交通领域诊断数字孪生的系统性映射研究
A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation
AI总结：
本研究通过系统性映射分析交通领域数字孪生诊断能力，发现现有研究多限于监控与故障检测，缺乏诊断推理，呼吁加强相关研究。
摘要：
近年来，数字孪生已在多个领域被提出并实施，其潜在应用范围从原型设计到维护不等。展望未来，数字孪生将赋能众多高效且可持续的技术，其中包括自动驾驶汽车。然而，尽管在许多领域已有大量研究，学术界尚未就数字孪生的确切定义达成共识——因此，对其能力与局限性也尚无定论。为加深理解，我们探索了数字孪生在交通领域诊断方面的能力。我们开展了一项系统性映射研究，涵盖车辆及其部件的数字孪生，以及交通基础设施。我们发现，很少有关于数字孪生的论文描述了任何诊断过程。此外，大多数现有方法似乎局限于系统监控或故障检测。这些发现表明，我们需要更多利用数字孪生进行诊断推理的研究。
英文摘要：
In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -- and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
