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Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data
المؤلفون المشاركون
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-05-15
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety.
A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors.
Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification.
A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model.
The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter.
This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Peng, Jinshuan& Shao, YiMing. 2018. Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data. Complexity،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1133002
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Peng, Jinshuan& Shao, YiMing. Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data. Complexity No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1133002
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Peng, Jinshuan& Shao, YiMing. Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data. Complexity. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1133002
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1133002
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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