FDAN: Fuzzy deep attention networks for driver behavior recognition

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Xiao, Weichu;Xie, Guoqi;Liu, Hongli;Chen, Weihong;Li, Renfa

通讯作者：

Xie, GQ

作者机构：

[Xiao, Weichu; Liu, Hongli] Hunan Univ, Coll Elect & Informat Engn, Changsha, Hunan, Peoples R China.

[Xie, Guoqi; Xie, GQ; Li, Renfa] Hunan Univ, Coll Informat Sci & Engn, Changsha, Hunan, Peoples R China.

[Xiao, Weichu] Hunan City Univ, Coll Informat & Elect Engn, Yiyang, Hunan, Peoples R China.

[Chen, Weihong] Hunan Univ Finance & Econ, Coll Informat Technol & Management, Changsha, Hunan, Peoples R China.

通讯机构：

[Xie, GQ ] H

Hunan Univ, Coll Informat Sci & Engn, Changsha, Hunan, Peoples R China.

语种：

英文

关键词：

Attention;Deep learning;Driver behavior recognition;Fuzzy logic

期刊：

Journal of Systems Architecture

ISSN：

1383-7621

年：

2024

卷：

147

页码：

103063

DOI：

10.1016/j.sysarc.2023.103063

基金类别：

National Natural Science Foundation of China [61971182, 62173133]

机构署名：

本校为其他机构

院系归属：

信息与电子工程学院

摘要：

Driver behavior is an essential factor affecting traffic safety, and driver behavior monitoring systems (DMSs) are widely exploited in intelligent transportation systems to reduce the risk of traffic accidents. However, understanding driver behavior is challenging because of the uncertainty of real driving scenarios. Most of the existing methods use deterministic models, which suffer from data uncertainty, for recognizing driver behaviors. In this paper, the fuzzy deep attention network (FDAN) method is proposed to improve driver behavior recognition. FDAN integrates fuzzy logic and an attenti...

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FDAN: Fuzzy deep attention networks for driver behavior recognition

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