Next Article in Journal
Scattering of Microwaves by a Passive Array Antenna Based on Amorphous Ferromagnetic Microwires for Wireless Sensors with Biomedical Applications
Next Article in Special Issue
Sensor Fault Detection and Signal Restoration in Intelligent Vehicles
Previous Article in Journal
Optical Detection of Vapor Mixtures Using Structurally Colored Butterfly and Moth Wings
Previous Article in Special Issue
A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations
Open AccessArticle

Vehicle Driver Monitoring through the Statistical Process Control

Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais, Santos Dumont, MG 36240-000, Brazil
Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, MG 35400-000, Brazil
Instituto de Computação, Universidade Federal de Alagoas, Maceió, AL 57072-970, Brazil
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3059;
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

Back to TopTop