Driver State Research Articles

PERCEPTIONS OF PROFESSIONAL AND UNPROFESSIONAL DRIVERS ABOUT THEIR OWN BEHAVIOURS AND SKILLS

According to Social Identity Theory people tend to be positive for members of group to which they feel as belonging and to be negative for members of group to which they do not feel as belonging. Such perception bias is thought to be exist among different group of drivers in traffic, as well. In this context, this study aims to investigate professional (like taxi drivers) and unprofessional (private car users) drivers’ driver skills, safety concerns and risk-taking behaviors and together with researching perception of each group of drivers toward the other. 40 unprofessional and 39 professional drivers participated in the study. After each participant assessed himself/herself through Driver Skill Inventory (DSI) and risk-taking behavior scale, s/he also evaluated members of the other group of drivers through these scales. One-way analysis of variance (ANOVA) revealed significant differences between the two groups. Specifically, it was found that professional drivers state their driver skills and safety concerns higher and their risk-taking behaviors lower than unprofessional drivers in traffic. Moreover, each group’s self-assessment scores with regard to driving skills, safety concerns and risk-taking behaviors are generally in line with the other group’s perception of that group on these variables.

Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study.

Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.

A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods

Introduction: The existing selection of driving distraction recognition methods is based on a specific research perspective and does not provide comprehensive information on the entire field of view. Method: We conducted a systematic review of previous studies, aiming to come up with appropriate research methods to identify the driver's distraction state. First, this article selects four sets of search keywords related to driving distraction discrimination from five databases (Web of Science, ScienceDirect, Springer Link, IEEE, and TRID) and identifies 1,620 peer-reviewed documents from 2000 to 2020; these 1,620 documents underwent bibliographic analysis and co-occurrence network analysis. The co-occurrence coupling relationship is analyzed from the aspects of time, country, publication, author and keywords. Second, 37 papers published were screened, and the driving distraction recognition methods proposed by these 37 papers were summarized and analyzed. Results: The results show that this field has been prevalent since 2013; countries such as the United States, Britain, Germany, Australia, China, and Canada are in the forefront of research in this field, and the cooperation between related countries is relatively close. The cooperation between authors is characterized by aggregation, and the mobile phone as the main keyword is almost connected to other keyword nodes; the recognition model of deep learning algorithm based on video surveillance data sources has become the mainstream hot spot distraction recognition method. The recognition model of machine learning algorithm based on vehicle dynamics data, driver physiology, and eye movement data sources has specific advantages and disadvantages. Practical Applications: The results can help people to understand the current situation of driving distraction comprehensively and systematically, provide better theoretical support for researchers to choose the subsequent driving distraction recognition model, and provide research direction for driving distraction recognition in the future.

ESTIMATION OF INFLUENCE OF PSYCHOPHYSIOLOGICAL CONDITION OF THE DRIVER ON SAFETY OF PASSENGER AUTOMOBILE TRANSPORTATIONS

The safety of road passenger transport depends on the main key elements that make up the system: driver-car-road-environment. Failure of any element of this system increases the probability of its exit from the normal-functional state, and most importantly, will increase the risk of an accident (hereinafter - road accident). Therefore, the search for ways to reduce the likelihood of accidents during passenger road transport based on the analysis of the consequences of changes in the psychophysiological states of the driver is an urgent task.
 The purpose of the study is to develop recommendations for reducing the probability of accidents during passenger road transportation based on the analysis of the consequences of changes in the psychophysiological states of a driver. The study used the method of expert assessments and the method of "Failure Mode and Effects Analysis" (hereinafter the method "FMEA"), which includes organizational, logical and mathematical and statistical procedures aimed at obtaining from experts the assessment of hazards affecting the change the psychophysiological state of a driver while driving a passenger bus, their analysis and generalization of the results in order to prepare rational decisions.
 It is determined that changes in the psychophysiological state of a driver lead to errors in driving a passenger bus and are the main catalyst, which together with various hazards when driving a vehicle and joint interaction of the subject with the object - leads to traffic violations and further - to road accidents.
 It is shown that in case of elimination of one of the listed components, the road accident will not occur. It is established that the loss of concentration, changes in emotional state and intensity of the load are the main factors that affect the psychophysiological state of a passenger bus driver. The dependence of the accident on the presence of the catalyst, the hazard, and the interaction of the subject with it is determined. It is proposed to use the driver's reaction as a catalyst, which depends on the change of their psychophysiological state while driving a passenger bus.
 Keywords: driver, psychophysiological condition, passenger bus, hazard, interaction, catalyst.

