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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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    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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