Driver Sleep Research Articles

Driver Drowsiness Detection and Alert System

Abstract: Driver drowsiness is a significant factor in road accidents, necessitating the development of effective detection and alert systems to mitigate this risk. This paper provides a comprehensive review of driver drowsiness detection and alert systems, examining the technologies, methodologies, and challenges associated with these systems. Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not. Despite advancements, challenges remain in achieving high accuracy and minimizing false alarms. Future directions include improving sensor technology, enhancing algorithm robustness, and addressing user acceptance issues. Overall, driver drowsiness detection and alert systems play a crucial role in enhancing road safety and warrant continued research and development efforts.

Reducing crash risk for young drivers: Protocol for a pragmatic randomised controlled trial to improve young driver sleep

BackgroundRoad trauma is a leading cause of death and disability for young Australians (15–24 years). Young adults are overrepresented in crashes due to sleepiness, with two-thirds of their fatal crashes attributed to sleepy driving. This trial aims to examine the effectiveness of a sleep extension and education program for improved road safety in young adults. MethodsYoung adults aged 18–24 years (n = 210) will be recruited for a pragmatic randomised controlled trial employing a placebo-controlled, parallel-groups design. The intervention group will undergo sleep extension and receive education on sleep, whereas the placebo control group will be provided with information about diet and nutrition. The primary outcomes of habitual sleep and on-road driving performance will be assessed via actigraphy and in-vehicle accelerometery. A range of secondary outcomes including driving behaviours (driving simulator), sleep (diaries and questionnaire) and socio-emotional measures will be assessed. DiscussionSleep is a modifiable factor that may reduce the risk of sleepiness-related crashes. Modifying sleep behaviour could potentially help to reduce the risk of young driver sleepiness-related crashes. This randomised control trial will objectively assess the efficacy of implementing sleep behaviour manipulation and education on reducing crash risk in young adult drivers.

EVALUATION OF READINESS OF AGRA MAS BUS DRIVERS BASED ON PHYSICAL, MENTAL,AND WORK ASPECTS USING THE FITNESS FOR DUTY MODEL

Fitness for duty (FFD) is an individual's ability to perform tasks in relation to the physical and mental health of each member and individual readiness to work.The development of a bus driver's work readiness model based on physical, mental, and work aspects is very important so that bus drivers are in a fit condition or ready to work. This study aims to develop a model of Agra Mas Bus driver's fitness for duty using measuring instruments in the form of the Visual Analogue Scale (VAS), Karolinska Sleepiness Scale (KSS) and Quantitative Analysis of Situational Awareness (QUASA). Thirty Agra Mas Bus drivers on the Wonogiri – Kendal – Karawang route participated in the study with independent variables such as age, weight, height, sleep duration, sleep quality, cigarette consumption, caffeine consumption, eye health, shift work, attention level, fatigue, and sleepiness, as well as the dependent variable in the form of FFD results of physical test screening. The results of this study indicate that the study and evaluation of the driver's job readiness level are quite good. That is, 100% of the drivers can be declared ready to be seen from the model that has been made. A total of 96.7% of drivers were correctly predicted, meaning that the predictive power or the accuracy of the model in classifying its observations was 96.7%. The ability of the model to predict the observation value is high, which is 75.8%. The adjusted R2 value of the model is 66.67%. VAS and KSS can be used to evaluate the work readiness of drivers, while QUASA cannot be used. Fatigue and sleepiness affect the driver's work readiness, while attention does not. Fatigue affects the driver's level of sleepiness. The more tired a driver is, the higher the level of sleepiness felt. A shift has no significant effect on attention, fatigue, and sleepiness. Shifts can affect the duration and quality of a driver's sleep. The expected results of this study can be a reference for predicting the fitness for duty of bus drivers so that the risk of road accidents is reduced.

