  With the enrichment of smartphones, driving distractions caused by phone
usages have become a threat to driving safety. A promising way to mitigate
driving distractions is to detect them and give real-time safety warnings.
However, existing detection algorithms face two major challenges, low user
acceptance caused by in-vehicle camera sensors, and uncertain accuracy of
pre-trained models due to drivers individual differences. Therefore, this study
proposes a domain-specific automated machine learning (AutoML) to self-learn
the optimal models to detect distraction based on lane-keeping performance
data. The AutoML integrates the key modeling steps into an auto-optimizable
pipeline, including knowledge-based feature extraction, feature selection by
recursive feature elimination (RFE), algorithm selection, and hyperparameter
auto-tuning by Bayesian optimization. An AutoML method based on XGBoost, termed
AutoGBM, is built as the classifier for prediction and feature ranking. The
model is tested based on driving simulator experiments of three driving
distractions caused by phone usage: browsing short messages, browsing long
messages, and answering a phone call. The proposed AutoGBM method is found to
be reliable and promising to predict phone-related driving distractions, which
achieves satisfactory results prediction, with a predictive power of 80\% on
group level and 90\% on individual level accuracy. Moreover, the results also
evoke the fact that each distraction types and drivers require different
optimized hyperparameters values, which reconfirm the necessity of utilizing
AutoML to detect driving distractions. The purposed AutoGBM not only produces
better performance with fewer features; but also provides data-driven insights
about system design.
