Physiological and behavioural changes associated to the management of secondary tasks while driving

Predicting Driver's mental workload using physiological signals: A functional data analysis approach

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

The Effects of Secondary Task Demands on Cybersickness in Active Exploration Virtual Reality Experiences

Active exploration in virtual reality (VR) involves users navigating immersive virtual environments, going from one place to another. While navigating, users often engage in secondary tasks that require attentional resources, as in the case of distracted driving. Inspired by research generally studying the effects of task demands on cybersickness (CS), we investigated how the attentional demands specifically associated with secondary tasks performed during exploration affect CS. Downstream of this, we studied how increased attentional demands from secondary tasks affect spatial memory and navigational performance. We discuss the results of a multi-factorial between-subjects study, manipulating a secondary task's demand across two levels and studying its effects on CS in two different sickness-inducing levels of an exploration experience. The secondary task's demand was manipulated by parametrically varying $n$ in an aural $n$-back working memory task and the provocativeness of the experience was manipulated by varying how frequently users experienced a yaw-rotational reorientation effect during the exploration. Results revealed that increases in the secondary task's demand increased sickness levels, also resulting in a higher temporal onset rate, especially when the experience was not already highly sickening. Increased attentional demand from the secondary task also vitiated navigational performance and spatial memory. Overall, increased demands from secondary tasks performed during navigation produce deleterious effects on the VR experience.

An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving

By the year 2045, it is projected that Autonomous Vehicles (AVs) will make up half of the new vehicle market. Successful adoption of AVs can reduce drivers' stress and fatigue, curb traffic congestion, and improve safety, mobility, and economic efficiency. Due to the limited intelligence in relevant technologies, human-in-the-loop modalities are still necessary to ensure the safety of AVs at current or near future stages, because the vehicles may not be able to handle all emergencies. Therefore, it is important to know the takeover readiness of the drivers to ensure the takeover quality and avoid any potential accidents. To achieve this, a comprehensive understanding of the drivers' physiological states is crucial. However, there is a lack of systematic analysis of the correlation between different human physiological responses and takeover behaviors which could serve as important references for future studies to determine the types of data to use. This paper provides a comprehensive analysis of the effects of takeover behaviors on the common physiological indicators. A program for conditional automation was developed based on a game engine and applied to a driving simulator. The experiment incorporated three types of secondary tasks, three takeover events, and two traffic densities. Brain signals, Skin Conductance Level (SCL), and Heart Rate (HR) of the participants were collected while they were performing the driving simulations. The Frontal Asymmetry Index (FAI) (as an indicator of engagement) and Mental Workload (MWL) were calculated from the brain signals to indicate the mental states of the participants. The results revealed that the FAI of the drivers would slightly decrease after the takeover alerts were issued when they were doing secondary tasks prior to the takeover activities, and the higher difficulty of the secondary tasks could lead to lower overall FAI during the takeover periods. In contrast, The MWL and SCL increased during the takeover periods. The HR also increased rapidly at the beginning of the takeover period but dropped back to a normal level quickly. It was found that a fake takeover alert would lead to lower overall HR, slower increase, and lower peak of SCL during the takeover periods. Moreover, the higher traffic density scenarios were associated with higher MWL, and a more difficult secondary task would lead to higher MWL and HR during the takeover activities. A preliminary discussion of the correlation between the physiological data, takeover scenario, and vehicle data (that relevant to takeover readiness) was then conducted, revealing that although takeover event, SCL, and HR had slightly higher correlations with the maximum acceleration and reaction time, none of them dominated the takeover readiness. In addition, the analysis of the data across different participants was conducted, which emphasized the importance of considering standardization or normalization of the data when they were further used as input features for estimating takeover readiness. Overall, the results presented in this paper offer profound insights into the patterns of physiological data changes during takeover periods. These findings can be used as benchmarks for utilizing these variables as indicators of takeover preparedness and performance in future research endeavors.

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving

The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.

Persistent effects of mobile phone conversation while driving after disconnect: Physiological evidence and driving performance

Cognitive workload has been known as a key factor in traffic accidents, which can be highly increased by talking on the phone while driving. A wide range of studies around the world investigated the effects of mobile phone conversations on driving performance and traffic accidents. But less noticed is the durability of cognitive effects of mobile phone conversations. This study aimed to determine the effects of different types of mobile phone conversations on physiological response and driving performance during and after the conversation. Heart rate, heart rate variability (physiological response), Standard deviation of lane position (SDLP), and the relative distance between two cars (driving performance) of 34 samples (male and female) in the driving simulator were recorded. In this study, three types of conversations (neutral, cognitive, and arousal) were used. Neutral conversation did not pursue specific purpose questions. Cognitive conversations were simple mathematical problem-solving questions and arousal conversations aimed at arousing participant emotions. Each conversation was used as a secondary task in a condition. The study had three conditions; in each condition the participant drove for 15 min. Each condition consisted of 5 min of driving (Background), 5 min of driving and conversation (dual tasks) and 5 min of driving after conversation to trace the effects of the conversation. Vehicle speed was 110 km/h in each of the three conditions using car-following scenario. The results showed that neutral conversations had no significant effects on physiological response. Though, arousal conversations had significant effects on physiological responsiveness and driving performance during conversations, where it was even more significant after disconnection. Therefore, the content of the conversation determines the amount of cognitive load imposed on the driver. Considering the persistence of cognitive effects caused by conversation, the risk of traffic accidents is still high even after disconnection.

Analysis of Electric Bicycle Riders' Use of Mobile Phones While Riding on Campus

Based on the theory of rational action (TRA), overconfidence theory (OT), and deterrence theory (DT), this study explores the reasons for mobile phone use by Chinese students riding electronic bicycles (e-bikes) in Fuzhou City. We tested the reliability and validity of an extended TPB, OT and DT questionnaire (with 531 eligible responses) and constructed a structural equation model of mobile phone use behavior while riding e-bikes, based on the improved model. The structural equation model (SEM) is used to evaluate the relationship between the internal factors of mobile phone riding behavior. The results show that the correlation among mobile phone dependence, punishment mechanism, attitude, and controllable operation impacts e-bike riders’ behavior when using mobile phones while riding.

Differences in stress system (re-)activity between single and dual- or multitasking in healthy adults: A systematic review and meta-analysis

AbstractIn the age of digitization, multitasking requirements are ubiquitous, especially in the workplace. Multitasking (MT) describes the activity of performing multiple (at least two) tasks at the same time. Dual tasking (DT) refers to the sequential switching between two tasks. The aim of our systematic review and meta-analysis was first to investigate whether physiological stress systems become activated in response to or during MT/DT and, secondly, whether this (re-)activity is higher compared to single tasking. We focused on the Sympathetic Nervous System (SNS), the Parasympathetic Nervous System (PNS), the hypothalamic-pituitary adrenal (HPA) axis, and the immune system. The systematic review has been pre-registered with PROSPERO (CRD42020181415). A total of twenty-five articles were identified as eligible, in which n = 26 studies were reported, with N = 1,142 participants. Our main findings are that SNS activity is significantly higher and PNS activity is significantly lower during MT/DT than during single tasking. Only two studies were found, in which HPA axis (re-)activity was surveyed. No eligible study was identified in which immune system (re-)activity was investigated. This is the first systematic synthesis of the literature base showing that stress system activity is increased during MT/DT in comparison to single-tasking.

Take-Over Requests after Waking in Autonomous Vehicles

Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers’ most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered <5 s prior needed a visual accompaniment to quickly inform drivers of on-road situations. This study furthers understanding of how a driver’s cognitive and physical demands interact with TOR situations at the moment of waking from sleep and designs effective interventions for intelligibility services to best comply with safety and driver experience in AVs.

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study

Road traffic accidents (RTAs) are an ever-existing threat to all road users. Automated vehicles (AVs; SAE Level 3-5) are developed in many countries. They are promoted with numerous benefits such as increased safety yielding less RTAs, less congestion, less greenhouse gas emissions, and the possibility of enabling non-driving related tasks (NDRTs). However, there has been no study which has investigated different NDRT conditions, while comparing participants who experienced a severe RTA in the past with those who experienced no RTA. Therefore, we conducted a driving simulator study (N = 53) and compared two NDRT conditions (i.e., auditory-speech (ASD) vs. heads-up display (HUD)) and an accident (26 participants) with a non-accident group (27; between-subjects design). Although our results did not reveal any interaction effect, and no group difference between the accident and the non-accident group on NDRT, take-over request (TOR), and driving performance, we uncovered for both groups better performances for the HUD condition, whereas a lower cognitive workload was reported for the ASD condition. Nevertheless, there was no difference for technology trust between the two conditions. Albeit we observed higher self-ratings of PTSD symptoms for the accident than for the non-accident group, there were no group differences on depression and psychological resilience self-ratings. We conclude that severe RTA experiences do not undermine NDRT, TOR, and driving performance in a SAE Level 3 driving simulator study, although PTSD symptoms after an RTA may affect the psychological wellbeing.

Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance.

Predicting driver takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4 s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7 s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.

Counteracting effect of verbal ratings of sleepiness on dual task interference

The aim of the present study was to demonstrate the effect of verbal ratings on arousal in the electroencephalogram (EEG) and psychomotor vigilance test (PVT) performance. Thirty participants underwent the PVT for 40 min in three experimental conditions: (1) Rating condition, in which they verbally rated subjective sleepiness with Karolinska sleepiness scale, following pure tone sound played every 20 s during PVT, (2) No-rating condition, in which they underwent PVT with the similar sound as the Rating experiment but without the verbal rating task, and (3) Control condition, in which they underwent PVT with a no-sound stimulus and without the verbal rating task. The results show that during the first half of the task epoch, alpha power density was lower in the Rating than in the No-rating condition, while performance was not different between the conditions. During the second half of the task epoch, performance was better in the Non-rating than in the Rating condition, but no difference in the alpha power density. These results suggest that performance deterioration could be masked by the arousal effect of the dual task itself. It could also explain why the PVT performance and arousal in EEG sometimes dissociate, particularly in dual task situations.

Efficacy of a Neurofeedback Training on Attention and Driving Performance: Physiological and Behavioral Measures

Increased attention and lower stress levels are associated with more functional and safe driving behavior, since they contribute to reduce distractibility and risk-taking at the wheel. Previous neuroscience research highlighted that NeuroFeedback (NF) training mediated by wearable devices could be effective in terms of neurocognitive strengthening and attention regulation with a direct effect on driving attentional performance. Thus, this research aims to test the effectiveness of a NF protocol on a sample of drivers, to observe its impact on attentional skills and psychophysiological levels of stress involved in driving behavior. 50 participants were randomly assigned to the experimental and active control group. The experimental condition consisted of a 21-day mindfulness NF training with incremental duration sessions. A pre- (t0) and post-treatment (t1) assessment included behavioral, psychometric, neuropsychological, and psychophysiological autonomic measures. Specifically, the Driver Behavior Questionnaire (DBQ) and the Active Box (AB) device were used to evaluate the everyday driving behavior. Results underlined an improvement in driving behavior performance and a decrease of violations at the wheel of the experimental group (EXPg) at t1 measured, respectively by AB and DBQ. About the autonomic and neuropsychological measure, an increase in heart rate (HR) and an increased accuracy at the Stroop Task were detected: a specific increase of Stroop-related HR was found for the EXPg at t1. Also, reduced reaction times were found in the Multiple Features Target Cancellation for the EXPg at t1. Overall, the EXPg displayed a physiological, behavioral and neuropsychological increased efficiency related to attention as well as a driving-related behavioral improvement after NF training.

Effect of Using Mobile Phones on Driver's Control Behavior Based on Naturalistic Driving Data

Distracted driving behaviors are closely related to crash risk, with the use of mobile phones during driving being one of the leading causes of accidents. This paper attempts to investigate the impact of cell phone use while driving on drivers' control behaviors. Given the limitation of driving simulators in an unnatural setting, a sample of 134 cases related to cell phone use during driving were extracted from Shanghai naturalistic driving study data, which provided massive unobtrusive data to observe actual driving process. The process of using mobile phones was categorized into five operations, including dialing, answering, talking and listening, hanging up, and viewing information. Based on the concept of moving time window, the variation of the intensity of control activity, the sensitivity of control operation, and the stability of control state in each operation were analyzed. The empirical results show strong correlation between distracted operations and driving control behavior. The findings contribute to a better understanding of drivers' natural behavior changes with using mobiles, and can provide useful information for transport safety management.

Identifying the Causes of Drivers' Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements

Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.

Relationship Between Kinematic and Physiological Indices During Braking Events of Different Intensities

Objective To study the relationship between physiological indices and kinematic indices during braking events of different intensities. Background Based on mental workload theory, driving and other task demands may generate changes in physiological indices, such as the driver's heart rate and skin conductance. However, no attempts were made to associate changes in physiological indices with changes in vehicle kinematics that result from the driver attempts to meet task demands. Method Twenty-five drivers participated in a field experiment. We manipulated braking demands using roadside signs to communicate the speed (km/h) before braking (50 or 60) and the target speed for braking (30 or to a complete stop). In an additional session, we asked drivers to brake as if they were responding to an impending collision. We analyzed the relationship between the intensities of braking events as measured by deceleration values (g) and changes in heart rate, heart rate variability, and skin conductance. Results All physiological indices were associated with deceleration intensity. Especially salient were the differences in physiological indices between the intensive (|g| > 0.5) and nonintensive braking events. The strongest relationship was between braking intensity and skin conductance. Conclusions Skin conductance, heart rate, and heart rate variability can mirror the mental workload elicited by varying braking intensities. Application Associating vehicle kinematics with physiological indices related to short-term driving events may help improve the performance of driver assistance systems.

Cellphone Legislation and Self-Reported Behaviors Among Subgroups of Adolescent U.S. Drivers

PURPOSE

The relationship between cellphone use while driving legislation and self-reported adolescent driver behavior is poorly understood, especially across demographic subgroups. This study investigated the relationship between statewide cellphone legislation and cellphone use behaviors across adolescent driver subgroups, including age (16/17 vs. 18), sex, race/ethnicity (white non-Hispanic and others), and rurality (urban or rural).

METHODS

Data from the 2011-2014 Traffic Safety Culture Index Surveys were combined with state legislation. The outcomes were self-reported texting and handheld cellphone conversations. The exposure was the presence of a texting or handheld cellphone ban applicable to all drivers (i.e., universal) in the drivers' state of residence. A multilevel, modified Poisson regression model was used to estimate the risk of engaging in these behaviors.

RESULTS

Approximately 34% of respondents reported to have driven while conversing, and 37% texted and drove in the 30 days before the survey. Universal handheld calling bans were associated with lower occurrences of cellphone conversations across all groups except rural drivers. Overall, handheld cellphone bans were associated with 55% lower (adjusted risk ratio .45, 95% confidence interval .32-.63) occurrences of cellphone conversations. However, universal texting bans were not associated with fewer texting behaviors in any subgroup.

CONCLUSIONS

Universal handheld calling bans may discourage adolescents from engaging in handheld phone conversations, whereas universal texting bans may not fully discourage texting behaviors. More interventional or educational work is necessary, particularly addressing texting while driving.

Effect of distraction task on driving performance of experienced taxi drivers

Background

Driving performance is influenced by human, vehicular, and environmental factors.

Objectives

To investigate the effects of distraction tasks, such as sending a text message (STM) and searching a navigation device (SN), on the driving performance of experienced taxi drivers.

Methods

Twelve male taxi drivers (age: 56.3 ± 4.4 y; experience: 28.4 ± 6.4 y) and 14 female taxi drivers (age: 55.5 ± 3.5 y; experience: 19.4 ± 5.0 y) drove in a simulator at a constant speed (90 km/h) for 2 min while maintaining a gap of 30 m from the car in front, also traveling at 90 km/h. Participants were instructed to drive only for the first 1 min (control phase). For an additional 1 min (task phase), they were instructed to drive only, drive + STM, or drive + SN.

Results

Compared with driving only, during driving + STM or driving + SN, the drivers’ skin conductance level was relatively increased, suggesting that the distraction task increased the drivers’ workload and sympathetic nervous system activity. Compared with driving only, during driving + STM or driving + SN, the average distance from the car in front, speed deviation, and anterior–posterior and medial–lateral coefficients of variation increased, suggesting that maintaining the instructed gap and speed, and the longitudinal and transverse control of the car, was more difficult because of the distraction task.

Conclusions

Even for highly experienced taxi drivers, distraction tasks increased workload, increased the difficulty of vehicle control, and detracted from safe driving.

Does Talking on a Cell Phone, With a Passenger, or Dialing Affect Driving Performance? An Updated Systematic Review and Meta-Analysis of Experimental Studies

Objective An up-to-date meta-analysis of experimental research on talking and driving is needed to provide a comprehensive, empirical, and credible basis for policy, legislation, countermeasures, and future research. Background The effects of cell, mobile, and smart phone use on driving safety continues to be a contentious societal issue. Method All available studies that measured the effects of cell phone use on driving were identified through a variety of search methods and databases. A total of 93 studies containing 106 experiments met the inclusion criteria. Coded independent variables included conversation target (handheld, hands-free, and passenger), setting (laboratory, simulation, or on road), and conversation type (natural, cognitive task, and dialing). Coded dependent variables included reaction time, stimulus detection, lane positioning, speed, headway, eye movements, and collisions. Results The overall sample had 4,382 participants, with driver ages ranging from 14 to 84 years ( M = 25.5, SD = 5.2). Conversation on a handheld or hands-free phone resulted in performance costs when compared with baseline driving for reaction time, stimulus detection, and collisions. Passenger conversation had a similar pattern of effect sizes. Dialing while driving had large performance costs for many variables. Conclusion This meta-analysis found that cell phone and passenger conversation produced moderate performance costs. Drivers minimally compensated while conversing on a cell phone by increasing headway or reducing speed. A number of additional meta-analytic questions are discussed. Application The results can be used to guide legislation, policy, countermeasures, and future research.

Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals

An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).

Visuospatial Working Memory Capacity Predicts Physiological Arousal in a Narrative Task

Physiological arousal that occurs during narrative production is thought to reflect emotional processing and cognitive effort (Bar-Haim et al. in Dev Psychobiol 44:238-249, 2004). The purpose of this study was to determine whether individual differences in visuospatial working memory and/or verbal working memory capacity predict physiological arousal in a narrative task. Visuospatial working memory was a significant predictor of skin conductance level (SCL); verbal working memory was not. When visuospatial working memory interference was imposed, visuospatial working memory was no longer a significant predictor of SCL. Visuospatial interference also resulted in a significant reduction in SCL. Furthermore, listener ratings of narrative quality were contingent upon the visuospatial working memory resources of the narrator. Potential implications for educators and clinical practitioners are discussed.

Mental workload and driving

The aim of this review is to identify the most representative measures of subjective and objective mental workload in driving, and to understand how the subjective and objective levels of mental workload influence the performance as a function of situation complexity and driving experience, i.e., to verify whether the increase of situation complexity and the lack of experience increase the subjective and physiological levels of mental workload and lead to driving performance impairments. This review will be useful to both researchers designing an experimental study of mental workload and to designers of drivers’ training content. In the first part, we will broach the theoretical approach with two factors of mental workload and performance, i.e., situation complexity and driving experience. Indeed, a low complex situation (e.g., highways), or conversely a high complex situation (e.g., town) can provoke an overload. Additionally, performing the driving tasks implies producing a high effort for novice drivers who have not totally automated the driving activity. In the second part, we will focus on subjective measures of mental workload. A comparison of questionnaires usually used in driving will allow identifying the most appropriate ones as a function of different criteria. Moreover, we will review the empirical studies to verify if the subjective level of mental workload is high in simple and very complex situations, especially for novice drivers compared to the experienced ones. In the third part, we will focus on physiological measures. A comparison of physiological indicators will be realized in order to identify the most correlated to mental workload. An empirical review will also take the effect of situation complexity and experience on these physiological indicators into consideration. Finally, a more nuanced comparison between subjective and physiological measures will be established from the impact on situation complexity and experience.

Driving performance changes of middle-aged experienced taxi drivers due to distraction tasks during unexpected situations

This study investigated the effects of distraction taskssuch as sending a text message with a cellphone and searching navigation with car navigation system-on the driving performance of 29 highly experienced taxi drivers in their 50s. All participants were instructed to drive using a driving simulator for 2 min. while maintaining a constant distance from the vehicle in front and a constant speed. Participants drove without any distractions for the first minute. For an additional minute, they performed Driving Only or performed a task while driving (Driving + Sending Text Message or Driving + Searching Navigation). An unexpected situation, in which the participant had to stop abruptly due to a sudden stop of the preceding vehicle, occurred during this period. Driving performance during the unexpected situation was evaluated by car control variables, medial-lateral coefficient of variation and brake time, and by motion variables such as the jerk-cost function. Compared to Driving Only, jerk-cost function, medial-lateral coefficient of variation, and brake time increased during Driving + Sending Text Message or Driving + Searching Navigation.

Measuring workload with electrodermal activity during common braking actions

How to assess mental load remains a recurrent question. We aimed to explore whether slight differences in real-world driving task demands could be discriminated by electrodermal response (EDR). A sample of 33 participants was observed under five conditions: controlled braking from 50 to 30 km/h, 80 to 50 km/h, 50 to 0 km/h, 80 to 0 km/h, and a single unexpected emergency braking event from 80 to 0 km/h. The likelihood of EDR and, whenever present, its duration were both correlated with workload as represented by the deceleration demand. A higher base travel speed and the unexpected demand of the emergency braking situation impacted EDR, thus attesting higher workload level. EDR explains why stopping the vehicle from 50 km/h and slowing down from 80 to 50 km/h was of similar strain. The results further demonstrate that EDR measures can be successfully employed to discriminate multiple levels of workload.

PRACTITIONER SUMMARY

Common braking elicited different loads as revealed by electrodermal response (EDR) with sensitivity to deceleration of - 0.2 g. Even the slightest braking elicited a strain measurable with EDR. Accordingly, EDR may objectively assess the resulting strain during driving, with enhanced reliability if associated with other variables, e.g. cardiac activity.

Associations between driving performance and engaging in secondary tasks: a systematic review

We conducted a systematic review and meta-analysis of the literature examining the relationship between driving performance and engaging in secondary tasks. We extracted data from abstracts of 206 empirical articles published between 1968 and 2012 and developed a logistic regression model to identify correlates of a detrimental relationship between secondary tasks and driving performance. Of 350 analyses, 80% reported finding a detrimental relationship. Studies using experimental designs were 37% less likely to report a detrimental relationship (P = .014). Studies examining mobile phone use while driving were 16% more likely to find such a relationship (P = .009). Quasi-experiments can better determine the effects of secondary tasks on driving performance and consequently serve to inform policymakers interested in reducing distracted driving and increasing roadway safety.

Blocking-out auditory distracters while driving: A cognitive strategy to reduce task-demands on the road

The current research examined how drivers handle task-demands induced by listening to the radio while driving. In particular, we explored the traces of a possible cognitive strategy that might be used by drivers to cope with task-demands, namely blocking-out auditory distracters. In Study 1 (N=15), participants listened to a radio-broadcast while watching traffic videos on a screen. Based on a recall task asking about what they had listened to, we created baseline scores reflecting the general levels of blocking-out of radio-content when there was no concurrent driving task accompanying the radio-listening. In Study 2 (N=46), participants were asked to complete two drives in the simulator: one drive in high-complexity traffic and another in low-complexity traffic. About half of the participants listened to a radio-broadcast while driving, and the other half drove in silence. The radio-listeners were given the same recall task that we had used in Study 1. The results revealed that the participants who drove while listening to the radio (Study 2) recalled less material from the radio-broadcast as compared to the participants who did not drive (Study 1). In addition, the participants who drove while listening to the radio recalled less talk-radio excerpts when driving in high-complexity traffic than when driving in low-complexity traffic. Importantly, listening to the radio did not impair driving performance. Together, these findings indicate that blocking-out radio-content might indeed be a strategy used by drivers to maintain their driving performance.

Sensitivity of physiological measures for detecting systematic variations in cognitive demand from a working memory task: an on-road study across three age groups

OBJECTIVE

To assess the sensitivity of two physiological measures for discriminating between levels of cognitive demand under driving conditions across different age groups.

BACKGROUND

Previous driving research presents a mixed picture concerning the sensitivity of physiological measures for differentiating tasks with presumed differences in mental workload.

METHOD

A total of 108 relatively healthy drivers balanced by gender and across three age groups (20-29, 40-49, 60-69) engaged in three difficulty levels of an auditory presentation-verbal response working memory task.

RESULTS

Heart rate and skin conductance level (SCL) both increased in a statistically significant fashion with each incremental increase in cognitive demand, whereas driving performance measures did not provide incremental discrimination. SCL was lower in the 40s and 60s age groups; however, the pattern of incremental increase with higher demand was consistent for heart rate and SCL across all age groups. Although each measure was quite sensitive at the group level, considering both SCL and heart rate improved detection of periods of heightened cognitive demand at the individual level.

CONCLUSION

The data provide clear evidence that two basic physiological measures can be utilized under field conditions to differentiate multiple levels of objectively defined changes in cognitive demand. Methodological considerations, including task engagement, may account for some of the inconsistencies in previous research.

APPLICATION

These findings increase the confidence with which these measures may be applied to assess relative differences in mental workload when developing and optimizing human machine interface (HMI) designs and in exploring their potential role in advanced workload detection and augmented cognition systems.

A comparison of the effect of mobile phone use and alcohol consumption on driving simulation performance

OBJECTIVE

The present study compared the effects of a variety of mobile phone usage conditions to different levels of alcohol intoxication on simulated driving performance and psychomotor vigilance.

METHODS

Twelve healthy volunteers participated in a crossover design in which each participant completed a simulated driving task on 2 days, separated by a 1-week washout period. On the mobile phone day, participants performed the simulated driving task under each of 4 conditions: no phone usage, a hands-free naturalistic conversation, a hands-free cognitively demanding conversation, and texting. On the alcohol day, participants performed the simulated driving task at four different blood alcohol concentration (BAC) levels: 0.00, 0.04, 0.07, and 0.10. Driving performance was assessed by variables including time within target speed range, time spent speeding, braking reaction time, speed deviation, and lateral lane position deviation.

RESULTS

In the BAC 0.07 and 0.10 alcohol conditions, participants spent less time in the target speed range and more time speeding and took longer to brake in the BAC 0.04, 0.07, and 0.10 than in the BAC 0.00 condition. In the mobile phone condition, participants took longer to brake in the natural hands-free conversation, cognitively demanding hands-free conversation and texting conditions and spent less time in the target speed range and more time speeding in the cognitively demanding, hands-free conversation, and texting conditions. When comparing the 2 conditions, the naturalistic conversation was comparable to the legally permissible BAC level (0.04), and the cognitively demanding and texting conversations were similar to the BAC 0.07 to 0.10 results.

CONCLUSION

The findings of the current laboratory study suggest that very simple conversations on a mobile phone may not represent a significant driving risk (compared to legally permissible BAC levels), whereas cognitively demanding, hands-free conversation, and particularly texting represent significant risks to driving.

Lane-changing behavior on urban streets: a focus group-based study

As lane-changing behavior has received increasing attention during the recent years, various algorithms have been developed. However, most of these models were derived and validated using data such as vehicle trajectories, with no consideration of driver characteristics. In this research, focus group studies were conducted to obtain driver-related information so that the driver characteristics can be incorporated into lane-changing models. Different urban lane-changing scenarios were examined and discussed in the focus group meetings. The likelihood for initiating lane changes under each scenario was obtained. The participating drivers were categorized according to their background information and verbal responses, so that the lane-changing behavior can be related to driver characteristics for each group. Two types of information, quantitative and qualitative responses from participants, were used to establish this relationship. The paper concludes by providing recommendations related to the implementation of study findings into micro-simulators to better replicate driver behavior in urban street networks.

Sensitivity of the lane change test as a measure of in-vehicle system demand

The Lane Change Test (LCT) is one of the growing number of methods developed to quantify driving performance degradation brought about by the use of in-vehicle devices. Beyond its validity and reliability, for such a test to be of practical use, it must also be sensitive to the varied demands of individual tasks. The current study evaluated the ability of several recent LCT lateral control and event detection parameters to discriminate between visual-manual and cognitive surrogate In-Vehicle Information System tasks with different levels of demand. Twenty-seven participants (mean age 24.4 years) completed a PC version of the LCT while performing visual search and math problem solving tasks. A number of the lateral control metrics were found to be sensitive to task differences, but the event detection metrics were less able to discriminate between tasks. The mean deviation and lane excursion measures were able to distinguish between the visual and cognitive tasks, but were less sensitive to the different levels of task demand. The other LCT metrics examined were less sensitive to task differences. A major factor influencing the sensitivity of at least some of the LCT metrics could be the type of lane change instructions given to participants. The provision of clear and explicit lane change instructions and further refinement of its metrics will be essential for increasing the utility of the LCT as an evaluation tool.

Physiological Reactivity to Graded Levels of Cognitive Workload across Three Age Groups: An On-Road Evaluation

This study examined the sensitivity of two physiological measures to the demand of structured cognitive tasks representing low, moderate and relatively high levels of secondary cognitive workload during actual highway driving. In a sample of 108 relatively healthy drivers, balanced by gender, and drawn from three age groups (20–29, 40–49, and 60–69), both heart rate and skin conductance were found to increase in a statistically significant and relatively linear fashion across 4 levels of workload. Issues associated with the study of sensitivity, task engagement and age are considered.

Phoning while driving II: a review of driving conditions influence

The first paper examined how the variables related to driving performance were impacted by the management of holding a phone conversation. However, the conditions under which this dual task is carried out are dependent upon a set of factors that may particularly influence the risk of crash. These conditions are defined by several independent variables, classified into five main categories: i) legislation; ii) phone type (hands-free or hand-held); iii) drivers' features regarding age, gender, personal individual profile and driving experience; iv) conversation content (casual or professional) and its context (held with passengers or with a cell (mobile) phone); v) driving conditions (actual or simulated driving, road type, traffic density and weather). These independent variables determine the general conditions. The way in which these factors are combined and interact one with another thus determines the risk that drivers undergo when a cell phone is used while driving. Finally, this review defined the general conditions of driving for which managing a phone conversation is likely to elicit a high risk of car crash or, conversely, may provide a situation of lower risk, with sufficient acceptance to ensure safety.

Phoning while driving I: a review of epidemiological, psychological, behavioural and physiological studies

The impact of cell (mobile) phone use on driving performance has been widely questioned for 20 years. This paper reviews the literature to evaluate the extent to which phoning may impact behaviour with a risk to affect safety. After analysing epidemiological studies that give an overview of cell phone use, this paper examines the experimental results and focuses on variables showing that driving is impacted by holding a mobile-phone conversation. Information processing (e.g. reaction time and detection rate of cues related to driving information) and variables associated with vehicle control (e.g. lane-keeping, headway and vehicle speed) seem the most relevant. Although less studied than behavioural indices, physiological data give information about the supplementary potential strain that the driver may undergo under dual-task conditions. This first part of the review highlights common findings, questionable results and differences among studies, which originate from specific experimental designs with particular dependent variables, i.e. self-report, behavioural and physiological indicators. Finally, how drivers try to compensate for the additional load brought by phone use is described. STATEMENT OF RELEVANCE: The two papers review the influence of mobile-phone use on driving performance. While there is ample evidence that this dual task is likely to increase the risk of car crash, the review analyses the variables eliciting detrimental conditions and, conversely, those that may preserve acceptable conditions for safety, close to usual driving. The decision of answering or initiating a cell phone call while driving depends upon the complex interaction among several variables, including driving conditions and driver's own characteristics. In addition, this decision remains under driver's awareness of being able or not to manage the two tasks simultaneously.