  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.
