Hierarchical Automated Machine Learning Approach for Self-Optimizable Driving Distraction Recognition Based on Driver’s Lane-Keeping Performance

Abstract

With the enrichment of smartphone uses, phone-related driving distractions have become a threat to driving safety. One way to mitigate driving distractions is to detect them and provide real-time warnings. However, most existing driving distraction recognition algorithms are pretrained models composed of structures, hyperparameters, and parameters that may not be able to account for drivers’ individual differences and, thus, might result in low model accuracy. This study proposes a domain-specific hierarchical automated machine learning (HAT-ML) model that self-learns personalized optimal models to detect driving distractions from vehicle movement data. The HAT-ML model integrates key modeling steps into auto-optimizable layers, including knowledge-based feature extraction, feature selection by recursive feature elimination, automated algorithm selection, and hyperparameter autotuning by Bayesian optimization. In our eight-degrees-of-freedom driving simulator experiment, we demonstrated the effectiveness of the proposed model using three driving distraction tasks: browsing a short message, browsing a long message, and answering a phone call. The HAT-ML model was found to be reliable and robust for predicting phone-related driving distraction, achieving satisfactory results with a predictive accuracy of 80% at the group level and 90% at the individual level. Moreover, the results revealed that each distraction and driver type required different optimized hyperparameter values, which demonstrated the value of utilizing HAT-ML to detect driving distractions. The key elements that dominated the performance of the model have several theoretical and practical implications. The proposed method not only enhanced performance, but also provided data-driven insights about model development.