An exploratory study of drivers’ EEG response during emergent collision avoidance

Introduction: EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers’ mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety–critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process. Method: A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants’ EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage. Results: The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages. Conclusions: Enhanced activities of different EEG bands could be concurrent at emergent and safety–critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process. Practical Applications: The study generates more understanding of drivers’ neurophysiological response in an emergent and safety–critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

Change of drivers’ functional condition during dangerous goods transportation

The study of the human factors in traffic safety is a relatively young scientific field that began to develop several decades before the mass car advent. The origins of transport psychology and psychophysiology were mostly aimed at creating more comfortable and safe driving conditions due to the design of highways. Over time, there was a need to take into account drivers working conditions, namely the ergonomics of vehicles. The latest works have already taken into account previous experience and considered the issues of freight and passenger transportation technological processes, including the human factor. Among the objects and subjects of research in previous works were considered the processes of driving vehicles with different ergonomic characteristics and buses of different capacities, which created a kind of case matrix for planning experiments. When it comes to trucks, the matrix of cases became much larger, as the set of ergonomic and dynamic features of vehicles adds a much wider set of specialization of cargo and truck types. In this case, the most important issue is to determine the road conditions impact the driver's functional condition, engaged in the transport of dangerous goods, which is the purpose of this article. The research used: methods of field survey to establish the values of traffic flow on highways; methods of in-house research to determine the value of road capacity; electrophysiological methods to determine changes in the driver's functional condition. The article describes the typing of road conditions according to their complexity, as well as the differentiation of drivers according to their psychological and professional qualities. Experimental studies of changes in driver's functional state of different age and socionic groups with different road conditions complexity, during dangerous goods transportation (second class of risk). The main results indicate that the analysis of traffic conditions' impact on the driver's functional state shows different dynamics of psycho-emotional stress change for people characterized by certain types of the nervous system and the ratio of their age to work experience.

DistractNet: a deep convolutional neural network architecture for distracted driver classification

Distracted driving has been considered one of the reasons for traffic accidents. The american national highway traffic safety administration (NHTSA) defines distracted driving as any activity that takes attention away from driving, such as doing makeup, texting, calling, and reaching behind. Most deaths, physical injuries, and economic losses could have been prevented if the distracted driver is alerted on time. This paper has proposed a new convolutional neural network (CNN) called DistractNet to detect drivers' distractions. The proposed model was trained and tested by state farm distracted driver detection image datasets available at Kaggle that contains images of drivers in the most common activities performed, which lead to distraction while driving divided into ten classes. Also, we have studied the performances of the proposed CNN model based on accuracy, training time, and model size. The performance of the proposed model was compared with four pre-trained networks such as ResNet-50, GoogLeNet, InceptionV3, and AlexNet using transfer learning techniques. The obtained experimental results show that the developed model-based CNN can achieve an overage accuracy of more than 99.32% with 93 min of training time and 7.99 MB of size. The extracted model can classify driver states into ten different classes with the predicted label and probability % for each class.

Classification of Driver Cognitive Load: Exploring the Benefits of Fusing Eye-Tracking and Physiological Measures

In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

Carrying a passenger and relaxation before driving: Classification of young drivers' physiological activation.

Drivers are often held responsible for road crashes. Previous research has shown that stressors such as carrying passengers in the vehicle can be a source of accidents for young drivers. To mitigate this problem, this study investigated whether the presence of a passenger behind the wheel can be predicted using machine learning, based on physiological signals. It also addresses the question whether relaxation before driving can positively influence the driver's state and help controlling the potential negative consequences of stressors. Sixty young participants completed a 10‐min driving simulator session, either alone or with a passenger. Before their driving session, participants spent 10 min relaxing or listening to an audiobook. Physiological signals were recorded throughout the experiment. Results show that drivers experience a higher increase in skin conductance when driving with a passenger, which can be predicted with 90%‐accuracy by a k‐nearest neighbors classifier. This might be a possible explanation for increased risk taking in this age group. Besides, the practice of relaxation can be predicted with 80% accuracy using a neural network. According to the statistical analysis, the potential beneficial effect of relaxation did not carry out on the driver's physiological state while driving, although machine learning techniques revealed that participants who exercised relaxation before driving could be recognized with 70% accuracy. Analysis of physiological characteristics after classification revealed several relevant physiological indicators associated with the presence of a passenger and relaxation.

Investigation of Three Potential Stress Inducement Tasks During On-Road Driving

A driving study was performed to induce stress with 24 participants performing different inducement tasks (n-back task, Sing-a-Song Stress Test and noise exposure). Both performance-based measures (Tactile Detection Response Task) as well as subjective measures were recorded to assess the driver state. Subjective ratings indicate that stress was most successfully induced on a group level with the n-back task with high inter-individual variation. The average response times doubled during the n-back task for a simultaneously performed tactile detection response task compared to baseline response times. Sympathetic nervous activation resulting in the increase of heart rate, respiration rate and decrease in heart rate variability (RMSSD) was found as a physiological reaction on stress-inducing secondary tasks. The most prominent physiological responses were found during the modified Sing-a-Song Stress Test. Subjective ratings on the perceived stress level and physiological response rarely correlated. This study provided reference data for driver state algorithm development in the EU-funded project ADAS&ME.

Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe

Abstract Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

Technologies for Risk Mitigation and Support of Impaired Drivers

This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.

Unobtrusive Measurement of Physiological Features Under Simulated and Real Driving Conditions

Objective: For driver state estimation, physiological features might be promising input parameters. As cable-bound sensing of these parameters is impractical for ubiquitous monitoring, the measurement certainly has to be based on unobtrusive and contact-free technologies. In this work, unobtrusive methods for heart rate (HR) and respiration rate (RR) monitoring, including a hybrid imaging approach, are evaluated under simulated and real driving conditions. Methods: The feasability of unobtrusive methods was tested by comparing measurements from unobtrusive sensors to reference sensors. Under laboratory conditions, magnetic induction and photoplethysmography, both integrated into the seat belt, and hybrid imaging, combining visual and thermal imaging, were evaluated for RR sensing. In real driving, creating an urban and a rural scenario, sensing of RR by hybrid imaging and sensing of HR by a seat-integrated capacitive ECG were evaluated. Results: Under laboratory conditions, a reliable RR detection was possibly using all three sensor technologies. In real-world driving, a reliable HR and RR detection was possible during the rural scenario. In the urban scenario, only the RR detection was feasible. Due to motion artifacts, the capacitive ECG was disturbed and the HR detection impaired. Conclusion: The evaluated unobtrusive measurement systems can monitor physiological parameters during e.g. long-time driving on highways, but may not yet be feasible for monitoring during agile inner-city driving situations, due to motion artifacts. Therefore, future work should focus on artifact reduction. Significance: Physiological features might be used as input parameters for driver state estimation systems. This work presents unobtrusive sensing methods for these parameters.

Adaptive User Experience in the Car—Levels of Adaptivity and Adaptive HMI Design

Advancements in driver state detection and artificial intelligence allow for more and more user-centred and individual experiences. Intelligence and adaptivity in the vehicle context address the three main goals: Increasing safety, usability and empathy in vehicle systems. Adaptivity of systems can be evaluated by considering the technical system features, user-interface-related features and the actual user experience of the adaptive system. We provide an overview of classifications for adaptive systems including the Levels of Adaptive Sensitive Responses (LASR). The levels differentiate the input that a system considers in its operations to adapt to user groups or to the individual user. Along with that, we propose User Experience (UX) design guidelines applicable to the different levels. In an online survey, we varied LASR and one of the UX design guidelines, namely transparency. The within-subjects study showed that both, the levels and the variation of transparency, influenced the perception of intelligence, transparency and intuitive design. However, a significant proportion of users did not understand the difference between the two LASR versions, indicating that users build mental models of systems that imply more personal data usage than the system actually employs. The LASR framework allowed this differentiation to be revealed in system performance and user perception. More research is necessary to elaborate the correlation between levels of adaptivity, UX design, specific UX design guidelines and user experience measures.

Physiological Measures of Risk Perception in Highly Automated Driving

Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers’ availability to take-over manual control in safety-critical scenarios. In this empirical research, we measured drivers’ (N = 20) risk perception with cardiac and skin conductance indicators through a series of high-fidelity, simulated highly automated driving scenarios. By manipulating the presence of surrounding traffic and changing driving conditions as long-term risk modulators, and including a driving hazard event as a short-term risk modulator, we hypothesised that an increase in risk perception would induce greater physiological arousal. Our results demonstrate that heart rate variability features are superior at capturing arousal variations from these long-term, low to moderate risk scenarios. In contrast, skin conductance responses are more sensitive to rapidly evolving situations associated with moderate to high risk. Based on this research, future driver state monitoring systems should adopt multiple physiological measures to capture changes in the long and short term, modulation of risk perception. This will enable enhanced perception of driver readiness and improved availability to safely deal with take-over events when requested by an automated vehicle.

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Driver fatigue detection system aims to monitor the driver state. When detecting a fatigue caused by different attitudes other than normal driving habit, the system warns the driver that traveling should be interrupted. In this way, it helps the driver to make the right decision. The aim of this study is to prevent traffic accidents. The system analyzes any changes in the driver's eyes and mouth features in real time and warns the driver when necessary. The proposed system contains several stages to detect the driver's fatigue. First, the preprocessing stage; enhancement of the frames, determining the face, and cropping eyes and mouth of the driver was done. Then, dealing with feature extraction stage; the features concerning each frame was processed. Finally, two classification approaches were presented and a comparison between them was addressed. In the first approach, four traditional classifiers were applied; Diagonal Linear Discriminant Analysis (DiagLDA), Linear Support Vector Machine (LSVM), K-Nearest Neighbor (KNN), and Random Forest Classifier (RFC). The results show that two classifiers; KNN and RFC yield the highest average accuracy of 91.94% for all subjects presented in this paper. In the second approach, one model of deep learning neural network (CNN) was applied; "Resnet-50" model. The results also show that the proposed deep learning model yields a high average accuracy of 96.3889% for the same data. In general, the drowsiness and lost focus of drivers with high accuracy have been detected with the developed image processing based system, which makes it practicable and reliable for real-time applications.

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review.

Theory of Mind (ToM)—the ability of the human mind to attribute mental states to others—is a key component of human cognition. In order to understand other people's mental states or viewpoint and to have successful interactions with others within social and occupational environments, this form of social cognition is essential. The same capability of inferring human mental states is a prerequisite for artificial intelligence (AI) to be integrated into society, for example in healthcare and the motoring industry. Autonomous cars will need to be able to infer the mental states of human drivers and pedestrians to predict their behavior. In the literature, there has been an increasing understanding of ToM, specifically with increasing cognitive science studies in children and in individuals with Autism Spectrum Disorder. Similarly, with neuroimaging studies there is now a better understanding of the neural mechanisms that underlie ToM. In addition, new AI algorithms for inferring human mental states have been proposed with more complex applications and better generalisability. In this review, we synthesize the existing understanding of ToM in cognitive and neurosciences and the AI computational models that have been proposed. We focus on preference learning as an area of particular interest and the most recent neurocognitive and computational ToM models. We also discuss the limitations of existing models and hint at potential approaches to allow ToM models to fully express the complexity of the human mind in all its aspects, including values and preferences.

Peculiarities of psychophysiological state of car drivers in the dynamics of the working shift

Introduction. Fatigue driving is one of the main causes of accidents. According to statistics, up to 30% of road traffic accidents occur due to driving in a state of fatigue or tiredness. To prevent the onset of accidents and injuries to drivers, it is necessary to study the functional capabilities of employees in order to optimize work modes and justify the duration of the working day depending on age and health group. The aim of the work is to study changes in the psychophysiological state of drivers of light vehicles of different age groups in the dynamics of work shifts. Materials and methods. To assess the work intensity of drivers, an analysis of the indicators of the central nervous and cardiovascular systems was carried out. Assessment of the state of the central nervous system was determined using a blank test with Landolt rings and a complex visual-motor reaction. The state of the cardiovascular system was determined by blood pressure indicators by the Korotkov method, registration of heart rate, the index of functional changes in the circulatory system according to Baevsky was calculated. A hygienic assessment of the heaviness and intensity of the labor process was carried out according to the Guidelines R 2.2.2006-05. Results. According to the results of the hygienic assessment, it was found that the work of drivers of light vehicles corresponds to class 3.1 in terms of heaviness and class 3.2 in terms of the intensity of the labor process. In the dynamics of the drivers' daytime work shifts, a deterioration in the indicators of attention and reaction speed was revealed. It was found that the highly stressful work of drivers negatively affects the indicators of body systems, this is confirmed by high levels of diastolic, systolic pressure and an increase in heart rate during the entire work shift, which exceed the physiological norms of working stress. Conclusion. The data obtained indicate an overstrain of the body of drivers of passenger vehicles in the dynamics of the entire period of their examination, which, with chronic exposure, can contribute to the development of industrial-related diseases of the cardiovascular system. It is necessary to further develop recommendatory documents for specific categories of drivers of light vehicles, containing a system of preventive measures aimed at maintaining their performance during the work shift, which can include both a specialized set of physical exercises, gymnastics for the eyes, and methods of self-regulation and relaxation, used to normalize sleep, relieve stress, restore psychophysiological state, etc.

Multimodal driver state modeling through unsupervised learning

Naturalistic driving data (NDD) can help understand drivers’ reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver’s state and behavioral patterns. Unsupervised analysis of NDD can be used to automatically detect different patterns from the driver and vehicle data. In this paper, we propose a methodology to understand changes in driver’s physiological responses within different driving patterns. Our methodology first decomposes a driving scenario by using a Bayesian Change Point detection model. We then apply the Latent Dirichlet Allocation method on both driver state and behavior data to detect patterns. We present two case studies in which vehicles were equipped to collect exterior, interior, and driver behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal brake, curved driving, and highway driving), as well as two patterns of driver’s heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy (i.e., low versus high), were detected in these two case studies. The findings of these case studies indicated that among our participants, the drivers’ HR had a higher fraction of abnormal patterns during harsh brakes, accelerating and curved driving. Additionally, free-flow driving with close to zero accelerations on the highway was accompanied by more fraction of normal HR as well as a lower gaze entropy pattern. With the proposed methodology we can better understand variations in driver’s psychophysiological states within different driving scenarios. The findings of this work, has the potential to guide future autonomous vehicles to take actions that are fit to each specific driver.

Driver Alertness Identification And Alarming System Using Haar Cascade Algorithm

Motorist fatigue has been one of the top causes of automobile accidents throughout the world in recent years. The state of the driver, i.e. drowsiness, is a simple way of determining driver fatigue. It is vital to recognise the driver's tiredness in order to protect lives and property. The purpose of this project is to construct a prototype of a drowsiness detection system. This is a real-time system that continuously captures photos and analyses the eye's condition using the approach described, as well as delivering warnings as required. Although various methods for assessing fatigue exist, this technique is fully non-intrusive and so has no effect on the driver, revealing the driver's genuine state. The retina's per-closing value is utilised to detect whether or not a person is tired. When a driver's eyelids close more than a particular amount, he or she is deemed drowsy. This system is made up of numerous OpenCv libraries, the most important of which being Haar-cascade. Furthermore, to improve the driver's security, as well as to check if the driver is adhering to the "do not drunk and drive" rule. Before the automobile starts, the amount of alcohol is detected, and if the driver is determined to be drunk, the automobile will not start. This keeps the driver out of trouble while simultaneously keeping him safe.