A low-cost remote driver sleep monitoring system

Today, the use of smart transportation systems has become widespread due to the change in the structure of cities, technological developments, and the increase in the number of vehicles. It aims at targets such as reducing the damage to people and the environment by increasing traffic safety with smart transportation systems. In addition, one of the big problems of long-distance drivers is falling asleep while driving. This is an event that puts both the driver’s life, the passengers in the vehicle, and the vehicles on the road in danger, and puts the company in a difficult situation if it works for a company. In this direction, the driver sleep alarm project has been developed. This project is a control system based on monitoring the user, measuring, and analyzing their movements. Literature research, which is the first stage of the project, was conducted and research continued throughout the project process. Within the scope of this project, a headset was designed for the driver’s sleep alarm and a mobile application was created. The first prototype of the project was built. First, in the project, the angle values made by the driver's head were produced with the IMU connected to the microcontroller. 10° to 20° margins identified as sleeping state for roll, pitch, and yaw angles. Later, a wearable headset was designed for the driver. Based on these angles produced in the microcontroller, an algorithm has been created that detects whether the driver is asleep. These generated angle values and status information were transferred from the headset to the mobile application via Bluetooth. This data transferred in the mobile application is reflected on the screen. In addition, an algorithm has been created in the mobile application, which sends a notification to the driver when sleep status is detected and sends an informative SMS to the headquarters if the driver is unresponsive to this notification. For the second prototype, it was aimed to produce a PCB for the headset and thus make a new mechanical design. Data mining applications are planned with the collected data from drivers for future works.

Sleep behavior of New York City taxi drivers compared to the general US population

IntroductionTaxi drivers are a low income population with long work hours, a high-stress sedentary occupation, and varied work shifts, augmenting their risk for sleep disorders. We evaluated sleep quality among New York City (NYC) taxi drivers, a predominantly immigrant/minority population, for associations with sleep outcomes and examined intervention development and policy implications. MethodsA cross-sectional survey was conducted at driver-frequented locations with 211 consenting NYC taxi drivers and included the 12-item Medical Outcomes Study Sleep Scale and the Perceived Stress Scale-10. For five domains (sleep disturbance, snoring, shortness of breath, sleep adequacy, and somnolence) and Sleep Problems Index-II, self-reported scores were calculated and normed against a nationally representative 2009 US sample to obtain standardized t-test scores. Scores <47 were lower than US averages, indicating worse sleep quality. Results were explored in multivariable linear and logistic regression models. ResultsAlmost one-half (47%) of drivers had scores indicating sleep quality below US averages for snoring, 36% for shortness of breath/obstruction, and 19% for somnolence. There were significant associations of perceived stress with Sleep Problems Index II (p < 0.001), sleep disturbance (p < 0.001), somnolence (p < 0.001), and sleep adequacy (p < 0.05). Stress was predictive of sleep adequacy (p < 0.05, bivariate; p < 0.05, multivariate) and shortness of breath/obstruction (p < 0.01, bivariate; p < 0.001, multivariate). Nightshift drivers had significantly worse sleep disturbance scores than dayshift drivers (p < 0.05). Taxi drivers were more likely to get an inadequate amount of daily sleep (<7 h) than the average US male (48.5% vs. 38.3%). ConclusionsSleep hygiene and stress management interventions could benefit the health of this population and improve driver and public safety. The associations of taxi driver sleep quality and stress indicate an opportunity for targeted intervention. Further research into sleep as an important determinant of taxi driver health is needed, especially in the burgeoning for-hire vehicle (Uber, Lyft, etc) sector.

Driver Drowsiness Detection and Alert System

Nowadays, accidents occur during drowsy road trips and increase day by day; It is a known fact that many accidents occur due to driver fatigue and sometimes inattention, this research is primarily devoted to maximizing efforts to identify drowsiness. State of the driver under real driving conditions. The aim of driver drowsiness detection systems is to try to reduce these traffic accidents. The secondary data collected focuses on previous research on systems for detecting drowsiness and several methods have been used to detect drowsiness or inattentive driving.Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not.When the driver is sleepy, it plays the buzzer alarm and increases the buzzer sound. If the driver doesn't wake up, they'll send a text message and email to their family members about their situation. Hence, this utility goes beyond the problem of detecting drowsiness while driving. Eye extraction, face extraction with dlib.

Mindfulness, sleep, and post-traumatic stress in long-haul truck drivers.

