Parallel Distance: A New Paradigm of Measurement for Parallel Driving

作     者：Teng Liu Hong Wang Bin Tian Yunfeng Ai Long Chen Teng Liu;Hong Wang;Bin Tian;Yunfeng Ai;Long Chen

作者机构：the Vehicle Intelligence Pioneers Inc.Qingdao 266109 the Department of Automotive EngineeringChongqing UniversityChongqing 400044China the Mechanical and Mechatronics Engineering DepartmentWaterloo UniversityN2L 3G1Canada the State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049China the School of Data and Computer ScienceSun Yat-sen UniversityGuangzhou 510006China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2020年第7卷第4期

页      面：1169-1178页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：supported in part by the National Natural Science Foundation of China(61533019,91720000) Beijing Municipal Science and Technology Commission(Z181100008918007) the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(ICRI-IACV) 

主　　题：Artificial and physical system parallel distance parallel driving 3.0 parallel system rotational and accelerator signal 

摘      要：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.