An XGBoost approach to detect driver visual distraction based on vehicle dynamics

Abstract

Objectives

Distracted driving such as reading phone messages during driving is risky, as it increases the probability of severe crashes. This study proposes an XGBoost model for visual distraction detection based on vehicle dynamics data from a driving simulation study.

Methods

A simulated driving experiment involving thirty-six drivers was launched. We obtained the vehicle dynamics parameters required for the model using the time window and fast Fourier transform methods, totaling 26 items. Meanwhile, the effects of varied time window sizes (1–7 s) and amount of input indications on model performance were studied.

Results

By conducting a comparative analysis, it has been determined that the ideal time window size is 5 s. Additionally, the optimal number of input indicators was found to be 23. The XGBoost model for distinguishing distractions achieved an accuracy rate of 85.68%, a precision rate of 85.83%, a recall rate of 83.85%, an F1 score of 84.82%, and an AUC value of 0.9319, which were higher than SVM and RF. The gain-based feature rank demonstrated that the standard deviation of vehicle sideslip rate and the mean amplitude of the 0–1 Hz spectrum component of the steering wheel angle were more crucial than other features.

Conclusions

The research results indicate that the steering wheel angle and vehicle sideslip angle may be more conducive to identifying distractions. This XGBoost model could potentially be applied in advanced driving assistant systems (ADAS) to warn driver and reduce cellphone involved distracted driving.

Keywords:

• Distraction detection
• visual distraction
• vehicle dynamics
• XGBoost
• simulated driving

Acknowledgments

The authors would like to thank the volunteers who participated in this experiment.

Authors’ contributions

The authors confirm contribution to the paper as follows: study design, data preparation, analysis interpretation of results, and draft manuscript preparation: Yongqiang Guo; study conception and design: Hua Ding; analysis interpretation of results: Xingxing ShangGuan. All authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Funding

This research was supported by grants from the National Key Research and Development Program of China (2019YFB1600500).