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Title	Artificial Intelligence Enabled Digital Twins For Training Autonomous Cars
ID_Doc	10461
Authors	Chen D.; Lv Z.
Year	2022
Published	Internet of Things and Cyber-Physical Systems, 2
DOI	http://dx.doi.org/10.1016/j.iotcps.2022.05.001
Abstract	This exploration is aimed at the system prediction and safety performance of the Digital Twins (DTs) of autonomous cars based on artificial intelligence technology, and the intelligent development of transportation in the smart city. On the one hand, considering the problem of safe driving of autonomous cars in intelligent transportation systems, it is essential to ensure the transmission safety of vehicle data and realize the load balancing scheduling of data transmission resources. On the other hand, convolution neural network (CNN) of the deep learning algorithm is adopted and improved, and then, the DTs technology is introduced. Finally, an autonomous cars DTs prediction model based on network load balancing and spatial-temporal graph convolution network is constructed. Moreover, through simulation, the performance of this model is analyzed from perspectives of Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that in comparative analysis, the accuracy of road network prediction of the model reported here is 92.70%, which is at least 2.92% higher than that of the models proposed by other scholars. Through the analysis of the security performance of network data transmission, it is found that this model achieves a lower average delay time than other comparative models. Besides, the message delivery rate is basically stable at 80%, and the message leakage rate is basically stable at about 10%. Therefore, the prediction model for autonomous cars constructed here not only ensures low delay but also has excellent network security performance, so that information can interact more efficiently. The research outcome can provide an experimental basis for intelligent development and safety performance improvement in the transportation field of smart cities. © 2022 The Authors
Author Keywords	Artificial intelligence; Autonomous cars; Deep learning; Digital twins; Information security

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