Environmentally Robust Triboelectric Tire Monitoring System for Self‐Powered Driving Information Recognition via Hybrid Deep Learning in Time‐Frequency Representation

• Published in: Small (Weinheim an der Bergstrasse, Germany) Vol. 20; no. 34; pp. e2400484 - n/a
• Main Authors: Kim, BaekGyu, Song, Jin Yeong, Kim, Do Young, Cho, Min Woo, Park, Ji Gyo, Choi, Dongwhi, Lee, Chengkuo, Park, Sang Min
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.08.2024
• Subjects: Artificial intelligence; artificial intelligence of things; Artificial neural networks; Deep learning; Driving; Fourier transforms; Graphene; laser‐induced graphene; Machine learning; Moisture content; Monitoring; Monitoring systems; Neural networks; Recognition; Roads; Robustness; self‐powered sensors; Tires; Traffic information; triboelectric nanogenerators; triboelectric nanogenerators; robustness; artificial intelligence of things; laser‐induced graphene; self‐powered sensors
• ISSN: 1613-6810; 1613-6829; 1613-6829
• DOI: 10.1002/smll.202400484

Developing a robust artificial intelligence of things (AIoT) system with a self‐powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire‐road friction. The optimization of the process and structure of a laser‐induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200–2000 rpm and contact fractions of line. Employing a hybrid model combining short‐term Fourier transform with a convolution neural network‐long short‐term memory, the LIG‐based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%–90%) and temperatures (50–70 °C). The real‐time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self‐powered triboelectric sensor and hybrid deep learning for smart mobility. This study introduces environmentally robust triboelectric sensor‐assisted tire monitoring artificial intelligence of things system. Based on optimized laser‐induced graphene electrode, the tire reliably generates self‐powered informative triboelectric signal. Utilizing hybrid deep learning model, the system robustly recognizes traffic lines and road states in real time across intransigent environments including fog, dampness, intense sunlight, and heat shimmer.