A hybrid deep learning framework for conflict prediction of diverse merge scenarios at roundabouts

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成果类型：

期刊论文

作者：

Li, Ye;Ge, Chang;Xing, Lu;Yuan, Chen;Liu, Fei;...

通讯作者：

Xing, L

作者机构：

[Li, Ye; Jin, Jieling; Liu, Fei; Ge, Chang; Yuan, Chen] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Hunan, Peoples R China.

[Li, Ye] Changsha Univ Sci & Technol, Hunan Key Lab Smart Roadway & Cooperat Vehicle Inf, Changsha 410114, Hunan, Peoples R China.

[Xing, Lu] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410004, Hunan, Peoples R China.

[Yuan, Chen] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China.

通讯机构：

[Xing, L ] C

Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410004, Hunan, Peoples R China.

语种：

英文

关键词：

Roundabout;Conflict prediction;Deep learning model;Attention mechanism

期刊：

Engineering Applications of Artificial Intelligence

ISSN：

0952-1976

年：

2024

卷：

130

页码：

107705

DOI：

10.1016/j.engappai.2023.107705

机构署名：

本校为通讯机构

院系归属：

交通运输工程学院

摘要：

The unique traffic situation at roundabouts causes complex interactions between merging vehicles, thereby increasing the likelihood of conflicts. Reliable prediction of conflict risk contributes to active safety improvement, but few studies have investigated the merge risk of roundabouts at a microscopic level. In light of this, this study develops a hybrid deep learning framework for predicting potential conflict risks in complex merging scenarios at roundabouts. Specifically, a roundabout coordinate system is devised to define vehicle characteristics based on trajectory data. Then, an improv...

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A hybrid deep learning framework for conflict prediction of diverse merge scenarios at roundabouts

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