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.

Driver drowsiness detection based on multisource information

“…Decreased vehicle control due to driver drowsiness is one of the major causes of road accidents. It is estimated that crashes resulting from driver drowsiness account for 10%–20% of all vehicle accidents (Bergasa, Nuevo, Sotelo, Barea, & Lopez, 2006; Dinges, Mallis, Maislin, & Powell, 1998). The National Highway Traffic Safety Administration conservatively estimates that 100,000 police‐reported crashes are caused by drowsy drivers each year in the United States alone.…”

Driver Fatigue Monitoring System Using Video Images and Steering Grip Force

“…So we used the fuzzy set to estimate fatigue with various parameters obtained in the precious section. Fuzzy inference system has a reputation for its well-known linguistic concept modeling ability and can be used for knowledge induction processes [15].…”

“…Four experimental subjects have been tested in the simulated driving environment, which two of them wearing glasses named User1 and User3. A thorough frame by frame investigation has been conducted to verify the accuracy, which is stated in Table 2.The average accuracy is 99.42% .We choose a representation 795 frames fatigue clip which the PERCLOS is above 0.15 within the longest closure time duration [15] to demonstrate the transition from normal to fatigue. Figs 6 and 7 are off-line processed results for the percentage of openness of the eyes and mouth.…”

Formal Detection of Attentional Tunneling in Human Operator–Automation Interactions

“…A consistent method to avoid such drawbacks is to use automated machine-learning techniques [22], as they allow mathematical links between inputs and outputs to be established from a statistically sound point of view, rather than relying on experts. The machine-learning literature provides a wide range of methods and algorithms to learn efficient classifiers (e.g., neural networks [12], support vector machines (SVMs) [23], hidden Markov models [24]) from which one can find the appropriate method for the specific application. Neuro-fuzzy learning [25] is well suited in this particular case as in addition to its learning ability, the method retains the advantages of fuzzy logic.…”