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Vehicle Driver Monitoring through the Statistical Process Control.
Assuncao, Arthur N; Aquino, Andre L L; Câmara de M Santos, Ricardo C; Guimaraes, Rodolfo L M; Oliveira, Ricardo A R.
Afiliação
  • Assuncao AN; Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais, Santos Dumont, MG 36240-000, Brazil.
  • Aquino ALL; Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.
  • Câmara de M Santos RC; Instituto de Computação, Universidade Federal de Alagoas, Maceió, AL 57072-970, Brazil. alla@laccan.ufal.br.
  • Guimaraes RLM; Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.
  • Oliveira RAR; Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil.
Sensors (Basel) ; 19(14)2019 Jul 11.
Article em En | MEDLINE | ID: mdl-31336711
ABSTRACT
This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver's behavior. Based on the SPC, we propose a method for the lane departure detection; a method for detecting sudden driver movements; and a method combined with computer vision to detect driver fatigue. All methods consider information from sensors scattered by the vehicle. The results showed the efficiency of the methods in the identification and detection of unwanted driver actions, such as sudden movements, lane departure, and driver fatigue. Lane departure detection obtained results of up to 76.92% (without constant speed) and 84.16% (speed maintained at ≈60). Furthermore, sudden movements detection obtained results of up to 91.66% (steering wheel) and 94.44% (brake). The driver fatigue has been detected in up to 94.46% situations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article