Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-15
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133002