Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

2011 ◽  
Vol 50 (12) ◽  
pp. 127202 ◽  
Author(s):  
Jaeik Jo
Keyword(s):  
Monitoring System ◽  
Driver Drowsiness ◽  
Driver Monitoring

Driver inattention monitoring system based on multimodal fusion with visual cues to improve driving safety

2016 ◽  
Vol 40 (3) ◽  
pp. 885-895 ◽  
Author(s):  
Xuanpeng Li ◽  
Emmanuel Seignez
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Visual Cues ◽  
Real Life ◽  
Multimodal Fusion ◽  
Driving Safety ◽  
Driver Drowsiness ◽  
Driver Monitoring ◽  
Driver Inattention ◽  
Multi Level

Driver inattention, either driver drowsiness or distraction, is a major contributor to serious traffic crashes. In general, most research on this topic studies driver drowsiness and distraction separately, and is often conducted in a well-controlled, simulated environment. By considering the reliability and flexibility of real-time driver monitoring systems, it is possible to evaluate driver inattention by the fusion of multiple selected cues in real life scenarios. This paper presents a real-time, visual-cue-based driver monitoring system, which can track both multi-level driver drowsiness and distraction simultaneously. A set of visual cues are adopted via analysis of drivers’ physical behaviour and driving performance. Driver drowsiness is evaluated using a multi-level scale, by applying evidence theory. Additionally, a general framework of extensive hierarchical combinations is used to generate a probabilistic evaluation of driving risk in real time. This driver inattention monitoring system with multimodal fusion has been proven to improve the accuracy of risk evaluation and reduce the rate of false alarms, and acceptance of the system is recommended.

Smart Driver Monitoring System Using AI

2021 ◽  
pp. 225-250
Author(s):  
Shubhi Shaily ◽  
Srikaran Krishnan ◽  
Saisriram Natarajan ◽  
Sasikumar P.
Keyword(s):  
Artificial Intelligence ◽  
Monitoring System ◽  
Driver Drowsiness ◽  
Efficient Management ◽  
Driver Monitoring ◽  
Fatigue Detection

This chapter provides a contemporary solution to driver drowsiness and fatigue detection on-board whilst the driver is driving the car. The mechanism provided is both non-intrusive relatively and involves the use of artificial intelligence networks. This would aid in providing accurate and desired results thereby avoiding damage generally caused due to negligence and imposters in vulnerable industries that involve massive manpower and inventory. The system created will work based on vehicle details received from the OBD-II and the camera mounted on the dashboard to monitor the driver. As the driver enters the car, he initiates the authentication process when he turns the ignition knob or presses the start stop button in an attempt to start the car, and the on-board camera is turned on. Such efficient management could save the responsible company damages caused and the subsequent dent in their capital.

Integrated Rear-view Mirror for Driver Monitoring System

2006 ◽  
Author(s):  
Koji Okuda ◽  
Michimasa Itoh ◽  
Bunji Inagaki
Keyword(s):  
Monitoring System ◽  
Rear View Mirror ◽  
Driver Monitoring

scholarly journals On-Road Driver Monitoring System Based on a Solar-Powered In-Vehicle Embedded Platform

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yen-Lin Chen ◽  
Chao-Wei Yu ◽  
Zi-Jie Chien ◽  
Chin-Hsuan Liu ◽  
Hsin-Han Chiang
Keyword(s):  
Solar Cells ◽  
Embedded System ◽  
Monitoring System ◽  
Driving Safety ◽  
Detection Accuracy ◽  
Eye Detection ◽  
Electric Cars ◽  
Vehicle System ◽  
Driver Monitoring ◽  
Solar Powered

This study presents an on-road driver monitoring system, which is implemented on a stand-alone in-vehicle embedded system and driven by effective solar cells. The driver monitoring function is performed by an efficient eye detection technique. Through the driver’s eye movements captured from the camera, the attention states of the driver can be determined and any fatigue states can be avoided. This driver monitoring technique is implemented on a low-power embedded in-vehicle platform. Besides, this study also proposed monitoring machinery that can detect the brightness around the car to effectively determine whether this in-vehicle system is driven by the solar cells or by the vehicle battery. On sunny days, the in-vehicle system can be powered by solar cell in places without the vehicle battery. While in the evenings or on rainy days, the ambient solar brightness is insufficient, and the system is powered by the vehicle battery. The proposed system was tested under the conditions that the solar irradiance is 10 to 113 W/m2and solar energy and brightness at 10 to 170. From the testing results, when the outside solar radiation is high, the brightness of the inside of the car is increased, and the eye detection accuracy can also increase as well. Therefore, this solar powered driver monitoring system can be efficiently applied to electric cars to save energy consumption and promote the driving safety.

Sign in / Sign up

Export Citation Format

Share Document