Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing.

Zhou, Rui; Huang, Helai; Lee, Jaeyoung; Huang, Xiangzhi; Chen, Jiguang; Zhou, Hanchu

Zhou, Rui; Huang, Helai; Lee, Jaeyoung; Huang, Xiangzhi; Chen, Jiguang; Zhou, Hanchu.

Afiliação

Zhou R; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
Huang H; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
Lee J; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Huang X; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China.
Chen J; Newhood Technologies Co., Ltd., Changsha 410075, China.
Zhou H; School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China; School of Data Science, City University of Hong Kong, Hong Kong 99907, China. Electronic address: hanchuzhou@csu.edu.cn.

Accid Anal Prev ; 191: 107218, 2023 Oct.

Article em En | MEDLINE | ID: mdl-37467602

Assuntos

Acidentes de Trânsito; Veículos Autônomos; Humanos; Acidentes de Trânsito/prevenção & controle; Algoritmos; Análise por Conglomerados; Bases de Dados Factuais

Palavras-chave

Autonomous vehicles; Clustering analysis; Stacked autoencoder; Testing scenarios

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Acidentes de Trânsito / Veículos Autônomos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Accid Anal Prev Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

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