In this paper, a new paradigm named parallel distance 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.

中文翻译：

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平行距离：平行驾驶的一种新的测量范式

在本文中，提出了一种新的并行距离范式来测量并行驱动系统中的数据信息。例如，在并行距离框架中测量和评估并行驱动系统中的核心变量。首先，介绍了包括控制管理平台，智能汽车平台和远程控制平台的并行驾驶3.0系统。然后，利用马尔可夫链（MC）对这些系统中控制命令的转移概率矩阵进行建模。此外，为了区分人工和物理驾驶条件下的控制变量，列举了不同的距离计算方法以指定虚拟信号和真实信号之间的差异。通过这样做，可以指导真实的系统，并且可以改善虚拟系统。最后，