@inproceedings{3a222e28238048df8f5fc5121e7af14a,
title = "WhozDriving: Abnormal driving trajectory detection by studying multi-faceted driving behavior features",
abstract = "Vehicles have become essential tools of transport, offering a great opportunity to exploit the relationship between people and the car. This paper aims to solve an interesting problem, recognizing who the person is through their driving behaviors. Driver identification is useful for quite a few situations, such as car usage authentication, context-based recommendation, and determination of auto-insurance compensation. In this work, we propose WhozDriving, an approach that analyzes drivers{\textquoteright} driving behavior data and extract some sudden changes of driver behaviors as features which can be applied to distinguish different drivers. We propose a supervised learning method to detect anomaly driving trajectory from driving data. Experimental results on driving datasets show that our proposed approach is effective in terms of anomaly detection rate and misclassification anomaly rate.",
keywords = "Anomaly detection, Driver behavior patterns, Driver identification, GPS trajectory, KNN",
author = "Meng He and Bin Guo and Huihui Chen and Alvin Chin and Jilei Tian and Zhiwen Yu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 2nd International Conference on Big Data Computing and Communications, BigCom 2016 ; Conference date: 29-07-2016 Through 31-07-2016",
year = "2016",
doi = "10.1007/978-3-319-42553-5_12",
language = "英语",
isbn = "9783319425528",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "135--144",
editor = "Yu Wang and Ge Yu and Guoren Wang and Yanyong Zhang and Zhu Han",
booktitle = "Big Data Computing and Communications - 2nd International Conference, BigCom 2016, Proceedings",
}