Autonomous driving will be widely deployed in the near future which can efficiently reduce the workload on human drivers. Unlike complicated urban driving conditions, fully autonomous driving is expected to be implemented in closed and relatively simple environments, e.g., mines. Autonomous driving in mines holds substantial practical value that can significantly reduce mining costs and enhance operational efficiency. To ensure vehicle safety, unmanned driving systems should be rigorously tested before deployment. With the rapid development of digital twin technology, offline testing in cyberspace has gained increasing significance. While numerous driving simulators have been recently released, none of them can support simulation tests in mines. In addition, due to the ignorance of human factors in the system, the digital twin in CPS (Cyber-Physical Systems) is inadequate for developing next-generation intelligent testing systems. To address these issues, we propose PMWorld (Parallel Mines World) based on parallel intelligence in this paper, which is a novel parallel testing platform for autonomous mining vehicles. It not only considers human behaviors during testing but also constitutes a closed loop between physical and virtual systems, allowing for more efficient testing methods. We also introduce the key technical infrastructures for constructing the PMWorld platform and present various testing functionalities supported by PMWorld, including HIL (Hardware-In-the-Loop) test, RIL (Road-In-the-Loop) test, virtual-real interactive test, and integrated test with the scheduling system.


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    Titel :

    PMWorld: A Parallel Testing Platform for Autonomous Driving in Mines


    Beteiligte:
    Ai, Yunfeng (Autor:in) / Liu, Yuhang (Autor:in) / Gao, Yu (Autor:in) / Zhao, Chen (Autor:in) / Cheng, Xiang (Autor:in) / Han, Jinpeng (Autor:in) / Tian, Bin (Autor:in) / Chen, Long (Autor:in) / Wang, Fei-Yue (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2024-01-01


    Format / Umfang :

    5690903 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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