Digital Twins (DTs) are virtual representations of physical objects or
processes that can collect information from the real environment to represent,
validate, and replicate the physical twin's present and future behavior. The
DTs are becoming increasingly prevalent in a variety of fields, including
manufacturing, automobiles, medicine, smart cities, and other related areas. In
this paper, we presented a systematic reviews on DTs in the autonomous
vehicular industry. We addressed DTs and their essential characteristics,
emphasized on accurate data collection, real-time analytics, and efficient
simulation capabilities, while highlighting their role in enhancing performance
and reliability. Next, we explored the technical challenges and central
technologies of DTs. We illustrated the comparison analysis of different
methodologies that have been used for autonomous vehicles in smart cities.
Finally, we addressed the application challenges and limitations of DTs in the
autonomous vehicular industry.

本篇论文提供了数字孪生在自主车辆工业中的系统评述，重点强调了准确的数据收集、实时分析和高效的模拟能力，以此来提高性能和可靠性，同时探讨了数字孪生的技术挑战和中心技术，并对在智慧城市中自主车辆所使用的不同方法进行了对比分析，最后讨论了数字孪生在自主车辆行业中的应用挑战和限制。