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Vehicle Driver Monitoring through the Statistical Process Control

1
Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais, Santos Dumont, MG 36240-000, Brazil
2
Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil
3
Instituto de Computação, Universidade Federal de Alagoas, Maceió, AL 57072-970, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3059; https://doi.org/10.3390/s19143059
Received: 23 April 2019 / Revised: 22 June 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Intelligent Vehicles)
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PDF [4088 KB, uploaded 12 July 2019]
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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. View Full-Text
Keywords: driver monitor; lane departure; statistical process control driver monitor; lane departure; statistical process control
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MDPI and ACS Style

Assuncao, A.N.; Aquino, A.L.L.; Câmara de M. Santos, R.C.; Guimaraes, R.L.M.; Oliveira, R.A.R. Vehicle Driver Monitoring through the Statistical Process Control. Sensors 2019, 19, 3059.

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