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

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.