Effects of Partial Sleep Deprivation on Braking Response of Drivers in Hazard Scenarios

Can stimulants make you smarter, despite stealing your sleep?

Nonmedical use of psychostimulants for cognitive enhancement is widespread and growing in neurotypical individuals, despite mixed scientific evidence of their effectiveness. Sleep benefits cognition, yet the interaction between stimulants, sleep, and cognition in neurotypical adults has received little attention. We propose that one effect of psychostimulants, namely decreased sleep, may play an important and unconsidered role in the effect of stimulants on cognition. We discuss the role of sleep in cognition, the alerting effects of stimulants in the context of sleep loss, and the conflicting findings of stimulants for complex cognitive processes. Finally, we hypothesize that sleep may be one unconsidered factor in the mythology of stimulants as cognitive enhancers and propose a methodological approach to systematically assess this relation.

Effects of sleep deprivation and hazard types on the hazard perception of young novice drivers: An ERP study

OBJECTIVE

The present study aimed to explore the effects of sleep deprivation on young novice drivers' cognitive neural processing of different hazard types.

METHOD

A 2 (sleep deprivation group, control group) × 3 (no hazard, covert hazard, overt hazard) mixed experimental design was used. Twenty-eight young drivers were sleep-deprived (no sleep within the past 24 h), while 28 drivers were in the control group (maintaining a normal schedule throughout the week). Eighty pictures containing a covert hazard (20 pictures), overt hazard (20 pictures) and no hazard (40 pictures) were presented. Participants were asked to press the keyboard quickly if they detected a hazard situation. The reaction time, accuracy, and changes in the N1 (100-150 ms) and N2 (250-350 ms) components of event-related potentials (ERP) measured using electroencephalography (EEG) were obtained.

RESULTS

Compared to the control group, the response accuracy of sleep-deprived drivers was higher in the cover-hazard situation and their N1 latency was longer in the no-hazard situation. Compared to the no-hazard and overt-hazard situations, the participants' reaction times and N2 amplitudes were significantly greater, and the response accuracy was significantly lower in the covert-hazard situation.

CONCLUSION

Hazard perception is compromised when drivers are sleep-deprived, especially when they are confronted with covert hazard situations. The findings help understand the negative effects of sleep deprivation in the early stage of young novice drivers' hazard perception.

Effects of Sleep Deprivation and Hazard Types on the Visual Search Patterns and Hazard Response Times of Taxi Drivers

The present study attempted to explore the effects of sleep deprivation on the visual search patterns and hazard response times of taxi drivers when they encountered different types of hazards. A two (driver groups: sleep deprivation or control) × two (hazard types: covert hazard or overt hazard) mixed experimental design was employed. A total of 60 drivers were recruited, half of whom were in the sleep-deprived group and half of whom were in the control group. A validated video-based hazard perception test that either contained covert hazards (12 video clips) or overt hazards (12 video clips) filmed from the drivers' perspective was presented to participants. Participants were instructed to click the left mouse button quickly once they detected a potentially dangerous situation that could lead to an accident. Participants' response time and eye movements relative to the hazards were recorded. The sleep-deprived group had a significantly longer response time and took a longer time to first fixate on covert hazards than the control group, while they had a shorter response time to overt hazards than the control group. The first fixation duration of sleep-deprived drivers was longer than that of the control group for overt hazards, while the duration of the first fixation of the two driver groups was similar for covert hazards. Sleep deprivation affects the visual search patterns and response times to hazards, and the adverse effects of sleep deprivation were worse in relation to covert hazards. The findings have some implications for classifying and evaluating high-risk taxi drivers whose hazard perception ability might be affected by insufficient sleep.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers' collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers' collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers' TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

The Effect of Partial Sleep Deprivation and Time-on-Task on Young Drivers' Subjective and Objective Sleepiness

Despite sleepiness being considered one of the main factors contributing to road crashes, and even though extensive efforts have been made in the identification of techniques able to detect it, the assessment of fitness-to-drive regarding driving fatigue and sleepiness is still an open issue. In the literature on driver sleepiness, both vehicle-based measures and behavioral measures are used. Concerning the former, the one considered more reliable is the Standard Deviation of Lateral Position (SDLP) while the PERcent of eye CLOSure over a defined period of time (PERCLOS) seems to be the most informative behavioral measure. In the present study, using a within-subject design, we assessed the effect of a single night of partial sleep deprivation (PSD, less than 5 h sleeping time) compared to a control condition (full night of sleep, 8 h sleeping time) on SDLP and PERCLOS, in young adults driving in a dynamic car simulator. Results show that time-on-task and PSD affect both subjective and objective sleepiness measures. Moreover, our data confirm that both objective and subjective sleepiness increase through a monotonous driving scenario. Considering that SDLP and PERCLOS were often used separately in studies on driver sleepiness and fatigue detection, the present results have potential implications for fitness-to-drive assessment in that they provide useful information allowing to combine the advantages of the two measures for drowsiness detection while driving.

Combined Exposure to Chronic Sleep Deprivation and Caffeine Potentiates Behavioural Deficits by Altering Neurochemical Profile and Synaptophysin Expression in Long-Evans Rats

Using the Unpredictable Chronic Sleep Deprivation (UCSD) paradigm we developed, the combined effects of chronic sleep deprivation and high caffeine intake on prefrontal cortical synaptophysin expression, neurochemical profiles, and behavioural outcomes in Long-Evans rats were evaluated. The combination of chronic sleep deprivation and high-dose caffeine treatment produced varying degrees of behavioural impairments, depletion of antioxidants, serotonin, and an upregulation of acetylcholinesterase (AChE) activity in the prefrontal cortex. An immunohistochemical assessment revealed a reduction in synaptophysin protein expression in the prefrontal cortex following exposure to high-dose caffeine and chronic sleep deprivation. Overall, our findings support the advocacy for adequate sleep for optimal mental performance as a high intake of caffeine to attenuate the effects of sleep deprivation that may alter the neurochemical profile and synaptic plasticity in the prefrontal cortex, significantly increasing the risk of neuropsychiatric/degenerative disorders.

Sleep restriction impairs visually and memory-guided force control

Sleep loss is a common phenomenon with consequences to physical and mental health. While the effects of sleep restriction on working memory are well documented, it is unknown how sleep restriction affects continuous force control. The purpose of this study was to determine the effects of sleep restriction on visually and memory-guided force production magnitude and variability. We hypothesized that both visually and memory-guided force production would be impaired after sleep restriction. Fourteen men participated in an eleven-day inpatient sleep study and completed a grip force task after two nights of ten hours’ time in bed (baseline); four nights of five hours’ time in bed (sleep restriction); and one night of ten hours’ time in bed (recovery). The force task entailed four 20-second trials of isometric force production with the thumb and index finger targeting 25% of the participant’s maximum voluntary contraction. During visually guided trials, participants had continuous visual feedback of their force production. During memory-guided trials, visual feedback was removed for the last 12 seconds of each trial. During both conditions, participants were told to maintain the target force production. After sleep restriction, participants decreased the magnitude of visually guided, but not memory-guided, force production, suggesting that visual attention tasks are more affected by sleep loss than memory-guided tasks. Participants who reported feeling more alert after sleep restriction and recovery sleep produced higher force during memory-guided, but not visually guided, force production, suggesting that the perception of decreased alertness may lead to more attention to the task during memory-guided visual tasks.

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving

Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment.

Keeping the driver in the loop through semi-automated or manual lane changes in conditionally automated driving

In the current study we investigated if drivers of conditionally automated vehicles can be kept in the loop through lane change maneuvers. More specifically, we examined whether involving drivers in lane-changes during a conditionally automated ride can influence critical take-over behavior and keep drivers' gaze on the road. In a repeated measures driving simulator study (n = 85), drivers drove the same route three times, each trial containing four lane changes that were all either (1) automated, (2) semi-automated or (3) manual. Each ride ended with a critical take-over situation that could be solved by braking and/or steering. Critical take-over reactions were analyzed with a linear mixed model and parametric accelerated failure time survival analysis. As expected, semi-automated and manual lane changes throughout the ride led to 13.5% and 17.0% faster maximum deceleration compared to automated lane changes. Additionally, semi-automated and manual lane changes improved the quality of the take-over by significantly decreasing standard deviation of the steering wheel angle. Unexpectedly, drivers in the semi-automated condition were slowest to start the braking maneuver. This may have been caused by the drivers' confusion as to how the semi-automated system would react. Additionally, the percentage gaze off-the-road was significantly decreased by the semi-automated (6.0%) and manual (6.6%) lane changes. Taken together, the results suggest that semi-automated and manual transitions may be an alarm-free instrument which developers could use to help maintain drivers' perception-action loop and improve automated driving safety.

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.

Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models

Braking performance of drivers is a crucial factor in evaluating the collision patterns and implementing road safety measures. Further, alcohol is known to impair driving control. The present study aims to examine the influence of a comprehensive range of Blood Alcohol Concentration (BAC) levels (0%, 0.03 %, 0.05 % and 0.08 %) on brake transition times of drivers. As young drivers show significantly higher crash risks compared to the experienced drivers, fifty-four young Indian drivers in the age group of 21-25 years (forty males and fourteen females) participated in the driving simulator experiments. The study adopted the framework of a within-subjects design, where each driver encountered rural and urban driving scenarios in a counterbalanced order, during experimental driving at each of the four BAC levels. Their brake transition times were estimated with respect to sudden pedestrian crossing events. Weibull Accelerated Failure Time (AFT) models with shared frailty were developed for quantifying the effects of BAC levels along with driver attributes on brake transition time. Preliminary analysis showed significant main effects of BAC (p < 0.001) and driving environment (p = 0.002) on brake transition time; however, their interaction effect was not significant (p = 0.485). The models revealed that 0.03 %, 0.05 % and 0.08 % BACs significantly reduced the brake transition times by 16 %, 28 % and 52 % in rural driving environment, and by 23 %, 37 % and 53 % in urban driving environment, compared to 0% BAC. The study outcomes may find application in assisting collision warning systems which take into account the braking behaviour of drivers.

Sleep-deprived car-following: Indicators of rear-end crash potential

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior

A large percentage of traffic accidents are due to human errors. Driving behavior and driving stress influence the probability of making these mistakes. Both are influenced by multiple factors, among which might be elements such as age, gender, sleeping hours, or working hours. The objective of this paper is to study, in a real scenario and without forcing the driver’s state, the relationship between driving behavior, driving stress, and these elements. Furthermore, we aim to provide guidelines to improve driving assistants. In this study, we used 1050 driving samples obtained from 35 volunteers. The driving samples correspond to regular commutes from home to the workplace. ANOVA and ANCOVA tests were carried out to check if there are significant differences in the four factors analyzed. Although the results show that driving behavior and driving stress are affected by gender, age, and sleeping hours, the most critical variable is working hours. Drivers with long working days suffer significantly more driving stress compared to other drivers, with the corresponding effect on their driving style. These drivers were the worst at maintaining the safety distance.

Exploring the effectiveness of a digital voice assistant to maintain driver alertness in partially automated vehicles

OBJECTIVE

Vehicle automation shifts the driver's role from active operator to passive observer at the potential cost of degrading their alertness. This study investigated the role of an in-vehicle voice-based assistant (VA; conversing about traffic/road environment) to counter the disengaging and fatiguing effects of automation.

METHOD

Twenty-four participants undertook two drives- with and without VA in a partially automated vehicle. Participants were subsequently categorized into high and low participation groups (based on their proportion of vocal exchanges with VA). The effectiveness of VA was assessed based on driver alertness measured using Karolinska Sleepiness Scale (KSS), eye-based sleepiness indicators and glance behavior, NASA-TLX workload rating and time to gain motor readiness in response to take-over request and performance rating made by the drivers.

RESULTS

Paired samples t-tests comparison of alertness measures across the two drives were conducted. Lower KSS rating, larger pupil diameter, higher glances (rear-mirror, roadside vehicles and signals in the drive with VA) and higher feedback ratings of VA indicated the efficiency of VA in improving driver alertness during automation. However, there was no significant difference in alertness or glance behavior between the driver groups (high and low-PR), although the time to resume steering control was significantly lower in the higher engagement group.

CONCLUSION

The study successfully demonstrated the advantages of using a voice assistant (VA) to counter these effects of passive fatigue, for example, by reducing the time to gain motor-readiness following a TOR. The findings show that despite the low engagement in spoken conversation, active listening also positively influenced driver alertness and awareness during the drive in an automated vehicle.

Gender differences in driving control of young alcohol-impaired drivers

BACKGROUND

Male and female drivers exhibit different degrees of vehicle control while driving under the influence of alcohol. However, this interaction between alcohol and gender is understudied. The present study examined the effects of different alcohol levels on the driving control of male and female drivers with the help of driving simulator experiments in heterogeneous traffic conditions.

METHOD

Forty young drivers (20 males and 20 females) completed simulated driving at four Blood Alcohol Concentration (BAC) levels: 0% (control), 0.03%, 0.05% and 0.08%. Driving impairment in vehicle control was measured in terms of average speed, acceleration variability and reaction time of drivers. Repeated-measures ANOVA tests were conducted and regression models were developed for male and female drivers to quantify the effects of BAC levels and driver characteristics on the driving control measures.

RESULTS

Significant effects of gender were observed for average speed (p <  0.001) and acceleration variability (p = 0.015) but not for reaction time of drivers (p =  0.891). Further, the effect of BAC was significant in all the three measures of vehicle control (p <  0.001). Driving control improved with increasing age of male drivers while caffeine consumption was observed as an alcohol-antagonizing factor in female drivers.

CONCLUSION

The findings suggest that vehicle control of female drivers is more likely to get affected even at low BAC levels, providing evidence that they belong to critical section of driving community in terms of alcohol-related impairment. The findings may help in discouraging drinking and driving among male and female drivers.