An Intelligent Driving Monitoring System Utilizing Pedal Motion Sensor Integrated with Triboelectric‐Electromagnetic Hybrid Generator and Machine Learning

Abstract Driver's driving behavior and driving style have a crucial impact on traffic safety, capacity, and efficiency, so it is of great significance to monitor the driver's driving behavior and recognize their driving style. In this work, an intelligent driving monitoring system based on a triboelectric nanogenerator and electromagnetic generator is designed. The system consists of a self‐powered pedal motion sensor (SPMS) and an intelligent data processing unit (IDPU), which can monitor driving behavior and recognize driving style. SPMS is used for driving behavior monitoring, which mainly consists of a six‐phase triboelectric nanogenerator (S‐TENG) and a free‐rotating disk electromagnetic generator (FD‐EMG). S‐TENG can recognize information such as pedal movement direction, movement amplitude, and movement speed, and FD‐EMG can realize the function of a self‐powered driver's driving behavior warning. The IDPU includes a numerical calculation system for driving style characteristic variables and a driving style classifier. It can recognize the driving style based on the driving data collected by SPMS. The driving style classifier design is based on a combination of simulated driving experiments and machine learning techniques, and its accuracy is verified through experiments. This work has important potential applications in the field of traffic safety and intelligent driving.