Driving risk status prediction using Bayesian networks and logistic regression-Reference-Cited by-同舟云学术

Driving risk status prediction using Bayesian networks and logistic regression

Published:2017-09 Issue:7 Volume:11 Page:431-439
ISSN:1751-9578
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Transport Systems

Author:

Yan Lixin¹,Huang Zhen²,Zhang Yishi³,Zhang Liyan⁴,Zhu Dunyao²,Ran Bin⁵

Affiliation:

1. College of Transportation and LogisticsEast China Jiaotong UniversityChangbei Open and Developing DistrictNanchang330013JiangxiPeople's Republic of China

2. Intelligent Transport Systems Research CenterWuhan University of TechnologyHeping Avenue #1040Wuhan430063People's Republic of China

3. Management SchoolJinan UniversityHuangpu W. Road #601, Huiquan buildingGuangzhou510632People's Republic of China

4. School of Civil EngineeringSuzhou University of Science and Technology1701 Binhe Road, New DistrictSuzhouJiangsuPeople's Republic of China

5. TOPS LaboratoryDepartment of Civil and Environmental EngineeringUniversity of Wisconsin‐Madison1415 Engineering Drive, 2205 Engineering HallMadisonWIUSA

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2016.0207

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4. Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios

5. A Multilevel Collision Mitigation Approach—Its Situation Assessment, Decision Making, and Performance Tradeoffs

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