Real-time system for monitoring driver vigilance

Abstract

Abstract-This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented.Index Terms-Driver vigilance, eyelid movement, face position, fuzzy classifier, percent eye closure (PERCLOS), visual fatigue behaviors.

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

“…By doing so, they can ensure the reliability and accuracy of the eye-gaze data; these studies have shown some promise of using eye gaze as a cue for driver intent. There have also been real-world studies that relied on automatically detecting eye gaze, but their results were limited due to robustness issues, particularly with regard to occlusions from sunglasses and harsh lighting conditions [25]- [28]. Finally, there have been several studies that achieved promising results using just head motion as a cue for behavior prediction [1], [7], [8], [29].…”

Driving Behavior Assessment Using Fuzzy Inference System and Low-Cost Inertial Sensors

“…In addition, the researchers have proposed the FACS (facial action coding system) [5]. This latter is based on the coding of the eyes and the lip movements and it utilizes the machine learning to detect the manners.…”

“…This latter is based on the coding of the eyes and the lip movements and it utilizes the machine learning to detect the manners. However, changing the light and the shadow disturbs the physical characteristics because of using the camera and the visual signals [5].…”

“…The advantage of the FISs appears when the driving behaviour estimation remains complex due to the system high complexity. Also, FISs are utilized for the knowledge induction process as they are the worldwide approximators [5].…”

“…The two major types of FISs are Mamdani and Sugeno-TSK that the recent literatures focused on the comparison of these two methods [5].…”