The high stress culture and demands associated with long-haul truck driving place truckers at risk for mental health and sleep disorders, and thereby, increased risk for accidents, injuries, and fatality. Hours-of-service regulations have proven insufficient as a stand-alone intervention to protect the welfare of long-haul truckers, impacting those working in the industry and those sharing our nation's roads. Interventions to increase mindfulness have been used across occupational and personal domains to improve sleep quality, mental health, awareness of the environment, and reaction time. The purpose of this study was to examine the relationships between sleep, mental health, health care utilization, and mindfulness in long-haul truck drivers in the United States. Participants (N = 140) were recruited to complete a web-based survey. Descriptive statistics, bivariate analysis, and regression analysis were used to examine variables of interest. Post-traumatic Stress Disorder (PTSD) symptomology and daytime sleepiness predicted mental health care utilization in the past year. Mindfulness was inversely correlated with PTSD symptomology, however in the full regression model, mindfulness failed to predict mental health care utilization. Occupational health professionals should utilize mindfulness screenings as an adjunctive component to traditional mental health screenings and refer drivers for advanced care as appropriate.

Integrated Speed and Driver Fatigue Detected with Method Image Processing

Integrated Speed and fatigue drivers detected with the image processing method are devices that can give a warning of fatigue when the driver operates a truck or bus, so that potential accidents can be minimized caused by driver fatigue. This device uses the image processing method by image processing with camera right on the driver's face to determine facial changes that include formulated driver yawning intervals in the form of Mount Aspect Ratio (MAR), Eye Aspect Ratio (ER), Eye State (ES), Mouth State (MS), and the Slifa Sleepiness Scale (SSS) is a scale used to describe the driver's sleep level. The results of this research are a device that can detect driver fatigue levels by connecting to speed limiting devices, from the data obtained when range float EAR value less than 0.2 eye close and then value 0.2-0.3 eye condition half open and then greater than 0.3 eye condition open, range float MAR value less than 0.5 mouth close condition, 0,5-0,8 mouth half open and greater than 0.8 mouth open condition, SSS value scale 1-9, if driver condition scale 1-5 fatigue not detected, if the driver scale 6-9 driver fatigue detected.

Fighting fatigue: A conceptual model of driver sleep in the gig economy

The purpose of this paper was to integrate the sleep science, occupational health, and organizational psychology literature to develop a conceptual model of driver sleep and fatigue in the gig economy. We develop an integrative framework, which proposes that aspects of the on-demand driving context influence driver sleep health and fatigue. Driver outcomes include safety incidents, injuries, health, job attitudes, interpersonal behavior, and performance. In addition, moderators, such as driver demographics and health conditions, can interact with aspects of the driver context. A number of practical implications are provided, addressing the ways in which occupational health researchers, online labor platform companies, and drivers can improve sleep health. This is the first paper to provide a broad understanding of how scientists, through both research and practice, can help improve sleep, a primary issue in the ridesharing industry.

On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters

A proper design of human–robot interaction strategies based on human cognitive factors can help to compensate human limitations for safety purposes. This work is focused on the development of a human–robot interaction system for commercial vehicle (Renault Twizy) driving, that uses driver cognitive parameters to improve driver’s safety during day and night tasks. To achieve this, eye blink behavior measurements are detected using a convolutional neural network, which is capable of operating under variable illumination conditions using an infrared camera. Percentage of eye closure measure values along with blink frequency are used to infer diver’s sleepiness level. The use of such algorithm is validated with experimental tests for subjects under different sleep-quality conditions. Additional cognitive parameters are also analyzed for the human–robot interaction system such as driver sleep quality, distraction level, stress level, and the effects related to not wearing glasses. Based on such driver cognitive state parameters, a human–robot interaction strategy is proposed to limit the speed of a Renault Twizy vehicle by intervening its acceleration and braking system. The proposed human–robot interaction strategy can increase safety during driving tasks for both users and pedestrians.

Assessing the impact of sleep time on truck driver performance using a recurrent event model.

Driver fatigue is a major safety concern for commercial truck drivers and is directly related to the total hours of sleep prior to a working shift. To evaluate changes in driving performance over a long on-duty driving period, we propose a mixed Poisson process recurrent-event model with time-varying coefficients. We use data from 96 commercial truck drivers whose trucks were instrumented with an advanced in situ data acquisition system. The driving performance is measured by unintentional lane deviation events, a known performance deterioration related to fatigue. Driver sleep time and other activities are extracted from a detailed activity register. The time-varying coefficients are used to model the baseline intensity and difference among three cohorts of shifts in which the driver slept less than 7 hours, between 7 to 9 hours, and more than 9 hours prior to driving. We use the penalized B-splines approach to model the time-varying coefficients and an expectation-maximization algorithm with embedded penalized quasi-likelihood approximation for parameter estimation. Simulation studies show that the proposed model fits low and high event rate data well. The results show a significantly higher intensity after 8 hours of on-duty driving for shifts with less than 7 hours of sleep prior to work. The study also shows drivers tend to self-adjust sleep duration, total driving hours, and breaks. This study provides crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on-road safety implications of insufficient sleep and breaks while driving.

Quality of the sleep of truck drivers that travel in the region of the extreme-west of Santa Catarina

This study aimed to perform a subjective evaluation of the sleep quality of truck drivers in the extreme western region of Santa Catarina. For this purpose, a questionnaire was used to evaluate the participants' profile and two other questionnaires to measure the degree of sleepiness (Epworth Scale) and Pittsburgh sleep quality index. The sample consisted of 179 truck drivers, all of them male, aged between 21 and 65 years; 77.65% married and 22.34% single; 72.6% have children, with an average of two children per participant. As for the professional profile, 75.9% of the interviewees work as wage earners and 24.1% as freelancers. The type of route covered, in most cases, is long distance, representing 66%, and 34% being short distance routes. Regarding the educational level, 53.5% of the participants have elementary education; 42.4% high school and 3.9% were graduates. Overweight or obesity was identified in 59.80% of the interviewees. About sleep quality, 22.5% reported sleeping less than 5 hours daily; 35.1% from 5h to 6h; 29.0% from 6h to 7h; 12.8% sleep more than 7 hours. On the Epworth Sleepiness Scale, 67.0% presented no sleepiness; 27.3% presented mild degree of somnolence; 5.5% had moderate degree of somnolence. Regarding the Pittsburgh Sleep Quality Index, it was verified that 55.8% presented subjective poor sleep quality. The results suggested that lifestyle and poor habits can negatively influence the health of these professionals.

Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

Driver monitoring is added as a primary safety measure in the EuroNCAP roadmap 2025. Especially with the introduction of automated driving systems into the market, new requirements are set to driver monitoring systems (DMSs). When not being actively involved in driving, the risk of drivers becoming drowsy and even falling asleep at the wheel increases. Modern DMSs will have to be able to detect a driver falling asleep or sleeping in order for the automation to take appropriate actions. Conventional measures for detecting the driver state such as analysing the driving behaviour are not available in automated driving. The aim of the study was to identify potential physiological measures as a basis for the development of systems that are able to detect sleep in drivers during automated driving. A within-subjects study with N = 21 subjects was conducted in a high-fidelity driving simulator. Electromyography, electrodermal activity (EDA), respiration and electrocardiography (ECG) were measured in drivers during states of wakefulness and sleep. Sleep stages were assigned with the electroencephalography as a ground truth. The results indicate the potential of EDA and ECG parameters to differentiate between sleep and wakefulness. Implications for the implementation in DMS are discussed.

Assessing the relationship between heavy vehicle driver sleep problems and confirmed driver behavior measurement tools in Iran

Road traffic crashes have become an important public health problem. By studying the statistics of heavy vehicle crashes in the world, it becomes obvious that although heavy vehicle drivers experience fewer crashes than private car drivers, a large percentage of heavy vehicle crashes leads to death. So, it is important to identify the driving characteristics of heavy vehicle drivers. In this study, 474 Iranian truck drivers were interviewed face to face. Response rate of the survey was 80%, the participants who answered the questions on two behavioral questionnaires: Driver Behavior Questionnaire (DBQ) and Attention-Related Driving Error Scale (ARDES). Also, two sleep assessing questionnaires, Global Dissatisfaction with Sleep (GSD) and the Berlin Questionnaire, were completed. Confirmatory Factor Analysis (CFA) was used to confirm the validity of DBQ and ARDES for Iranian heavy vehicle drivers. After that, a Structural Equation Modeling (SEM) was used to identify the influence of sleep characteristics on truck driver behaviors. Results showed that more truck drivers are dissatisfied with their sleep, the more daily fatigue they suffer, and consequently, the more errors, slips, violations, and inattention they exhibit. Similar result trend was obtained for driver exposure. Also, the results showed that the more expensive a truck is, the less daily fatigue felt by the driver of the truck. Consequently, this leads to less aberrant driving behavior and inattention during driving.

Intervention for regulating sleep–wake rhythms to prevent sleepiness in Japanese train drivers

The nocturnal sleep of train drivers is apt to be short and their lifestyle become irregular because they drive at midnight and early in the morning. As a result, train drivers often feel sleepy, which is one of the factors causing human errors. The present study investigated whether regulating the lifestyle and sleep–wake rhythms would be effective for preventing sleepiness among Japanese train drivers. An intervention study was conducted with train drivers (N = 134, mean age 33.22 ± 9.59 years) for regulating their sleep–wake rhythms. Sleep–wake rhythms of train drivers became more regular after the intervention, and their daytime sleepiness decreased as a result. These findings suggest that regulating the lifestyle is effective for preventing daytime sleepiness of irregular shift workers, such as train drivers.

Sleep quality, obstructive sleep apnea, and psychological distress in truck drivers: a cross-sectional study.

Sleep disorders are highly prevalent in truck drivers. The aim of this study is to evaluate the prevalence of self-reported obstructive sleep apnea (OSA) and poor sleep quality in truck drivers, and to analyze the relationship between sleep problems and mental health. A total of 526 male truck drivers (mean age 45.9; DS 9.4) responded to a questionnaire about risk factors for OSA (STOP-Bang), sleep quality perception (Pittsburg sleep questionnaire inventory, PSQI), excessive daytime sleepiness (Epworth sleepiness scale, ESS), and psychological disorders (general health questionnaire, GHQ-12). It was found that 51.1% of the drivers were at risk of OSA, 17.3% had bad sleep quality, and 8.9% had excessive daytime sleepiness (EDS). The association between psychological distress, OSA (OR = 1.67; CI 95% 0.99-2.83; p = 0.057), bad sleep quality (OR = 2.58; CI 95% 1.52-4.37; p < 0.001), and EDS (OR = 1.65; CI 95% 0.83-3.30; p = 0.151) was assessed. The high prevalence of sleep problems, especially suspected OSA, and low quality of sleep in truck drivers can worsen the general and psychological wellbeing of the workers. Educational programs focusing on sleep hygiene and behavioral interventions are needed to promote adequate sleep habits and improve individual and public health.

Health and wellness of long-haul truck and bus drivers: A systematic literature review and directions for future research

Abstract Objectives The purpose was to provide a comprehensive review of the literature related to the health and wellness of truck (long and short-haul) and bus drivers in Canada and the USA. Methods The following databases were searched: Medline (Pubmed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO and Scopus, as well as the grey literature using a combination of key words (e.g. truck or bus drivers, accidents, health, wellness, road safety). Only English articles published between 2000 and 2016 were included. The search yielded 33 peer reviewed articles and 9 reports relevant to the health and wellness of CMV drivers. Results The findings show that long-haul truck drivers have multiple risk factors (i.e., smoking, obesity, hypertension, poor diet, lack of exercise, stress and sleep) that can lead to various medical conditions (i.e., cardiovascular disorders, diabetes) and adverse events (i.e., crashes). Several medical conditions including sleep apnea (and fatigue more generally), obesity and cardiovascular disorders are all associated with increased crash risk. There was little information on bus drivers or short-haul truck drivers, however, the available information would suggest they are also exposed to negative work and driver environments leading to the development of risk factors associated with medical conditions. Conclusions Further research is needed to characterize the work environment and lifestyle practices (particularly sleep, smoking, diet and exercise) of truck and bus drivers to understand the interactions between various risk factors and health outcomes. Obtaining baseline information, including national prevalence rates of health issues, is vitally important for public health, regulatory organizations, and industry to coordinate prevention efforts.

Nouvelles technologies en santé : ubérisation ou augmentation ?

