A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems
It provides a comprehensive overview for researchers and practitioners in the autonomous vehicle industry, but is incremental as it synthesizes existing knowledge rather than introducing new methods.
This paper conducted a systematic review of Digital Twin applications in autonomous vehicular systems, addressing their characteristics, technical challenges, and methodologies to enhance performance and reliability.
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.