Stress Level Of Driver Research Articles

IoT-Based Assessment of a Driver's Stress Level.

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.

Bus priority lane in Bengaluru: A study on its effectiveness and driver stress

This paper studies the effectiveness of the bus priority lane (BPL) for public transport buses in the city of Bengaluru in India. We use the travel times on the BPL corridor as a measure of the effectiveness of the BPL. We find that there is a significant improvement in the travel times after the introduction of the BPL; for the worst 10% of the travel times, we find an improvement between 4% and 28%. Our methodology involves extracting trips on the BPL and computing the travel times for these trips from a time series of GPS information. Our methodology is scalable and can be used to compute the travel times between any two given points in other similar studies. We supplement our results with a novel test (called the D-test) for comparing the levels of stressful driving in the following scenarios: (a) morning peak hours (IST 07:00 h to 11:00 h) versus evening peak hours (IST 17:00 h to 21:00 h), and (b) northward trips versus southward trips on the BPL. We are able to infer that the drivers are generally more stressed during the morning peak hours and during the southward trips on the BPL. Partitioning the BPL into segments, we show that a majority of the segments exhibit similar effectiveness and driver stress trends as the full BPL stretch. We anticipate that corrective measures for the betterment of travel times and driver stress levels (e.g., introducing additional buses subject to vehicle re-balancing constraints, carefully planning the bus schedules to regulate bus traffic throughout the day, etc.) in some segments can lead to further improvements in travel times and reduction in driver stress levels.

Driver stress levels detection system using hyperparameter optimization

Stress and driving are a dangerous combination which can lead to crashes, as evidenced by the large number of road traffic crashes that involve stress. Therefore, it is essential to build a practical system that can classify driver stress level with high accuracy. However, the performance of such a system depends on hyperparameter optimization choices such as data segmentation (windowing hyperparameters). The configuration setting of hyperparameters, which has an enormous impact on the system performance, are typically hand-tuned while evaluating the algorithm. This tuning process is time consuming and there are also no generic optimal values for hyperparameters values. In this work, we propose a meta-heuristic approach to support automated hyperparameter optimization and provide a real-time driver stress detection system. This is the first systematic study of optimizing windowing hyperparameters based on Electrocardiogram (ECG) signal in the domain of driving safety. Our approach is to propose a framework based on Particle Swarm Optimization algorithm (PSO) to select an optimal/near optimal windowing hyperparameters values. The performance of the proposed framework is evaluated on two datasets: a public dataset (DRIVEDB dataset) and our collected dataset using an advanced simulator. DRIVEDB dataset was collected in a real-time driving scenario and our dataset was collected using an advanced driving simulator in the control environment. We demonstrate that optimizing the windowing hyperparameters yields significant improvement in terms of accuracy. The most accurate built model applied to the public dataset and our dataset, based on the selected windowing hyperparameters, achieved 92.12% and 77.78% accuracy, respectively.

Recognition of typical driving stressors and driver stress level in a Chinese sample

Drivers are adversely affected in decision-making and behavior under excessive stress, thus increasing road crash risks. In this study, the Driver Stress Inventory (DSI) was used to identify typical driving stress scenarios and explore the characteristics of drivers among different stress levels. A total of 1881 drivers took part in the survey. The Precedence Chart was used to rank the importance of driving stressors involved in the scale. K-means cluster was adopted to classify drivers’ stress into three levels, namely low, medium and high-stress. Finally, the Kruskal-Wallis test and Mantel-Haenszel test were employed to analyze the similarities and differences of demographic statistical characteristics under different stress levels. The results of the study indicate that various unexpected scenarios caused by the abnormal behavior of other road users are the most typical stressors. Drivers in the high-stress group tended to be younger and less experienced. Professional drivers reported higher stress than nonprofessional drivers. In addition, high-stress drivers were more prone to be involved in traffic crashes.

PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

Reducing the physical and mental weariness of drivers is significant in improving healthy and safe driving. This paper is aim to predict the stress level of drivers while braking in various conditions of the track. By discovering the drivers’ mental stress level, we are able to safely and comfortably adjust the distance in relation to the vehicle ahead. The initial step used was a study related to Artificial Intelligence (AI), Electroencephalogram (EEG), safe distance in braking, and the theory of mental stress. The data was collected by doing a direct measurement of drivers’stress levels using the EEG tool. The respondents were 5 parties around 30-50 years old who had experience in driving for> 5 years. The research asembled 400 pieces of data about braking including the data of the velocity before braking, track varieties (cityroad, rural road, residential road, and toll road), braking distance, stress level (EEG), and focus (EEG). The database constructed was used to input the machine learning (AI) – Back Propagation Neural Network (BPNN) in order to predict the drivers’ mental stress level. Referring to the data collection, each road type gave a different value of metal stress and focus. City road drivers used an average velocity of 23.24 Km/h with an average braking distance of 11.17 m which generated an average stress level of 53.44 and a focus value of 45.76.Under other conditions, city road drivers generated a 52.11 stress level, the rural road = 48.65, and 50.23 for the toll road. BPNN Training with 1 hidden layer, neuron = 17, ground transfer function, sigmoid linear, and optimation using Genetic Algorithm (GA) obtained the Mean Square Error (MSE) value = 0.00537. The road infrastructure, driving behavior, and emerging hazards in driving took part in increasing the stress level and concentration needs of the drivers. The conclusion may be drawn that the available data and the chosen BPNN structure were appropriate to be used in training and be utilized to predict drivers’ focus and mental stress level. This AI module is beneficial in inputting the data to the braking car safety system by considering those mental factors completing the existing technical factor considerations.

Designing an Intelligent System to Detect Stress Levels During Driving

In addition to the devastating effects of anxiety and stress on the development and exacerbation of the cardiovascular disease, lack of stress control increases drivers' risk of accidents. This paper aims to identify the stress of drivers in various driving situations to warn the driver to control the tense conditions during driving. In order to detect stress while driving, we used the heart signals in the Physionet database. To analyze the conditions of the electrocardiogram (ECG) under various driving situations, linear and non-linear features were used. The characteristics of the RRIs are the only able to identify driver stress in different driving modes relative to rest periods, while the return mapping features, in addition to identifying driver stress while resting, have the ability to identify stress between different driving positions also brought. The results showed that driver's stress level during driving in city 1 and highway 1 with a P-value of 0.028 and also in city 3 and highway 2 with a P-value of 0.041 can be distinguished. The accuracy obtained from the proposed detection method is 98±2% for 100 iterations. The result indicated an efficiency of our proposed method and enhanced the reliability.

Driver State Modeling Through Latent Variable State Space Framework in the Wild

Analyzing the impact of the environment on drivers’ stress level and workload is of high importance for designing human-centered driver-vehicle interaction systems and to ultimately help build a safer driving experience. However, driver’s state, including stress level and workload, are latent variables that cannot be measured on their own and should be estimated through sensor measurements such as psychophysiological measures. We propose using a latent-variable state-space modeling framework for driver state analysis. By using latent-variable state-space models, we model drivers’ workload and stress levels as latent variables estimated through multimodal human sensing data, under the perturbations of the environment in a state-space format and in a holistic manner. Through using a case study of multimodal driving data collected from 11 participants, we first estimate the latent stress level and workload of drivers from their heart rate, gaze measures, and intensity of facial action units. We then show that external contextual elements such as the number of vehicles as a proxy for traffic density and secondary task demands may be associated with changes in driver’s stress levels and workload. We also show that different drivers may be impacted differently by the aforementioned perturbations. We found out that drivers’ latent states at previous timesteps are highly associated with their current states. Additionally, we discuss the utility of state-space models in analyzing the possible lag between the two latent variables of stress level and workload, which might be indicative of information transmission between the different parts of the driver’s psychophysiology in the wild.

Predicting Driver Stress Levels with a Sensor-Equipped Steering Wheel and a Quality-Aware Heart Rate Measurement Algorithm.

Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g., drowsiness detection, these can be sensitive to varying lighting conditions and to driver facial accessories, such as eye/sunglasses. In this work, we evaluate the use of physiological signals derived from sensors embedded directly into the steering wheel. In particular, we are interested in monitoring driver stress levels. To achieve this goal, we first propose a modulation spectral signal representation to reliably extract electrocardiogram (ECG) signals from the steering wheel sensors, thus allowing for heart rate and heart rate variability features to be computed. When input to a simple logistic regression classifier, we show that up to 72% accuracy can be achieved when discriminating between stressful and non-stressful driving conditions. In particular, the proposed modulation spectral signal representation allows for direct quality assessment of the obtained heart rate information, thus can provide additional intelligence to autonomous driver monitoring systems.

Work-related stress in cycle rickshaw taxi drivers from Mexico City's Historic Downtown.

In Mexico, neither the pathological profile nor the health/illness conditions of the cycle rickshaw taxi drivers have been studied. Therefore, the factors that influence the physical and mental health of this labor group are unknown. To describe the characteristics of working and health/illness conditions that influence the stress level of the cycle rickshaw taxi drivers from Mexico City’s Historic Downtown. 90% (n = 110) of the workers participated in a cross-sectional study of a correlational type, in which a survey on working conditions and health was applied. Stress was analyzed using the Aro Symptomatic Scale, and vital signs, somatometry and a blood test were measured to analyze lipids and glucose. 16% of drivers were assessed with a high level of stress. No association was found with conditions related to physical and behavioral health, such as hypertension, dyslipidemia, diabetes, overweight, smoking and alcohol consumption. Therefore, the perception of violence risk (odds ratio [OR]: 1.19), the risks associated with the vehicle (OR: 1.18), having had a work accident (OR:38.2) and musculoskeletal complaints —mainly, in the elbow (OR: 1.20) and wrists (OR: 1.11)— are the factors that most influence the level of stress. The main cause of stress in drivers is related to the risks of their activity and musculoskeletal conditions, so interventions and action protocols are suggested to help preventing and face the risks of workers.

WITHDRAWN: Assessment of stress and safety practice level of heavy commercial passenger vehicle drivers using multi-grade fuzzy

Abstract Drivers’ Stress and Safety Practices (SSP) are very important for transportation organizations to save people’s lives, reduce fines and keep insurance premiums low. The purpose of this article is to evaluate the SSP level of heavy commercial passenger vehicle drivers in a transport organization using Multi-Grade Fuzzy (MGF) approach. The overall SSP index was calculated as 5.51 which indicate that the case transport organisation’s practice level is ‘moderately good practices’ and has scope for improvement. The stress and safety practice attributes are classified based on Importance-Performance Analysis (IPA). The results found that the weaker attributes of the case transport organisation were work timing, pragmatic trip scheduling, work-life balance and degree of freedom at work, which the organization has to improve. Using this framework, the any transport organisation can better evaluate their drivers’ SSP level.

Effect of Road Markings and Traffic Signs Presence on Young Driver Stress Level, Eye Movement and Behaviour in Night-Time Conditions: A Driving Simulator Study

The study investigates how the presence of traffic signalling elements (road markings and traffic signs) affects the behaviour of young drivers in night-time conditions. Statistics show that young drivers (≤30 years old) are often involved in road accidents, especially those that occur in night-time conditions. Among other factors, this is due to lack of experience, overestimation of their ability or the desire to prove themselves. A driving simulator scenario was developed for the purpose of the research and 32 young drivers took two runs using it: (a) one containing no road markings and traffic signs and (b) one containing road markings and traffic signs. In addition to the driving simulator, eye tracking glasses were used to track eye movement and an electrocardiograph was used to monitor the heart rate and to determine the level of stress during the runs. The results show statistically significant differences (dependent samples t-test) between the two runs concerning driving speed, lateral position of the vehicle, and visual scanning of the environment. The results prove that road markings and traffic signs provide the drivers with timely and relevant information related to the upcoming situation, thus enabling them to adjust their driving accordingly. The results are valuable to road authorities and provide an explicit confirmation of the importance of traffic signalling for the behaviour of young drivers in night-time conditions, and thus for the overall traffic safety.

