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Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience).
2017
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F. Tao et al., “Digital twin in industry: State-of-the-art,” IEEE Transactions on Industrial Informatics , vol. 15, no. 4, pp. 2405–2415, 2018
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2020
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2020
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A. Fuller et al., “Digital twin: Enabling technologies, challenges and open research,” IEEE access , vol. 8, pp. 108 952–108 971, 2020
2020
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L. U. Khan et al., “Dispersed federated learning: Vision, taxonomy, and future directions,” IEEE Wireless Communications , vol. 28, no. 5, pp. 192–198, 2021
2021
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D. Chen et al, “Digital twin for federated analytics using a bayesian approach,” IEEE Internet of Things Journal , vol. 8, no. 22, pp. 16 301–16 312, 2021
2021
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Y. Qi et al., “Privacy-preserving blockchain-based federated learning for traffic flow prediction,” Future Generation Computer Systems , vol. 117, pp. 328–337, 2021
2021
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M. A. Javed et al., “Ai-empowered content caching in vehicular edge computing: Opportunities and challenges,” IEEE Network , vol. 35, no. 3, pp. 109–115, 2021
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.
L. U. Khan et al., “Digital-twin-enabled 6g: Vision, architectural trends, and future directions,” IEEE Communications Magazine , vol. 60, no. 1, pp. 74–80, 2022
2022
Closest in time.
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2021
Cited alongside, same era.