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DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration
المؤلفون المشاركون
Dai, Xili
Zhu, Jinqi
Sun, Huazhi
Liu, Nianbo
Ma, Chunmei
Liu, Ming
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-03-22
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving.
However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations.
Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered.
The noises caused by hardware of the smartphone cannot be removed from the sensor reading.
In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration.
We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation.
Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven.
DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control.
We evaluate the effectiveness of our scheme under realistic environments.
The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively.
In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ma, Chunmei& Dai, Xili& Zhu, Jinqi& Liu, Nianbo& Sun, Huazhi& Liu, Ming. 2017. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1189249
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ma, Chunmei…[et al.]. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1189249
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ma, Chunmei& Dai, Xili& Zhu, Jinqi& Liu, Nianbo& Sun, Huazhi& Liu, Ming. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1189249
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1189249
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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