Real-time detection of drivers' texting and eating behavior based on vehicle dynamics. (October 2018) - Atiquzzaman, Md; Qi, Yan; Fries, Ryan
Highlights: Detection algorithms have been developed based on only vehicle dynamics data. Vehicles dynamics data was collected using a medium fidelity driving simulator. Two linear and two non-linear data mining techniques were explored. Algorithms were developed using inter-subject approach. Random forests algorithms were found to have best performance. Abstract: With rapid advancement in cellphones and intelligent in-vehicle technologies along with driver's inclination to multitasking, crashes due to distracted driving had become a growing safety concern in our road network. Some previous studies attempted to detect distracted driving behaviors in real-time to mitigate their adverse consequences. However, these studies mainly focused on detecting either visual or cognitive distractions only, while most of the real-life distracting tasks involve driver's visual, cognitive, and physical workload, simultaneously. Additionally, previous studies frequently used eye, head, or face tracking data, although current vehicles are not commonly equipped with technologies to acquire such data. Also those data are comparatively difficult to acquire in real-time during traffic monitoring operations. To address the above issues, this study focused on developing algorithms for detecting distraction tasks that involve simultaneous visual, cognitive, and physical workload using only vehicle dynamics data. Specifically, algorithms were developed to detect driving behaviors under two distraction tasks – texting and eating. Experiment was designed to include the two distracted driving scenarios and a control with multiple runs for each. A medium fidelity driving simulator was used for acquiring vehicle dynamics data for each scenario and each run. Several data mining techniques were explored in this study to investigate their performance in detecting distraction. Among them, the performance of two linear (linear discriminant analysis and logistic regression) and two nonlinear models (support vector machines and random forests) is reported in this article. Random forests algorithms had the best performance, which detected texting and eating distraction with an accuracy of 85.38% and 81.26%, respectively. This study may provide useful guidance to successful development and implementation of distracted driver detection algorithms in connected vehicle environment, as well as to auto manufacturers interested in integrating distraction detection systems in their vehicles.