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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Teng Liu, Hong Wang, Bin Tian, Yunfeng Ai and Long Chen, "Parallel Distance: A New Paradigm of Measurement for Parallel Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1169-1178, July 2020. doi: 10.1109/JAS.2019.1911633
Citation: Teng Liu, Hong Wang, Bin Tian, Yunfeng Ai and Long Chen, "Parallel Distance: A New Paradigm of Measurement for Parallel Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1169-1178, July 2020. doi: 10.1109/JAS.2019.1911633

Parallel Distance: A New Paradigm of Measurement for Parallel Driving

doi: 10.1109/JAS.2019.1911633
Funds:  The work was supported in part by the National Natural Science Foundation of China (61533019, 91720000), Beijing Municipal Science and Technology Commission (Z181100008918007), and the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV)
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  • In this paper, a new paradigm named parallel dis-tance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then, Markov chain (MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.

     

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  • [1]
    S. Parkinson, P. Ward, K. Wilson, and J. Miller, “Cyber threats facing autonomous and connected vehicles: Future challenges,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 11, pp. 2898–2915, Nov. 2017. doi: 10.1109/TITS.2017.2665968
    [2]
    X. L. Meng, S. Roberts, Y. J. Cui, Y. Gao, Q. S. Chen, C. Xu, and et al., “Required navigation performance for connected and autonomous vehicles: Where are we now and where are we going?” Transportation Planning and Technology, vol. 41, pp. 104–118, Nov. 2018. doi: 10.1080/03081060.2018.1402747
    [3]
    C. Lv, Y. H. Liu, X. S. Hu, H. Y. Guo, D. P. Cao, and F.-Y. Wang, “Simultaneous observation of hybrid states for cyber-physical systems: A case study of electric vehicle powertrain,” IEEE Trans. Cybernetics, vol. 48, no. 8, pp. 2357–2367, 2018. doi: 10.1109/TCYB.2017.2738003
    [4]
    T. Liu, H. L. Yu, H. Y. Guo, Y. C. Qin, and Y. Zou, “Online energy management for multimode plug-in hybrid electric vehicles,” IEEE Trans. Industrial Informatics, vol. 15, no. 7, pp. 4352–4361, Jul. 2019.
    [5]
    M. Aeberhard, S. Rauch, M. Bahram, G. Tanzmeister, J. Thomas, Y. Pilat, F. Homm, W. Huber, and N. Kaempchen, “Experience, results and lessons learned from automated driving on Germany’s highways,” IEEE Intell. Transp. Syst. Mag., vol. 7, no. 1, pp. 42–57, 2015. doi: 10.1109/MITS.2014.2360306
    [6]
    T. Liu and X. S. Hu, “A bi-level control for energy efficiency improvement of a hybrid tracked vehicle,” IEEE Trans. Ind. Informat., vol. pp, no. 99, pp. 1–1, 2018.
    [7]
    C. Lv, D. P. Cao, Y. F. Zhao, D. J. Auger, M. Sullman, H. J. Wang, L. M. Dutka, L. Skrypchuk, and A. Mouzakitis, “Analysis of autopilot disengagements occurring during autonomous vehicle testing,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 58–68, 2018. doi: 10.1109/JAS.2017.7510745
    [8]
    F.-Y. Wang, “Parallel system methods for management and control of complex systems,” Control Decision, vol. 19, no. 5, pp. 485–489, May 2004.
    [9]
    F.-Y. Wang, “The emergence of intelligent enterprises: From CPS to CPSS,” IEEE Intell. Syst., vol. 25, no. 4, pp. 85–88, Jul.–Aug. 2010. doi: 10.1109/MIS.2010.104
    [10]
    F.-Y. Wang, “Artificial societies, computational experiments, and parallel systems: A discussion on computational theory of complex social economic systems,” Complex Syst. Complexity Sci., vol. 1, no. 4, pp. 25–35, Oct. 2004.
    [11]
    F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630–638, Sep. 2010. doi: 10.1109/TITS.2010.2060218
    [12]
    F.-Y. Wang, N. N. Zheng, D. P. Cao, C. M. Martinez, L. Li, and T. Liu, “Parallel driving in CPSS: A unified approach for transport automation and vehicle intelligence,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 577–587, 2017. doi: 10.1109/JAS.2017.7510598
    [13]
    T. Liu, X. S. Hu, S. B. E. Li, and D. P. Cao, “Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 4, pp. 1497–1507, 2017. doi: 10.1109/TMECH.2017.2707338
    [14]
    F.-Y. Wang, “Scanning the issue and beyond: Parallel driving with software vehicular robots for safety and smartness,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 4, pp. 1381–1387, Aug. 2014. doi: 10.1109/TITS.2014.2342451
    [15]
    F.-Y. Wang, “Control 5.0: From Newton to Merton in popper’s cyber social physical spaces,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 3, pp. 233–234, Jul. 2016. doi: 10.1109/JAS.2016.7508796
    [16]
    T. Liu, Y. Zou, D. X. Liu, and F. C. Sun, “Reinforcement learning of adaptive energy management with transition probability for a hybrid electric tracked vehicle,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7837–7846, 2015. doi: 10.1109/TIE.2015.2475419
