Driver Fatigue Monitoring System Using Video Images and Steering Grip Force

Abstract

Previous research uses eye blink sensor technology to detect driver's fatigue, which has some limitations in the condition of wearing glasses and lighting changes. To overcome those problems, this paper proposes a way to detect fatigue for drivers through video images and grip forces on steering wheel. Simulated driving experiments are conducted on a platform developed by simulated driving software, during which grip forces of both hands and video images are collected. Analyzing the images from the video, we applied adaptive boosting (Adaboost) and Active Shape Models (ASM) algorithm to get the changes on the face, such as degrees of eye closure and degrees of mouth opening. These parameters including grip force that are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The results show that use of multiple visual parameters combined with steering grip force can effectively detect the driver's fatigue.

SSW: Smart Steering Wheel for Real-Time Heart Rate Monitoring of Drivers

“…By capturing a driver's face expression and with that video images and force given on the grip of steering wheel by the driver were determined to find the health condition of a person. These parameters were congregated with the help of fuzzy classifier [8]. In [9], author uses binary decision classifier to determine the state of the driver.…”

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

“…Failure to detect a hand on the steering wheel or the detection of steering wheel turns beyond the lane may indicate unusual driver behaviour due to fatigue. The sensors for measuring driving parameters are easily mounted on a vehicle and relatively cheap; however, the data they provide may be affected by road or weather conditions, and these sensors should therefore be used together with devices of other categories [ 3 , 15 , 16 ].…”