Driver activity recognition using deep learning based on multi-step batch size up - Utomo, Darmawan; Indria Prambodo, Natanael; Murtianta, Budihardja (Institute of Advanced Engineering and Science, 2025)
The increasing popularity of electric motorbikes in Indonesia, while promoting sustainable mobility, also raises concerns regarding traffic safety. Given the high incidence of motorcycle-related accidents, there is a critical need for systems capable of monitoring and recognizing driver behavior. This study proposes a driver activity recognition system for electric motorbikes, utilizing an event data recorder (EDR) to capture seven key sensor signals: three-axis acceleration, voltage, current, power, and speed. A custom dataset was constructed using data collected from 10 subjects, each performing five driving activities including forward drive, brake, stop, left turn, and right turn for over three-minute intervals per activity. The classification model is based on a long short-term memory (LSTM) neural network. To optimize training efficiency, a multi-step batch size up (MSBU) strategy was introduced, which accelerates training time by 1.84× compared to a fixed batch size of 32. The best performance was achieved using a segment length of 75 time-steps, yielding an accuracy and macro F1-score of 0.9873. These results demonstrate the effectiveness of the proposed system for real-time driver behavior monitoring and activity recognition in electric motorbike applications.