    [17]
    L. Li, D. Wen, N. N. Zheng, and L. C. Shen, “Cognitive cars: A new frontier for ADAS research,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 395–407, Mar. 2012. doi: 10.1109/TITS.2011.2159493
    [18]
    Y. Zou, T. Liu, D. X. Liu, and F. C. Sun, “Reinforcement learning-based real-time energy management for a hybrid tracked vehicle,” Appl. Energy, vol. 171, pp. 372–382, 2016. doi: 10.1016/j.apenergy.2016.03.082
    [19]
    L. Li, Y. L. Lin, D. P. Cao, N. N. Zheng, and F.-Y. Wang, “Parallel learning − a new framework for machine learning,” Acta Autom. Sinica, vol. 43, no. 1, pp. 1–18, Jan. 2017.
    [20]
    F.-Y. Wang, “Agent-based control strategies for smart and safe vehicles,” in Proc. IEEE Int. Conf. on Vehicular Electronics and Safety, Shaanxi, China, 2005, pp. 331–332.
    [21]
    N. Zhang, F.-Y. Wang, F. H. Zhu, D. B. Zhao, and S. M. Tang, “DynaCAS: Computational experiments and decision support for ITS,” IEEE Intell. Syst., vol. 23, no. 6, pp. 19–23, Nov.–Dec. 2008. doi: 10.1109/MIS.2008.101
    [22]
    T. Liu, Y. Zou, D. X. Liu, and F. C. Sun, “Reinforcement learning-based energy management strategy for a hybrid electric tracked vehicle,” Energies, vol. 8, no. 7, pp. 7243–7260, 2015. doi: 10.3390/en8077243
    [23]
    F.-Y. Wang, X. Wang, L. X. Li, and L. Li, “Steps toward parallel intelligence,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 4, pp. 345–348, Oct. 2016. doi: 10.1109/JAS.2016.7510067
    [24]
    T. Liu, B. Tian, Y. F. Ai, L. Li, D. P. Cao, and F.-Y. Wang, “Parallel reinforcement learning: A framework and case study,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 65–73, 2018.
    [25]
    T. Liu, X. L. Tang, H. Wang, H. L. Yu, and X. S. Hu, “Adaptive hierarchical energy management design for a plug-in hybrid electric vehicle,” IIEEE Trans. Vehicular Technology, vol. 68, no. 12, pp. 11513–11522, 2019.
    [26]
    D. P. Filev and I. Kolmanovsky, “Markov chain modeling approaches for on board applications,” in Proc. American Control Conf., pp. 4139–4145, 2010.
    [27]
    D. P. Filev and I. Kolmanovsky, “Generalized Markov models for real-time modeling of continuous systems,” IEEE Trans. Fuzzy. Syst., vol. 22, pp. 983–998, 2014. doi: 10.1109/TFUZZ.2013.2279535
    [28]
    B. Fuglede and F. Topsoe, “Jensen-Shannon divergence and Hilbert space embedding,” in Proc. Int. Symposium on Information Theory, ISIT 2004, Chicago, USA: IEEE, DOI: 10.1109/ISIT.2004.1365067.
    [29]
    M. Xie, J. K. Hu, S. Guo, and A. Y. Zomaya, “Distributed segment-based anomaly detection with Kullback-Leibler divergence in wireless sensor networks,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 1, pp. 101–110, Jan. 2017. doi: 10.1109/TIFS.2016.2603961
    [30]
    Z. Momani, M. Shridah, O. Arqub, and M. Momani, “Modeling and analyzing neural networks using reproducing kernel Hilbert space algorithm,” Applied Mathematics and Information Sciences, vol. 12, no. 1, pp. 89–99, 2018.
    [31]
    H. L. Yan, Y. K. Ding, P. H. Li, Q. L. Wang, Y. Xu, and W. M. Zuo, “Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition. Honlulu HI, USA: IEEE, 2017. 945–954.
    [32]
    J. H. Fan, and R. Z. Liang, “Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison,” Neural Computing and Applications, vol. 29, no. 10, pp. 733–743, May 2018. doi: 10.1007/s00521-016-2603-2
    [33]
    S. Yang, “Calculates the pairwise distance between sets of vectors,” [Online]. Available: https://github.com/shicai/matlab/blob/master/sc_pdist2.m, Aug. 2017.
    [34]
    T. Liu, B. Wang, and C. L. Yang, “Online Markov chain-based energy management for a hybrid tracked vehicle with speedy Q-learning,” Energy, vol. 160, pp. 544–555, 2018. doi: 10.1016/j.energy.2018.07.022
    [35]
    Y. Zou, T. Liu, F. C. Sun, and H. Peng, “Comparative study of dynamic programming and Pontryagin’s minimum principle on energy management for a parallel hybrid electric vehicle,” Energies, vol. 6, no. 4, pp. 2305–2318, 2013. doi: 10.3390/en6042305
    [36]
    X. L. Tang, D. J. Zhang, T. Liu, A. Khajepour, H. S. Yu, and H. Wang, “Research on the energy control of a dual-motor hybrid vehicle during engine start-stop process,” Energy, vol. 166, no. 1, pp. 1181–1193, 2019. doi: 10.1016/j.energy.2018.10.130

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    Highlights

    • Parallel driving 3.0 system as potential autonomous driving system is essentially discussed.
    • Parallel distance framework is presented to measure real and artificial world.
    • Techniques related to multiple distance calculation are quantified and compared.
    • Practical applications of parallel distance framework is introduced and outlined.

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