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Digital Twins (DTs) are virtual representations of physical objects orprocesses that can collect information from the real environment to represent,validate, and replicate the physical twin's present and future behavior. TheDTs are becoming increasingly prevalent in a variety of fields, includingmanufacturing, automobiles, medicine, smart cities, and other related areas. Inthis paper, we presented a systematic reviews on DTs in the autonomousvehicular industry. We addressed DTs and their essential characteristics,emphasized on accurate data collection, real-time analytics, and efficientsimulation capabilities, while highlighting their role in enhancing performanceand reliability. Next, we explored the technical challenges and centraltechnologies of DTs. We illustrated the comparison analysis of differentmethodologies that have been used for autonomous vehicles in smart cities.Finally, we addressed the application challenges and limitations of DTs in theautonomous vehicular industry.

A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems