Artificial intelligence enabled Digital Twins for training autonomous cars
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 autono...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-01-01
|
| Series: | Internet of Things and Cyber-Physical Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345222000116 |
| _version_ | 1797742182496468992 |
|---|---|
| author | Dongliang Chen Zhihan Lv |
| author_facet | Dongliang Chen Zhihan Lv |
| author_sort | Dongliang Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T14:37:19Z |
| format | Article |
| id | doaj.art-9327b9fb02294cad85a7ed5ca21bb3fe |
| institution | Directory Open Access Journal |
| issn | 2667-3452 |
| language | English |
| last_indexed | 2024-03-12T14:37:19Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj.art-9327b9fb02294cad85a7ed5ca21bb3fe2023-08-17T04:27:59ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522022-01-0123141Artificial intelligence enabled Digital Twins for training autonomous carsDongliang Chen0Zhihan Lv1College of Computer Science and Technology, Qingdao University, Qingdao, ChinaDepartment of Game Design, Faculty of Arts, Uppsala University, Sweden; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2667345222000116Deep learningAutonomous carsDigital twinsInformation securityArtificial intelligence |
| spellingShingle | Dongliang Chen Zhihan Lv Artificial intelligence enabled Digital Twins for training autonomous cars Internet of Things and Cyber-Physical Systems Deep learning Autonomous cars Digital twins Information security Artificial intelligence |
| title | Artificial intelligence enabled Digital Twins for training autonomous cars |
| title_full | Artificial intelligence enabled Digital Twins for training autonomous cars |
| title_fullStr | Artificial intelligence enabled Digital Twins for training autonomous cars |
| title_full_unstemmed | Artificial intelligence enabled Digital Twins for training autonomous cars |
| title_short | Artificial intelligence enabled Digital Twins for training autonomous cars |
| title_sort | artificial intelligence enabled digital twins for training autonomous cars |
| topic | Deep learning Autonomous cars Digital twins Information security Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2667345222000116 |
| work_keys_str_mv | AT dongliangchen artificialintelligenceenableddigitaltwinsfortrainingautonomouscars AT zhihanlv artificialintelligenceenableddigitaltwinsfortrainingautonomouscars |