Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones
Open Access
- 19 October 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Mobile Computing
- Vol. 16 (8), 2198-2212
- https://doi.org/10.1109/tmc.2016.2618873
Abstract
Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e., Weaving , Swerving , Sideslipping , Fast U-turn , Turning with a wide radius , and Sudden braking . Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormal D riving behavior D etection and i D entification system, $D^{3}$ , to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods, Support Vector Machine (SVM) and Neuron Networks (NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that $D^{3}$ achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.
Keywords
Funding Information
- NSFC (61170238, 61420106010, 61472254, 61170238, 61672349, 61303202)
- NSF (CNS1409767, CNS1514436)
- Program for New Century Excellent Talents in University of China
This publication has 17 references indexed in Scilit:
- Tracking human queues using single-point signal monitoringPublished by Association for Computing Machinery (ACM) ,2014
- Sensing vehicle dynamics for determining driver phone usePublished by Association for Computing Machinery (ACM) ,2013
- Context-Aware Driver Behavior Detection System in Intelligent Transportation SystemsIEEE Transactions on Vehicular Technology, 2013
- OpenSesame: Unlocking smart phone through handshaking biometricsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Driving behavior analysis with smartphonesPublished by Association for Computing Machinery (ACM) ,2012
- Estimating driving behavior by a smartphonePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Safe Driving Using Mobile PhonesIEEE Transactions on Intelligent Transportation Systems, 2012
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Can SVM be used for automatic EEG detection of drowsiness during car driving?Safety Science, 2009
- Adaptability to ambient light changes for drowsy driving detection using image processingJSAE Review, 1999