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The internet-of-vehicle traffic condition system developed by artificial intelligence of things

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Abstract

An Internet-of-Vehicle (IoV) system primary transmits traffic information and various kinds of emergency notices through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I); however, the transmission of multimedia enables drivers to control route conditions better, such as road obstacles and the range of a construction site. Additionally, car accidents usually require relevant video records of the scene for investigation; surrounding cars could transfer the accident scene videos to help the police restore the detailed situation. Meanwhile, the multimedia messages of IoV need to go through security verification and privacy protection for the system to deliver push notifications and multimedia messages to social groups instantly. The study aims to construct an IoV traffic condition system developed by Artificial Intelligence of Things (AIoT); the data transmitting method of this research is via the 6th Generation Network (6G Network), which has advantages of high transmission speed and Quality of Service (QoS) guarantee. Furthermore, the suggested system employs federated learning to ensure message security and privacy. The features of the researched system are: 1. Use Faster Region-based Convolutional Neural Networks (R-CNN) to recognize the objects in cameras and judge if there are road obstacles and any constructions; 2. Capture car accident videos through federated learning, and send the encrypted evidence to relevant legal units; 3. Use push notifications to send multimedia messages to social groups instantly, marking the locations and the road conditions to help drivers control the conditions with the surroundings. This study expects to delivering videos and Global Positioning System (GPS) data for road condition recognition, improving driving safety. The features of the approach developed in this article are different from those IoV alarms presented in past research that requires drivers to enter messages for notifying nearby cars. Instead, this research utilizes Faster R-CNN to recognize road conditions and transmit information to base stations, and the base stations will pass the information to other vehicles. The federated learning technique in this article can enhance the Faster R-CNN model’s accuracy in each car.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 109-2622-E-197 -012.

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  1. Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan

    Hsin-Te Wu

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Correspondence to Hsin-Te Wu.

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Wu, HT. The internet-of-vehicle traffic condition system developed by artificial intelligence of things. J Supercomput 78, 2665–2680 (2022). https://doi.org/10.1007/s11227-021-03969-0

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