834433"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] R. Olfati-Saber and R.M. Murray, \u201cDistributed cooperative control of multiple vehicle formations using structural potential functions,\u201d IFAC Proceedings Volumes, vol.35, no.1, pp.495-500, July 2002. 10.3182\/20020721-6-es-1901.00244","DOI":"10.3182\/20020721-6-ES-1901.00244"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] P. Basu and J. Redi, \u201cMovement control algorithms for realization of fault-tolerant ad hoc robot networks,\u201d IEEE Netw., vol.18, no.4, pp.36-44, July 2004. 10.1109\/mnet.2004.1316760","DOI":"10.1109\/MNET.2004.1316760"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] S. Gundry, J. Zou, C.S. Sahin, J. Kusyk, and M.U. Uyar, \u201cAutonomous and fault tolerant vehicular self deployment mechanisms in MANETs,\u201d Technologies for Homeland Security (HST), 2013 IEEE International Conference on, pp.595-600, IEEE, Nov. 2013. 10.1109\/ths.2013.6699071","DOI":"10.1109\/THS.2013.6699071"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] A. Taya, T. Nishio, M. Morikura, and K. Yamamoto, \u201cCoverage expansion through dynamic relay vehicle deployment in mmWave V2I communications,\u201d 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp.1-5, IEEE, June 2018. 10.1109\/vtcspring.2018.8417763","DOI":"10.1109\/VTCSpring.2018.8417763"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] P. Raja and S. Pugazhenthi, \u201cOptimal path planning of mobile robots: A review,\u201d Int. J. Phys. Sci., vol.7, no.9, pp.1314-1320, Feb. 2012. 10.5897\/ijps11.1745","DOI":"10.5897\/IJPS11.1745"},{"key":"19","unstructured":"[19] V. Mnih, A.P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, \u201cAsynchronous methods for deep reinforcement learning,\u201d The 33rd International Conference on Machine Learning, pp.1928-1937, June 2016."},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] D. Jiang, V. Taliwal, A. Meier, W. Holfelder, and R. Herrtwich, \u201cDesign of 5.9GHz DSRC-based vehicular safety communication,\u201d IEEE Wireless Commun., vol.13, no.5, pp.36-43, Oct. 2006. 10.1109\/wc-m.2006.250356","DOI":"10.1109\/WC-M.2006.250356"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] R. Molina-Masegosa and J. Gozalvez, \u201cLTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to-everything communications,\u201d IEEE Veh. Technol. Mag., vol.12, no.4, pp.30-39, Dec. 2017. 10.1109\/mvt.2017.2752798","DOI":"10.1109\/MVT.2017.2752798"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] R. Verdone, \u201cMultihop R-ALOHA for intervehicle communications at millimeter waves,\u201d IEEE Trans. Veh. Technol., vol.46, no.4, pp.992-1005, Nov. 1997. 10.1109\/25.653073","DOI":"10.1109\/25.653073"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] T. Paul, T.R. Krogstad, and J.T. Gravdahl, \u201cModelling of UAV formation flight using 3D potential field,\u201d Simul. Model. Pract. Theory, vol.16, no.9, pp.1453-1462, Oct. 2008. 10.1016\/j.simpat.2008.08.005","DOI":"10.1016\/j.simpat.2008.08.005"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] X. Yu, W. Huang, J. Lan, and X. Qian, \u201cA novel virtual force approach for node deployment in wireless sensor network,\u201d Proc. IEEE 8th International Conference on Distributed Computing in Sensor Systems, pp.359-363, May 2012. 10.1109\/dcoss.2012.32","DOI":"10.1109\/DCOSS.2012.32"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] J. Chen, S. Li, and Y. Sun, \u201cNovel deployment schemes for mobile sensor networks,\u201d Sensors, vol.7, no.11, pp.2907-2919, Nov. 2007. 10.3390\/s7112907","DOI":"10.3390\/S7112907"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] W. Roh, J.Y. Seol, J. Park, B. Lee, J. Lee, Y. Kim, J. Cho, K. Cheun, and F. Aryanfar, \u201cMillimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results,\u201d IEEE Commun. Mag., vol.52, no.2, pp.106-113, Feb. 2014. 10.1109\/mcom.2014.6736750","DOI":"10.1109\/MCOM.2014.6736750"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] E.M. Mohamed, K. Sakaguchi, and S. Sampei, \u201cMillimeter wave beamforming based on WiFi fingerprinting in indoor environment,\u201d IEEE International Conference on Communication Workshop (ICCW), pp.1155-1160, IEEE, June 2015. 10.1109\/iccw.2015.7247333","DOI":"10.1109\/ICCW.2015.7247333"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] N. Gonz\u00e1lez-Prelcic, R. M\u00e9ndez-Rial, and R.W. Heath, \u201cRadar aided beam alignment in mmWave V2I communications supporting antenna diversity,\u201d Information Theory and Applications Workshop (ITA), pp.1-7, IEEE, Feb. 2016. 10.1109\/ita.2016.7888145","DOI":"10.1109\/ITA.2016.7888145"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] R.S. Sutton and A.G. Barto, Reinforcement learning: An introduction, MIT Press Cambridge, 1998.","DOI":"10.1109\/TNN.1998.712192"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] N.D. Nguyen, T. Nguyen, and S. Nahavandi, \u201cSystem design perspective for human-level agents using deep reinforcement learning: A survey,\u201d IEEE Access, vol.5, pp.27091-27102, Nov. 2017. 10.1109\/access.2017.2777827","DOI":"10.1109\/ACCESS.2017.2777827"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, \u201cHuman-level control through deep reinforcement learning,\u201d Nature, vol.518, no.7540, pp.529-533, Feb. 2015. 10.1038\/nature14236","DOI":"10.1038\/nature14236"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G.V. Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, \u201cMastering the game of go with deep neural networks and tree search,\u201d Nature, vol.529, no.7587, pp.484-489, Jan. 2016. 10.1038\/nature16961","DOI":"10.1038\/nature16961"},{"key":"33","unstructured":"[33] A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A.D. Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, S. Legg, V. Mnih, K. Kavukcuoglu, and D. Silver, \u201cMassively parallel methods for deep reinforcement learning,\u201d International Conference on Machine Learning (ICML) Deep Learning Workshop, July 2015."},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] R. Jiang and Q.S. Wu, \u201cThe adaptive cruise control vehicles in the cellular automata model,\u201d Phys. Lett. A, vol.359, no.2, pp.99-102, Nov. 2006. 10.1016\/j.physleta.2006.06.015","DOI":"10.1016\/j.physleta.2006.06.015"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] Y. Wang, K. Venugopal, A.F. Molisch, and R.W. Heath, \u201cAnalysis of urban millimeter wave microcellular networks,\u201d Proc. IEEE 84th Vehicular Technology Conference (VTC-Fall), pp.1-5, Sept. 2016. 10.1109\/vtcfall.2016.7880906","DOI":"10.1109\/VTCFall.2016.7880906"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] H. Okamoto, T. Nishio, M. Morikura, K. Yamamoto, D. Murayama, and K. Nakahira, \u201cMachine-learning-based throughput estimation using images for mmWave communications,\u201d Proc. IEEE 85th Vehicular Technology Conference (VTC Spring), pp.1-5, June 2017. 10.1109\/vtcspring.2017.8108570","DOI":"10.1109\/VTCSpring.2017.8108570"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] H. Okamoto, T. Nishio, M. Morikura, and K. Yamamoto, \u201cRecurrent neural network-based received signal strength estimation using depth images for mmWave communications,\u201d Proc. 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