Non‐instinct detection of cellphone usage from lane‐keeping performance based on eXtreme gradient boosting and optimal sliding windows

Abstract

Driving distraction caused by cellphone usage has become a common safety threat. As distraction detection methods based on driver's position or eye movement may raise privacy issues, a promising way is to analyze the vehicle's lane-keeping performance. This paper proposed a detection algorithm based on eXtreme gradient boosting (XGBoost), to develop a real-time driving distraction detection based on lane-keeping performance. The algorithm includes knowledge-based volatility feature extraction and feature selection by recursive feature elimination (RFE). To obtain dynamic patterns of lane-keeping performance affected by different types of cellphone usage, browsing a short message, browsing a long message, and answering a phone call, a driving simulator experiment was conducted on 28 drivers. Results showed that the proposed XGBoost-RFE method is reliable and promising to predict phone usage with 80% accuracy. The results also evoke the fact that sliding window size, which is about 80% of subtask duration, can be appropriate for real-time detection of multiple cellphone usages. For overlap percentages, 67% of sliding window size can balance the efficiency and continuity of data in adjacent sliding windows. The paper's potential application includes the design of a real-time driving distraction detection system.

A Method of Car Driver's Phone Call Recognition Based on Human Joint Points

“…Kubilay [14] detected mobile phone usage based on YOLOv5 network. Liu [15] proposed a detection algorithm based on extreme gradient boosting (XGBoost) to recognize the use of phone. Benjamin [16] proposes a method to extend such systems by driver posture classification to detect driver cell phone usage.…”