A wearable driver gesture recognition system enabled AI application in virtual reality interaction for smart traffic

Kaixiao Xiong, Chengliang Fan, Deqiang He, Hongyu Chen, Xinyi Zhao, Xihui Feng, Zutao Zhang and Xiaoping Wu

Energy, 2025, vol. 327, issue C

Abstract: In the rapid development of virtual reality technology in the future, the driver's gesture will become the key information in the driving process. Translating these gestures into digital signals can provide an innovative technical solution for intelligent driving systems in virtual reality environments. More recently, there is a complete set of specifications for the gesture instructions of railway train drivers as an important part of ensuring the safety of train drivers in the driving process. This paper puts forward a Driver Command Smart System (DC-SS) for Smart Transportation based on the current railway driver's gesture command. A portable triboelectric sensor ring (TENG) is employed to capture the driver's gesture signals. By comparing the use of recurrent neural networks (RNN) and convolutional neural networks (CNN) in deep learning technology, we achieved real-time accurate recognition of driver gestures (98.9 %) and the generation of virtual actions. Finally, Unity3D is used for real-time generation of the cab scene, facilitating real-time virtual interaction between train drivers and various transportation units. The application of these technologies not only improves the accuracy of gesture recognition but also provides an effective technical approach for human-computer interaction and human-human interaction in driver-based intelligent transportation system. It lays a foundation for the application of intelligent traffic digital twinning technology in the field of meta-universe. Furthermore, its technical principles and methods also apply to the future of road, sea and air traffic and other key areas where human gesture recognition is crucial to safety and efficiency.

Keywords: Smart transportation; Wearable devices; Deep learning; TENG; Digital twin (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:327:y:2025:i:c:s0360544225020766

DOI: 10.1016/j.energy.2025.136434

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