Driver Stress Research Articles

Impact of Weaving Segment Configuration Designs on Drivers’ Acute Driving Stress: A Case Study on Houston Freeway Weaving Segments

Weaving turbulence on freeways significantly increases the demand for complex driving maneuvers, leading to high driver stress. This study explored the impact of freeway weaving segment configuration designs on drivers’ stress attributed to complex vehicle maneuvers in dynamic traffic. An on-road driving test was conducted to measure healthy, young drivers’ heart rate variability, a biomarker of driving stress, while they performed various freeway weaving maneuvers under different traffic conditions. Statistical analyses were conducted to identify specific weaving maneuvers that triggered high- and medium-level driver stress. The probability distributions of the heart rate variability were estimated and the probabilities of high- and medium driving-stress were determined. Results indicate that intense demand of vehicle maneuvers significantly increases drivers’ stress, especially in the driving scenarios, where two-lane changes were required and the base length of weaving segments were shorter than 400 m or longer than 700 m under medium traffic, or the non-weaving segments were under medium traffic. The baseline driver stress probability is 3.34%. This grew dramatically to more than 60% under the driving scenarios. The results of the study concluded that two-sided weaving segment type, the shorter base length of weaving segments, and medium traffic, are the determinants for a higher likelihood of the driving stress.

Identification of Stress State for Drivers Under Different GPS Navigation Modes

It is commonly known that Global Positioning System (GPS) can alleviate travelling difficulties of automobile drivers, and generally we hold the view that it reduces the driver's stress when they are in unfamiliar road conditions. In this research, an in-laboratory experiment and an in-car experiment are conducted to find out whether GPS instructions can reduce or may induce additional mental stress of drivers. Electrocardiography (ECG) signals are collected in the experiments and the extracted heart rate variability (HRV) features are used for analysis. Three binary classifiers, specifically Support Vector Machine, k-Nearest Neighbor (k-NN) and Random Forest, are trained based on the data collected in the in-laboratory experiment, where the stress state is elicited by the Stroop color word Test. The k-NN classifier outperforms the other two classifiers, and thus is applied to the data collected in the in-car experiment, to identify drivers' stress state under different driving events, such as waiting for traffic lights, turning, under GPS instructions, and traffic conditions like overtaking, or changing lanes. During each event, whether the driver is in stress or relaxed state for each time instant is predicted based on the trained classifier. The percentages of time that the driver is in stress state for each type of events are computed. It shows that GPS instructions cause the second largest time-percentage of stress state, lower than that caused by the turning event, but higher than that caused by the events of waiting for traffic lights and other traffic conditions.

Situation-cognitive traffic light control based on object detection using YOLO algorithm

Current traffic lights provide the green signal with fixed-time interval without considering the traffic situation. As a result, cars in a long line have to wait long time, which causes traffic jams and makes the drivers be irritated. In order to solve the problem, it is necessary to control the green signal interval according to the analysed traffic volume using the image processing and the machine learning techniques. This paper presents a situation-cognitive traffic light control algorithm that measures the traffic volume using object detection algorithm called you only look once (YOLO) and controls the traffic signal intervals according to the traffic volume. The algorithm expects the smooth traffic flow and the reduction of the driver's stress.

Situation-cognitive traffic light control based on object detection using YOLO algorithm

Current traffic lights provide the green signal with fixed-time interval without considering the traffic situation. As a result, cars in a long line have to wait long time, which causes traffic jams and makes the drivers be irritated. In order to solve the problem, it is necessary to control the green signal interval according to the analysed traffic volume using the image processing and the machine learning techniques. This paper presents a situation-cognitive traffic light control algorithm that measures the traffic volume using object detection algorithm called you only look once (YOLO) and controls the traffic signal intervals according to the traffic volume. The algorithm expects the smooth traffic flow and the reduction of the driver's stress.

Optimised Traffic Light Management Through Reinforcement Learning: Traffic State Agnostic Agent vs. Holistic Agent With Current V2I Traffic State Knowledge

Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., the positions and velocities of vehicles approaching intersections, and holistic systems that exploit knowledge of the current traffic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably and quickly collect the current traffic state. However, to the best of our knowledge, the optimized traffic light management without and with current traffic state information has not been compared in detail. This study fills this gap in the literature by designing representative Deep Reinforcement Learning (DRL) agents that learn the control of multiple traffic lights without and with current traffic state information. Our agnostic agent considers mainly the current phase of all traffic lights and the expired times since the last change. In addition, our holistic agent considers the positions and velocities of the vehicles approaching the intersections. We compare the agnostic and holistic agents for simulated traffic scenarios, including a road network from Barcelona, Spain. We find that the holistic system substantially increases average vehicle velocities and flow rates, while reducing CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, average wait and trip times, as well as a driver stress metric.

A Study on The Driving Characteristics and The Driver stress of Longer and Heavier Vehicle

A Study on The Driving Characteristics and The Driver stress of Longer and Heavier Vehicle

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.

A Marked Point Process Filtering Approach for Tracking Sympathetic Arousal From Skin Conductance

Human emotion represents a complex neural process within the brain. The ability to automatically recognize emotions from physiological signals has the potential to impact humanity in multiple ways through applications in human-machine interaction, remote health monitoring, smart living environments and entertainment. We present a marked point process-based Bayesian filtering approach to track sympathetic arousal from skin conductance features. The rate at which individual skin conductance responses (SCRs) occur and their respective amplitudes encode important information regarding an individual's psychological arousal level. We develop a state-space model relating a latent neuropsychological arousal state to the rate at which neural impulses underlying SCRs occur and the impulse amplitudes. We simultaneously estimate both arousal and the state-space model parameters within an expectation-maximization framework. We evaluate our method on both simulated and experimental data. Results on simulated data indicate the method's ability to accurately estimate an unobserved state from marked point process observations. The experimental data include studies involving mental stress artificially-induced in laboratory environments, real-world driver stress and Pavlovian fear conditioning. Results on experimental data outperform previous Bayesian filtering approaches in terms of a lower sensitivity to small impulses and avoiding overfitting. The filter is thus able to estimate emotional arousal from skin conductance features and is a promising approach for everyday emotion recognition applications.

System of control of the status «performance – sleep – tension» of drivers of vehicles based on the electric signal analysis

This paper presents a study of heart rate variability indicators based on a phase-frequency analysis of an electrocardiogram. As part of the task of diagnosing the state of drowsiness and stress of vehicle drivers, a method for determining the level of wakefulness is described. We studied electrocardiograms with deviations of the heart rate from the normal signals. Examples of the analyzed signals, their parameters, as well as analysis results are given. Based on the obtained data, it is planned to build a multi-threshold analysis system.

Examining the effects of stress on the driving abilities of paramedic students

Background: Research has suggested that stress may have a negative effect on paramedics' clinical performance. Stress has also been demonstrated to negatively affect the driving abilities of the general population, increasing the number of driving errors. However, no studies have explored stress and its potential impact on the non-clinical performance of paramedics, particularly their driving abilities. Methods: Paramedic students underwent emergency driving assessment in a driving simulator before and after exposure to a stressful medical scenario. The number and type of errors were documented before and after through the use of both driving simulator software and observation by two members of the research team. The NASA Task Load Index (TLX) was used to record self-reported stress levels. Results: Thirty-six students participated in the study. Following exposure to a stressful medical scenario, paramedic students had no rise in overall error rate, but demonstrated increases in three critical driving errors: namely, failure to wear a seat belt (three baseline versus 10 after stress); failing to stop for red lights or stop signs (seven versus 35); and losing control of the vehicle (two versus 11). Self-reported stress levels also increased after the clinical scenario, particularly in the area of mental (cognitive) demand. Conclusion: Paramedics are routinely exposed to acute stress in their everyday work, and this stress could affect their non-clinical performance. The critical errors committed by participants in the present study closely matched those considered to be contributory factors in many ambulance collisions. These results illustrate the need for further research into the effects of stress on non-clinical performance in general, and highlight the potential need to consider additional driver training and stress management education to mitigate the frequency and severity of driving errors among paramedics.

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.

Psychoemotional stress in review of ESC/ESH recommendations for treatment of hypertension (2018) and clinical trial results

Psychoemotional stress is a risk factor for cardiovascular disease. The ESC/ESH recommendations for the treatment of hypertension (2018) define psychological stress as a risk factor for hypertension. The basis of hypertension of the ‘white coat’ and masked hypertension are considered psychosocial factors. Stress causes early damage to target organs and uncontrolled blood pressure. The article discusses the mechanisms of formation of arterial hypertension under the influence of stress. On the example of clinical studies, the negative role of psycho-emotional stress in extreme professions is considered. The data of their own research on the effect of stress in locomotive drivers on the development of cardiovascular diseases are presented.

Methods to Detect and Reduce Driver Stress: A Review

Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver’s personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver’s attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver’s awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic.

Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic

Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart rate, heart rate variability, respiratory rate, and subjective responses of nine test drivers in the Tesla Model S with Autopilot. The participants, who were experienced with driver assistance systems but naïve to the Tesla, drove a 32 min motorway route back and forth while following a lead car in regular traffic. In one of the two drives, participants performed a heads-up detection task of bridges they went underneath. Averaged across the two drives, the participants’ mean self-reported overall workload score on the NASA Task Load Index was 19%. Moreover, the participants showed a reduction in heart rate and self-reported workload over time, suggesting that the participants became accustomed to the experiment and technology. The mean hit (i.e., pressing the button near a bridge) rate in the detection task was 88%. In conclusion, driving with the Tesla Autopilot on a motorway involved a low level of workload that decreased with time on task.

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%).

Predictive power of selected factors over driver stress at work

Professional drivers are considered prone to health risks. For this reason we have conducted a predictive study to analyze variables that may be predictors of stress in driving. Participating in this study were 372 drivers (93.4% men, 6.6% women) recruited through non-probabilistic sampling. The aim of the study is to develop a prediction model for job stress in professional drivers using the following indicators: personality, impulsiveness, hardy personality, job, age, seat comfort, seat suspension, lumbar support and driving hours. We found that the variables with predictive power over driving stress were: commitment over relaxed driving (ΔR 2 = 0.101; β = 0.135), danger prevention (ΔR 2 = 0.139; β = 0.342) and fatigue and anxiety (ΔR 2 = 0.063; β = −0.227); control over alertness and vigilance (ΔR 2 = 0.069; β = 0.278); agreeableness over sensation-seeking (ΔR 2 = 0.047; β = −0.268). In conclusion, driver stress can be predicted by certain variables. This study contributes to better understanding of driver stress and promotes safety at the wheel, thus helping to prevent traffic accidents.

Predictive variables for musculoskeletal problems in professional drivers

BackgroundProfessional drivers are considered prone to health risks with a high prevalence of diverse types of pain. Several authors have analyzed how certain work characteristics can produce musculoskeletal disorders in professional drivers. Drivers usually report back pain as a stressor, but they also report pain in areas such as the neck, shoulders and knees. Physical agents (vibrations and noise), postural stress, high density of traffic, numerous and frequent tasks put professional drivers at high risk of musculoskeletal disorders, fatigue, effects on drivers’ mental health. For this reason, we have conducted a predictive study to analyze variables that may be predictors of stress in driving. MethodsIn the present work we develop a predictive model for musculoskeletal disorders in professional drivers that uses the following indicators: Age, Gender, Seat Comfort, Seat Suspension, Adjustable Lumbar Support for the driver's seat, Driving Hours, Sleep Quality, Driver Stress, Irritation, Hardiness, Burnout, Safety Behaviors and Impulsivity. Participants in the study were 372 professional drivers from various transport sectors recruited via non-probabilistic sampling. For this study we used the SPSS 25.0 program. ResultsThe variables to predict trunk musculoskeletal problems were: Psychophysiological Disorders, Challenge, and ME Extremities. These variables account for 30.6% of the criterion variable's variance. The variables to predict musculoskeletal problems of the extremities were: Psychophysiological Disorders, ME Truck, Cognitive Irritation, Seat Suspension, Hours, Cynicism and Emotional Irritation. These variables account for 34.3% of the criterion variable's variance. The best predictor was Psychophysiological Disorders. ConclusionsThis study helps to extend knowledge of musculoskeletal disorders with the aim of improving the health of professional drivers. The results highlight the importance of designing individual interventions to reduce the incidence of musculoskeletal problems in professional drivers. This would provide greater well-being and lead to a reduction in sick leave.

Feedback Game-Based Shared Control Scheme Design for Emergency Collision Avoidance: A Fuzzy-Linear Quadratic Regulator Approach

Driver-machine shared control scheme opens up a new frontier for the design of driver assistance system, especially for improving active safety in emergency scenario. However, the driver's stress response to steering assistance and strong tire nonlinearity are main challenges suffered by controller designing for collision avoidance. These unfavorable factors are particularly pronounced during emergency steering maneuvers and sharply degrade shared control performance. This paper proposes a fuzzy-linear quadratic regulator (LQR) game-based control scheme to simultaneously enhance vehicle stability while compensating driver's inappropriate steering reaction in emergency avoidance. A piecewise linear-based Takagi–Sugeno (T–S) fuzzy structure is presented to mimic driver's knowledge about vehicle lateral nonlinearity, and the control authority is shared between driver and emergency steering assistance (ESA) system through steer-by-wire (SBW) assembly. An identical piecewise internal model is chosen for ESA and the shared lane-keeping problem is modeled as a fuzzy linear quadratic (LQ) problem, where the symmetrical fuzzy structure further enhances vehicle's ability to handle extreme driving conditions. In particular, the feedback Stackelberg equilibrium solutions of the fuzzy-LQ problem are derived to describe the interactive steering behavior of both agents, which enables the ESA to compensate driver's irrational steering reaction. Hardware-in-the-loop (HIL) experiment demonstrates the ESA's capability in compensating driver's aggressive steering behavior, as well as the copiloting system's excellent tracking and stabilizing performance in emergency collision avoidance.

Frequency and Severity of Tree and Other Fixed Object Crashes in Florida, 2006—2013

Roadside trees provide benefits to drivers such as traffic calming, roadway definition, and driver stress reduction. However, trees are also one of several roadway infrastructure elements commonly involved in single-vehicle crashes. In this study, Florida Highway Saftey and Motor Vehicle records were analyzed to: evaluate the relative frequency of tree-related crashes compared to other fixed-object crashes; assess the impact of roadway-, vehicle-, and driver-related factors on tree crash frequency; and compare the severity of tree crashes relative to other single-vehicle crashes. In accessing 3,033,041 crash records from 2006 to 2013 (all complete years), we identified 565,303 single-vehicle accidents (10.5%) and 47,341 tree-related accidents (1.6%). Trees were the fourth most common fixed object hit in urban single-vehicle accidents and the second most common fixed object hit in rural single-vehicle accidents. Driver gender, vehicle type, light conditions, weather conditions, and land use all were correlated with the frequency. Additionally, the injuries associated with tree crashes were more severe than all other single-vehicle crash types except vehicle rollovers.

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.