Reverse deduction of vehicle group situation based on dynamic Bayesian network
Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow theory and the intelligent vehicle driving syste...
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2018-03-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814017747708 |
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doaj-e118da798799497a800fd1e58dc390912020-11-25T03:40:41ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-03-011010.1177/1687814017747708Reverse deduction of vehicle group situation based on dynamic Bayesian networkXiaoyuan Wang0Jianqiang Wang1Yaqi Liu2Zhenxue Liu3Jingheng Wang4State Key laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, ChinaState Key laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, ChinaShandong Zibo Experimental High School, Zibo, ChinaVehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow theory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson’s distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible.https://doi.org/10.1177/1687814017747708 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoyuan Wang Jianqiang Wang Yaqi Liu Zhenxue Liu Jingheng Wang |
spellingShingle |
Xiaoyuan Wang Jianqiang Wang Yaqi Liu Zhenxue Liu Jingheng Wang Reverse deduction of vehicle group situation based on dynamic Bayesian network Advances in Mechanical Engineering |
author_facet |
Xiaoyuan Wang Jianqiang Wang Yaqi Liu Zhenxue Liu Jingheng Wang |
author_sort |
Xiaoyuan Wang |
title |
Reverse deduction of vehicle group situation based on dynamic Bayesian network |
title_short |
Reverse deduction of vehicle group situation based on dynamic Bayesian network |
title_full |
Reverse deduction of vehicle group situation based on dynamic Bayesian network |
title_fullStr |
Reverse deduction of vehicle group situation based on dynamic Bayesian network |
title_full_unstemmed |
Reverse deduction of vehicle group situation based on dynamic Bayesian network |
title_sort |
reverse deduction of vehicle group situation based on dynamic bayesian network |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2018-03-01 |
description |
Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group situation accurately for the research of traffic flow theory and the intelligent vehicle driving system. Three-lane condition was taken as an example and the privacy protection of driver (only the data of travel time were used) was a premise in this article. Poisson’s distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area. And the dynamic Bayesian network was used to build the reverse deduction model of vehicle group situation. The model was verified through actual and virtual driving experiments. Verification results showed that the model established in this article was reasonable and feasible. |
url |
https://doi.org/10.1177/1687814017747708 |
work_keys_str_mv |
AT xiaoyuanwang reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork AT jianqiangwang reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork AT yaqiliu reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork AT zhenxueliu reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork AT jinghengwang reversedeductionofvehiclegroupsituationbasedondynamicbayesiannetwork |
_version_ |
1724533402284064768 |