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

Digital twins (DTs) are digital copies of real-world items or processes that can learn from their surroundings to more accurately reflect, validate, and mimic the physical twin’s behavior in the here and now and into the future. 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 review of DTs in the autonomous vehicular industry. We addressed DTs and their essential characteristics, emphasizing 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.