Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles

• Published in: Nature communications Vol. 16; no. 1; pp. 1080 - 13
• Main Authors: Lu, Xiao, Tan, Haiqiu, Zhang, Haodong, Wang, Wuhong, Xie, Shaorong, Yue, Tao, Chen, Facheng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 147/28; 639/166/987; 639/166/988; 639/301/1005/1009; 639/925/927/511; 706/703/559; Automatic vehicle identification systems; Automation; Behavior; Budgets; Gloves; Humanities and Social Sciences; Modules; multidisciplinary; Real time; Science; Science (multidisciplinary); Stability; Traffic safety
• DOI: 10.1038/s41467-025-56169-2
• ISSN: 2041-1723

The takeover issue, especially the setting of the takeover time budget, is a critical factor restricting the implementation and development of conditionally automated vehicles. The general fixed takeover time budget has certain limitations, as it does not take into account the driver’s non-driving behaviors. Here, we propose an intelligent takeover assistance system consisting of all-round sensing gloves, a non-driving behavior identification module, and a takeover time budget determination module. All-round sensing gloves based on triboelectric sensors seamlessly detect delicate motions of hands and interactions between hands and other objects, and then transfer the electrical signals to the non-driving behavior identification module, which achieves an accuracy of 94.72% for six non-driving behaviors. Finally, combining the identification result and its corresponding minimum takeover time budget obtained through the takeover time budget determination module, our system dynamically adjusts the takeover time budget based on the driver’s current non-driving behavior, significantly improving takeover performance in terms of safety and stability. Our work presents a potential value in the application and implementation of conditionally automated vehicles. In this work, authors develop intelligent electronic gloves for Conditionally Automated Vehicles that dynamically adjust the time drivers have to take control based on real-time identification of non-driving behaviours improving takeover performance safety and stability.