Driver's Blinking Research Articles

Anti-Sleep Alarm for Drivers

Abstract: Nowadays it is very difficult to stay active all the time due to busy schedules. Falling asleep at the wheel can lead to serious consequences, accidents, and even death. This situation is much more common than we realize and therefore it is very important to fight this problem. So, to solve this problem, we developed a sleep alarm for drivers. This system alerts the user when he falls asleep at the wheel, thus preventing accidents and saving lives. This system is handy for long-distance travelers and late-night drivers. Designed with a blink sensor, it takes safety measures a step further by designing a system to stop the vehicle when the driver's eyes are closed. The system is developed using Arduino microcontroller. The eye blinking sensor is connected to a microcontroller that continuously monitors the frequency of the driver's eye blinking.If the driver's blink rate falls below a certain threshold, the system warns the driver and sends a signal to the vehicle's controlsystem to stop the vehicle.

Designing a Glass Mounted Warning System to Prevent Drivers to Fall in Sleep Based on Neck Posture and Blinking Duration.

Background:In this study, an electronic system based on driver's neck position and blinking duration is designed to help prevent car crashed due to driver drowsiness. When a driver falls in sleep his/her head is felled down. Hence, driver's neck posture can be a good sign of sleep which is measured utilizing a two?dimensional accelerator. However, this sign is not enough because he/she may need to look down during a drive and alarming driver by every moving down of head can be annoying.Methods:Thus, in this system, we used blinking duration too. When a person is awake, blinks more frequently than when he is drowsy.Result:As a result, in this system, blinking is detected using an infrared transceiver and if both conditions, i.e., neck posture and blinking duration are showing signs of sleep mode, driver will be alarmed.Conclusion:In this study, it is designed 2D accelerometer and IR sensor based system to measure the driver's neck angle and detect driver's blinking to realize the drowsiness of vehicle drivers and alert them using these signs of drowsiness.

Comprehensive Drowsiness Level Detection Model Combining Multimodal Information

This paper presents a drowsiness detection model that is capable of sensing the entire range of stages of drowsiness, from weak to strong. The key assumption underlying our approach is that the sitting posture-related index can indicate weak drowsiness that drivers themselves do not notice. We first determined the sensitivity of the posture index and conventional indices for the stages of drowsiness. Then, we designed a drowsiness detection model combining several indices sensitive to weak drowsiness and to strong drowsiness, to cover all drowsiness stages. Subsequently, the model was trained and evaluated on a dataset comprised of data collected from approximately 50 drivers in simulated driving experiments. The results indicated that posture information improved the accuracy of weak drowsiness detection, and our proposed model using the driver's blink and posture information covered all stages of drowsiness (F1-score 53.6%, root mean square error 0.620). Future applications of this model include not only warning systems for dangerously drowsy drivers but also systems which can take action before their drivers become drowsy. Since measuring the information requires no restrictive equipment such as on-body electrodes, the model presented here based on blink and posture information can be used in several practical applications.

Effects of In-vehicle Information on Driver Blink Characteristics and Workload

A real-vehicle experiment was carried out to study the effects of in-vehicle information on driver workload, during which data of the driver blink duration and frequency were collected to check for discrepancies among drivers with and without vehicle navigation usage. In the meanwhile, the blink characteristics of drivers with vehicle navigation device mounted at three different positions were explored through image prompt or image & sound multi-channel simultaneous prompt. Experimental results showed that when the data of blink with a duration of 0-200ms was distributed at a 10ms interval, the driver blink count distribution curve shows obvious bimodal characteristics. The peak of blink with 50-60ms duration was lower than that with 10-20ms duration when vehicle navigation was not used, and higher when vehicle navigation was used. The difference between medium and long blinks was not significant when the navigation device was mounted at different positions, yet the short blinks showed a significant difference. The peaks of blink count without voice navigation were all greater than those with voice navigation. In particular, without voice navigation, the short blinks increased obviously, and the medium blinks increased relatively, but the long blinks remained almost unchanged. The above results indicate that the driver workload was greater when using vehicle navigation. When the navigation device is installed in position B, the driver workload reaches the minimum. Using voice navigation could reduce driver workload.

Modular design of fatigue detection in naturalistic driving environments

Research in driver mental fatigue is motivated by the fact that errors made by drivers often have life-threatening consequences. This paper proposes a new modular design approach for the early detection of driver fatigue system taking into account optimisation of system performance using particle swarm optimisation (PSO). The proposed system is designed and implemented using an existing dataset that was simultaneously collected from participants and vehicles in a naturalistic environment. Four types of data are considered as fatigue-related metrics including: vehicle acceleration, vehicle rotation pattern, driver's head position and driver's head rotation. The driver's blink rate data is used in this work as a proxy for ground truth for the classification algorithm. The collected data elements are initially fed to input modules represented by ternary neural network classifiers that estimates alertness. A Bayesian algorithm with PSO is then used to combine and optimise detection performance based on the number of existing input modules as well as their output states. Performance of the developed fatigue-detection system is assessed experimentally with a small data samples of driver trips. The obtained results are found in agreement with the state-of-the-art in terms of accuracy (90.4%), sensitivity (92.6%) and specificity (90.7%). These results are achieved with significant design flexibility and robustness against partial loss of input data source(s). However, due to small sample size of dataset (N = 3), a larger dataset need to be tested with the same system framework to generalise the findings of this work.

A System on Intelligent Driver Drowsiness Detection Method

We actualized a fatigue driver recognition framework utilizing a mix of driver's state and driving conduct pointers. For driver's express, the framework observed the eyes' blinking rate and the flickering span. Fatigue drivers have these qualities higher than ordinary levels. We utilized a camera with machine vision procedures to find out and watch driver's blinking behavior. Harr's feature classifier was utilized to first find the eye's range, and once found, a layout coordinating was utilized to track the eye for fast preparing. For driving conduct, we gained the vehicle's state from inertial measurement unit and gas pedal sensors. The principle component analysis was utilized to choose the components that have high change. The difference esteems were utilized to separate weakness drivers, which are accepted to have higher driving exercises, from typical drivers.

How does long trip affect on driving performance, eye blinks and awareness of sleepiness among commercial drivers? A naturalistic driving test study

A total of 120 commercial drivers from four age groups participated in the expressway driving test in Shandong, China to perform 2, 3, and 4 h continuous driving tasks and collect the data on the driver's blinking, driving performance and self-reported level of sleepiness. Two-way repeated measures ANOVA was used to evaluate the effects of driving duration on the variation of the eye-blink behavior, driving performance and subjective feeling of sleepiness across the different age groups over the time periods tested. Additionally, Pearson product-moment correlation was used to quantify the association between the variations of the dependent variables. The results showed that there was significant difference between groups, significant effect over time and significant interaction between the age and driving duration in the variations of the driver’s blink frequency, blink duration, closure duration, speed perception, choice reaction time, number of incorrect action judgments and subjective level of sleepiness. However, a significant difference varied over time, but no effect of the interaction between groups and time were found in the variation of the driver’s attention allocation value. Furthermore, driver’s eye blink measures were more sensitive to sleepiness and older drivers were more likely to get sleepy in long distance driving.

Method of detecting drowsiness level by utilizing blinking duration

The purpose of this study is to detect the drowsiness of drivers to prevent traffic accidents. We have developed a system which measures the blinking duration of a driver by processing his facial image and estimates his drowsiness level. This paper presents an imaging unit which is less affected by ambient light, a method of measuring the driver's blinking duration which is independent of his facial features, and a method of estimating his drowsiness level from a change in his blinking duration.

生体計測応用 画像による瞬目計測と居眠り検知への応用

Many traffic accidents are caused by drowsiness while driving. The purpose of this research is to detect a driver's blinking using facial images obtained by a TV camera and to estimate the driver's arousal level to prevent the occurrence of an accident due to drowsiness. This report presents the method used to obtain the facial images, which are not affected much by the change of the ambient light by day and by night owing to the infrared light sources. This paper also describes the method for extracting the blinks and measuring the blinking durations, which are not influenced very much by individual differences in eye shape. Furthermore, we experiment with the relation between blinking durations and arousal levels as compared with vehicle deviation from the lane and driver's answer to the question about drowsiness. We also examine a method for estimating arousal level which reflects individual characteristics.