Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification
المؤلفون المشاركون
Thajchayapong, Suttipong
Tanprasert, Thitaree
Saiprasert, Chalermpol
المصدر
Journal of Advanced Transportation
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-10-16
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks.
The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors.
First anomaly detection is performed to assess if the current driver is whom he/she claims to be.
If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers.
To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions.
Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner.
It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds.
The proposed algorithm also outperforms the previously proposed driver identification algorithms.
Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Tanprasert, Thitaree& Saiprasert, Chalermpol& Thajchayapong, Suttipong. 2017. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1170828
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Tanprasert, Thitaree…[et al.]. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification. Journal of Advanced Transportation No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1170828
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Tanprasert, Thitaree& Saiprasert, Chalermpol& Thajchayapong, Suttipong. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1170828
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1170828
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر