SafeDrive: Detecting Distracted Driving Behaviors Using Wrist-Worn Devices

被引：21

作者：

Jiang, Landu ^{[1
]}

Lin, Xinye ^{[1
]}

Liu, Xue ^{[1
]}

Bi, Chongguang ^{[2
]}

Xing, Guoliang ^{[2
]}

机构：

[1] McGill University, 845 Rue Sherbrooke O, Montreal,QC,H3A 0G4, Canada

[2] Michigan State University, Department of Computer Science and Engineering, East Lansing,MI,48824, United States

来源：

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | 2017年 / 1卷 / 04期

基金：

加拿大创新基金会; 美国国家科学基金会;

关键词：

Automobile drivers - Behavioral research - Vehicles;

D O I：

10.1145/3161179

中图分类号：

学科分类号：

摘要：

Distracted driving causes a large number of fatalities every year and is now becoming an important issue in the traffic safety study. In this paper, we present SafeDrive, a driving safety system that leverages wearable wrist sensing techniques to detect and analyze driver distracted behaviors. Existing wrist-worn sensing approaches, however, do not address challenges under real driving environments, such as less distinguishable gesture patterns due to in-vehicle physical constraints, various gesture hallmarks produced by different drivers and significant noise introduced by various driving conditions. In response, SafeDrive adopts a semi-supervised machine learning model for in-vehicle distracting activity detection. To improve the detection accuracy, we provide online updated classifiers by collecting real-time gesture data, while at the same time utilize smartphone sensing to generate soft hints filtering out anomalies and non-distracted hand movements. In the evaluation, we conduct extensive real-road experiments involving 20 participants (10 males and 10 females) and 5 vehicles (a sedan, a minivan and three SUVs). Our approach can achieve an average classification accuracy of over 90% with a error rate of a few percent, which demonstrate that SafeDrive is robust to real driving environments, and has great potential to help drivers shape safe driving habits. © 2018 ACM.

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