Physiological and driving behaviour changes associated to different road intersections

Road traffic injuries claim more than 1.2 million lives each year in the world and have a huge impact on health and development. It is commonly acknowledged that the human factor and the interaction between the human factor and the road environment are among the most common causes of road accidents. Intersections are among the most complex road environments: their geometric and traffic characteristics weigh the driver workload, affecting the driving behaviour and consequently the risk of accident. This study intends therefore to contribute for a better understanding of the relationship between different types of intersection and the human factor. The ultimate aim is to understand how at grade intersections affect the driving behaviour by comparing the drivers’ stress level for roundabouts and standard intersections. Electrodermal activity can provide a real-time assessment of the driver's stress level. Electrodermal activity was therefore collected continuously during a driving study which took place on a test environment based at Cranfield University and surrounding roads. Twenty participants were involved within the study. The analysis focused on four crossing manoeuvres on three at grade intersections (two T-junctions and a roundabout) situated on the study location. Results showed that the number of SCR peaks as well as the amplitude of the peaks are overall higher for the two manoeuvres on the roundabout. The stress level induced by each type of intersection was evaluated through an Electrodermal Impact Index which takes into account both the number and the amplitude of SCR peaks. Results suggested that the stress level induced by roundabouts is more than double that induced by standard intersections.

SSW: Smart Steering Wheel for Real-Time Heart Rate Monitoring of Drivers

It’s very hard to know that Over 467,044 accidents took place specifically on the road in our country in 2018 as per a report revealed by the MoRTH recently. Rapid increasing in numbers of vehicles on the road, stress level of drivers in traffic and even lethargic driving are few reasons for road accidents. Unfortunately, sudden heart attacks of drivers also a reason behind some road accidents; this can be preventable. During driving, monitoring heart rate is not a big deal in today’s hi-tech world. Health monitoring while driving is not only important in daily healthcare, also a prevention of bus accidents. Here the proposed work is designing a smart steering wheel based on the technology so-called the Internet of Things (IoT) that monitors the heart rate and foresees the onslaught of fatigue of a driver; also it shares the location to the rescue team. Pulse sensor, GPS and GSM modules are conjoined into the steering wheel. The setup once it gets powered-up, which transmits the data of a driver’s health condition and get stored in the cloud environment. By this approach, the steering wheel gives the real-time based monitoring. Therefore this work is aiming for the development of a system which includes multiple sensors incorporated with the steering wheel which is capable to measure the pulse rate and alert the rescue team dynamically about the health related data of a driver, to prevent accidents.

Pedestrians influence on the traffic flow parameters and road safety indicators at the pedestrian crossing

The interaction between drivers and pedestrians at a pedestrian crossing is studied using the method of ergonomic design. The galvanic skin response (GSR) and changes of the heart rate of drivers have been measured to monitor any emotional state change when encountering a pedestrian at a pedestrian crossing. If the driver moves away from the pedestrian standing at the roadside edge, the driver experiences a minimal emotional change. However, if there is no vehicle speed and path variation, the driver experiences significant shift in the GSR and heart rate, thus highlighting a possible increase of the stress level. It was based on the research where the distance and current speed have a significant impact on the drivers and pedestrians with the following consequences for their health. To assess the influence of a pedestrian presence on the road on drivers, it was proposed to use the angular velocity of the vehicle with respect to pedestrians. The probability of the pedestrians presence in a dangerous condition for drivers at different distances and speeds of pedestrian crossing made it possible to find an optimal pedestrian position on the road. Thus, the paper proposed a safe distance for pedestrians on the road at a pedestrian crossing in order to reduce the driver stress level in the traffic flow.

Evaluation of driver stress level with survey, galvanic skin response sensor data, and force-sensing resistor data

Thousands of lives are lost in traffic accidents every year, and most traffic accidents are caused by driver errors. Causes and impairments such as fatigue, inattentiveness, alcohol usage, stress, and drugs are the main factors of these accidents. When a driver is subject to changing and complicated driving tasks in traffic, he or she should be able to assure driving authority to prevent potential hazards and accidents. In this context, the purpose of this study is to determine the stress level of the driver when driving in urban traffic in such situations requiring delegation of driving authority. Thus, the work combines stress questionnaire and galvanic skin response sensor to validate results and fuses with a force-sensing resistor. In this study, a prototype electric vehicle is equipped with sensors providing various drivers’ data including the responses of a force-sensing resistor sensor while galvanic skin is being collected on a specified route. At the end of the trip, the stress level of the drivers is determined by the collected data. Results indicate that the galvanic skin sensor stress results are consistent with the results of the survey with an average accuracy of 87.5%. The force-sensing resistor sensor is only used to determine gender stress. And the force-sensing resistor sensor gender-stress results are consistent with results of the survey with an accuracy of 100%. These results are used to validate the results of post-driving stress survey evaluated by SPSS 23.0 windows statistics software. Data analysis is particularly focused on demographic properties of participators, factor analysis, reliability tests, correlation, T-test, and one-way analysis of variance.