Taxi drivers are a low income population with long work hours, a high-stress sedentary occupation, and varied work shifts, augmenting their risk for sleep disorders. We evaluated sleep quality among New York City (NYC) taxi drivers, a predominantly immigrant/minority population, for associations with sleep outcomes and examined intervention development and policy implications.A cross-sectional survey was conducted at driver-frequented locations with 211 consenting NYC taxi drivers and included the 12-item Medical Outcomes Study Sleep Scale and the Perceived Stress Scale-10. For five domains (sleep disturbance, snoring, shortness of breath, sleep adequacy, and somnolence) and Sleep Problems Index-II, self-reported scores were calculated and normed against a nationally representative 2009 US sample to obtain standardized t-test scores. Scores <47 were lower than US averages, indicating worse sleep quality. Results were explored in multivariable linear and logistic regression models.Almost one-half (47%) of drivers had scores indicating sleep quality below US averages for snoring, 36% for shortness of breath/obstruction, and 19% for somnolence. There were significant associations of perceived stress with Sleep Problems Index II (p < 0.001), sleep disturbance (p < 0.001), somnolence (p < 0.001), and sleep adequacy (p < 0.05). Stress was predictive of sleep adequacy (p < 0.05, bivariate; p < 0.05, multivariate) and shortness of breath/obstruction (p < 0.01, bivariate; p < 0.001, multivariate). Nightshift drivers had significantly worse sleep disturbance scores than dayshift drivers (p < 0.05). Taxi drivers were more likely to get an inadequate amount of daily sleep (<7 h) than the average US male (48.5% vs. 38.3%).Sleep hygiene and stress management interventions could benefit the health of this population and improve driver and public safety. The associations of taxi driver sleep quality and stress indicate an opportunity for targeted intervention. Further research into sleep as an important determinant of taxi driver health is needed, especially in the burgeoning for-hire vehicle (Uber, Lyft, etc) sector.

Detecting slow eye movement for recognizing driver's sleep onset period with EEG features.

Slow eye movement (SEM) is reported as a reliable indicator of sleep onset period (SOP) in sleep researches, but its characteristics and functions for detecting driving fatigue have not been fully studied. Through visual observations on ten subjects' experimental data, we found that SEMs tend to occur during eye closure events (ECEs). SEMs accompanied with alpha wave's attenuation during simulated driving was observed in our study. We used box plots to analyze the distribution of durations of different ECEs to measure sleepiness level. Experimental results indicate that the ECEs with SEM have higher duration distribution, representing higher sleepiness level, especially for those accompanied by alpha wave's attenuation. This verifies that SEM can be used as a reliable indicator for recognizing driver's SOP. In light of this and considering the possible accompanying of Electroencephalograph (EEG) wave changes, we propose a new algorithm for detecting SEM, which extracted EEG power related features from occipital O2 signal to add them into features set of horizontal Electro-Oculogram (HEOG) signal. Then, maximum relevance and minimum redundancy (mRMR) method was used for feature selection and support vector machine (SVM) was used to classify the SEM class and non-SEM class. Experimental results demonstrate that using EEG power related features can improve the algorithm's accuracy by an average 1.4%. The feature P(α+θ)/β was ranked highest by mRMR among all EEG features, indicating the interactive relationship between EEG waves and SEM.

Real Time Implementation for Monitoring Drowsiness Condition of a Train Driver using Brain Wave Sensor

Driver fatigue and lack of sleep of drivers especially those who drive for a longer period of time as train accidents are a longer standing problem. It has been observed that each year numerous train accidents and fatalities may occur around the world due to driver falling asleep while driving the train. There are various traditional methods that may facilitate to detect drowsiness state of the driver to warn in such a manner, so that such accidents may be prevented to large extent. In this implementation, a system for determining drowsiness state of a driver to avoid an accident is disclosed. In one aspect, the system comprises a brainwave sensor, a microcontroller, and an alarm unit. The sensor which is the Brain Computer Interface (BCI) may be attached to one or more touch points for sensing a brainwave emitted by neurons in a brain of the driver. The microcontroller, coupled with the sensor, configured to analyze the mind in order to determine a category of the brainwaves. The brainwave may be categorized into the category based upon a predefined frequency range associated with the brainwave of types Delta, Theta, Alpha and Beta. The in-built Bluetooth will be paired up with the microcontroller to start messaging regarding the status of the driver through the GSM modem, and soon after it will alarm in the motor or in nearby station if required.