Conference Proceeding
Authors: Li Li (Edward P. Fitts Department of Industrial & Systems Engineering) , Ziyang Xie (Mechanical and Aerospace Engineering) , Xu Xu (Edward P. Fitts Department of Industrial & Systems Engineering) , Yulan Liang (Liberty Mutual Insurance, Boston, MA, USA) , William Horrey (AAA Foundation for Traffic Safety, Washington DC)
The goal of this study is to design a novel framework incorporating deep-learning techniques and wearable sensors to recognize manual distractions during driving. Manual distraction is defined as hands off the wheel for any reason (e.g. trying to get a cell phone). In this preliminary study, participants were tasked to drive in city street and highway scenarios in a driving simulator. Verbal instructions prompted participants to perform various manual distraction tasks. The motion of driver’s right wrist during driving was recorded by a wearable inertial measurement unit. A deep-learning technique called convolutional neural network (CNN) was then constructed and trained based on 72% of the experiment trials, and evaluated by the remaining 28% of trials. The results indicated that the convolutional neural network is able to recognize the type of manual distraction task based on the right wrist motion with 87.0% accuracy and F1-score of 0.87. The results indicated that there is a good potential to apply deep-learning techniques and wearable sensing to monitor driver’s inattention status.
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How to Cite: Li, L. , Xie, Z. , Xu, X. , Liang, Y. & Horrey, W. (2019) “Recognition of Manual Driving Distraction Through Deep-Learning and Wearable Sensing”, Driving Assessment Conference. 10(2019). doi: https://doi.org/10.17077/drivingassessment.1670