Study of older male drivers’ driving stress compared with that of young male drivers

In this study, older male drivers' stress while driving in straight links and while proceeding through intersections is investigated. Controller area network (CAN), self-reported stress (SRS), and physiological data was collected in 22.4 km-long experimental trips among older and young drivers. First, this study finds that older drivers reported much less stress than young drivers. However, principal components (PCs) of the physiological data demonstrate that older drivers might underrate their driving stress in entire trips, except regarding turning at intersections. Moreover, following other vehicles reduced older drivers' driving stress because preceding vehicles might help them control driving speed, detect the path, and prevent road risks. In contrast, the similar condition increased the stress level of young drivers. The results of random effects regression models confirm that age was the significant impact factor on SRS and physiological data. While examining whether the stress at intersections could affect their driving behaviors, significant difference between two age groups was found neither in turning time nor in the driving speed. This study also confirms that physical and mental changes with aging can negatively affect older adults' behaviors. Considering the relationships among stress, speed, and accidents, we suggest the provision of more driver assistance systems, training, and education and improving intersection design for older drivers.

Automatic driver stress level classification using multimodal deep learning

Stress has been identified as one of the contributing factors to vehicle crashes which create a significant cost in terms of loss of life and productivity for governments and societies. Motivated by the need to address the significant costs of driver stress, it is essential to build a practical system that can detect drivers’ stress levels in real time with high accuracy. A driver stress detection model often requires data from different modalities, including ECG signals, vehicle data (e.g., steering wheel, brake pedal) and contextual data (e.g., weather conditions and other ambient factors). Most of the current works use traditional machine learning techniques to fuse multimodal data at different levels (e.g., feature level) to classify drivers’ stress levels. Although traditional multimodal fusion models are beneficial for driver stress detection, they inherently have some critical limitations (e.g., ignore non-linear correlation across modalities) that may hinder the development of a reliable and accurate model. To overcome the limitations of traditional multimodal fusion, this paper proposes a framework based on adopting deep learning techniques for driver stress classification captured by multimodal data. Specifically, we propose a multimodal fusion model based on convolutional neural networks (CNN) and long short-term memory (LSTM) to fuse the ECG, vehicle data and contextual data to jointly learn the highly correlated representation across modalities, after learning each modality, with a single deep network. To validate the effectiveness of the proposed model, we perform experiments on our dataset collected using an advanced driving simulator. In this paper, we present a multi-modal system based on the adoption of deep learning techniques to improve the performance of driver stress classification. The results show that the proposed model outperforms model built using the traditional machine learning techniques based on handcrafted features (average accuracy: 92.8%, sensitivity: 94.13%, specificity: 97.37% and precision: 95.00%).

An energy efficient IoT data compression approach for edge machine learning

Many IoT systems generate a huge and varied amount of data that need to be processed and responded to in a very short time. One of the major challenges is the high energy consumption due to the transmission of data to the cloud.Edge computing allows the workload to be offloaded from the cloud at a location closer to the source of data that need to be processed while saving time, improving privacy, and reducing network traffic. In this paper, we propose an energy efficient approach for IoT data collection and analysis. First of all, we apply a fast error-bounded lossy compressor on the collected data prior to transmission, that is considered to be the greatest consumer of energy in an IoT device. In a second phase, we rebuild the transmitted data on an edge node and process it using supervised machine learning techniques. To validate our approach, we consider the context of driving behavior monitoring in intelligent vehicle systems where vital signs data are collected from the driver using a Wireless Body Sensor Network (WBSN) and wearable devices and sent to an edge node for stress level detection. The experimentation results show that the amount of transmitted data has been reduced by up to 103 times without affecting the quality of medical data and driver stress level prediction accuracy.

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions.