Affiliation:
1. McGill University, Montreal, QC, Canada
2. Michigan State University, Department of Computer Science and Engineering, East Lansing, MI, USA
Abstract
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.
Funder
Mitacs
National Science Foundation
NSERC Collaborative Research and Development Grant
NSERC Discovery Grant
Canada Foundation for Innovation's John R. Evans Leaders Fund
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Cited by
36 articles.
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