Efficient driver drowsiness detection at moderate levels of drowsiness

Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.     

Driver risk factors for sleep-related crashes

A population-based case-control study was carried out to examine driver risk factors for sleep-related motor vehicle crashes. Cases included 312 drivers involved in recent North Carolina crashes and identified on police reports as asleep at the time of the crash and 155 drivers identified as fatigued. Controls were 529 drivers also involved in recent crashes but not identified as asleep or fatigued, and 407 drivers not involved in recent crashes. All drivers were contacted for brief telephone interviews. Results showed that drivers in sleep-related crashes were more likely to work multiple jobs, night shifts, or other unusual work schedules. They averaged fewer hours sleep per night, reported poorer quality sleep, were less likely to feel they got enough sleep, were sleepier during the day, drove more often late at night, and had more prior instances of drowsy driving. Compared to drivers in non-sleep-related crashes, they had been driving for longer times, been awake more hours, slept fewer hours the night before, and were more likely to have used soporific medications. Knowledge of specific risk factors for sleep-related crashes is an important first step in reducing the thousands of deaths and injuries each year in the US attributed to drowsy driving.     

Driver state examination—Treading new paths

A large proportion of crashes in road driving can be attributed to driver fatigue. Several types of fatigue are discussed, comprising sleep-related fatigue, active task-related fatigue (as a consequence of workload in demanding driving situations) as well as passive task-related fatigue (as related to monotonous driving situations). The present study investigated actual states of fatigue in a monotonous driving situation, using EEG measures and a long-lasting driving simulation experiment, in which drivers had to keep the vehicle on track by compensating crosswind of different strength. Performance data and electrophysiological correlates of mental fatigue (EEG Alpha and Theta power, Inter Trial Coherence (ITC), and auditory event-related potentials to short sound stimuli) were analyzed. Driving errors and driving lane variability increased with time on task and with increasing crosswind. The posterior Alpha and Theta power also increased with time on task, but decreased with stronger crosswind. The P3a to sound stimuli decreased with time on task when the crosswind was weak, but remained stable when the crosswind was strong. The analysis of ITC revealed less frontal Alpha and Theta band synchronization with time on task, but no effect of crosswind. The results suggest that Alpha power in monotonous driving situations reflects boredom or attentional withdrawal due to monotony rather than the decline of processing abilities as a consequence of high mental effort. A more valid indicator of declining mental resources with increasing time on task seems to be provided by brain oscillatory synchronization measures and event-related activity.     

Cognitive components of simulated driving performance: Sleep loss effects and predictors

Driving is a complex task, which can be broken down into specific cognitive processes. In order to determine which components contribute to drowsy driving impairments, the current study examined simulated driving and neurocognitive performance after one night of sleep deprivation. Nineteen professional drivers (age 45.3 ± 9.1) underwent two experimental sessions in randomised order: one after normal sleep and one after 27 h total sleep deprivation. A simulated driving task (AusEd), the psychomotor vigilance test (PVT), and neurocognitive tasks selected from the Cognitive Drug Research computerised neurocognitive assessment battery (simple and choice RT, Stroop Task, Digit Symbol Substitution Task, and Digit Vigilance Task) were administered at 10:00 h in both sessions. Mixed-effects ANOVAs were performed to examine the effect of sleep deprivation versus normal sleep on performance measures. To determine if any neurocognitive tests predicted driving performance (lane position variability, speed variability, braking RT), neurocognitive measures that were significantly affected by sleep deprivation were then added as a covariate to the ANOVAs for driving performance. Simulated driving performance and neurocognitive measures of vigilance and reaction time were impaired after sleep deprivation (p < 0.05), whereas tasks examining processing speed and executive functioning were not significantly affected by sleep loss. PVT performance significantly predicted specific aspects of simulated driving performance. Thus, psychomotor vigilance impairment may be a key cognitive component of driving impairment when sleep deprived. The generalisability of this finding to real-world driving remains to be investigated.     

Naturalistic field study of the restart break in US commercial motor vehicle drivers: Truck driving,sleep, and fatigue

Commercial motor vehicle (CMV) drivers in the US may start a new duty cycle after taking a 34-h restart break. A restart break provides an opportunity for sleep recuperation to help prevent the build-up of fatigue across duty cycles. However, the effectiveness of a restart break may depend on its timing, and on how many nighttime opportunities for sleep it contains. For daytime drivers, a 34-h restart break automatically includes two nighttime periods. For nighttime drivers, who are arguably at increased risk of fatigue, a 34-h restart break contains only one nighttime period. To what extent this is relevant for fatigue depends in part on whether nighttime drivers revert back to a nighttime-oriented sleep schedule during the restart break. We conducted a naturalistic field study with 106 CMV drivers working their normal schedules and performing their normal duties. These drivers were studied during two duty cycles and during the intervening restart break. They provided a total of 1260 days of data and drove a total of 414,937 miles during the study. Their duty logs were used to identify the periods when they were on duty and when they were driving and to determine their duty cycles and restart breaks. Sleep/wake patterns were measured continuously by means of wrist actigraphy. Fatigue was assessed three times per day by means of a brief psychomotor vigilance test (PVT-B) and a subjective sleepiness scale. Data from a truck-based lane tracking and data acquisition system were used to compute lane deviation (variability in lateral lane position). Statistical analyses focused on 24-h patterns of duty, driving, sleep, PVT-B performance, subjective sleepiness, and lane deviation. Duty cycles preceded by a restart break containing only one nighttime period (defined as 01:00–05:00) were compared with duty cycles preceded by a restart break containing more than one nighttime period. During duty cycles preceded by a restart break with only one nighttime period, drivers showed more nighttime-oriented duty and driving patterns and more daytime-oriented sleep patterns than during duty cycles preceded by a restart break with more than one nighttime period. During duty cycles preceded by a restart break with only one nighttime period, drivers also experienced more lapses of attention on the PVT-B and increased lane deviation at night, and they reported greater subjective sleepiness. Importantly, drivers exhibited a predominantly nighttime-oriented sleep schedule during the restart break, regardless of whether the restart break contained only one or more than one nighttime period. Consistent with findings in laboratory-based studies of the restart break, the results of this naturalistic field study indicate that having at least two nighttime periods in the restart break provides greater opportunity for sleep recuperation and helps to mitigate fatigue.     

Modeling lateral control in driving studies

In driving studies based on simulators and instrumented vehicles, specific models are needed to capture key aspects of driving data such as lateral control. We propose a model that uses weighted polynomial projections to predict each data point from the previous three time points, and accommodates the attempts of the drivers to re-center the vehicle before crossing the borders of the traffic lane. Our model also allows the possibility that average position within the lane may vary from driver to driver. We demonstrate how to fit the model using standard statistical procedures available in software packages such as SAS. We used a fixed-base driving simulator to obtain data from 67 drivers with Alzheimer's disease and 128 elderly drivers without dementia. Using these data, we estimated the subject-specific parameters of our model, and we compared the two groups with respect to these parameters. We found that the parameters based on our model were able to distinguish between the groups in an interpretable manner. Hence, this model may be a useful tool to define outcome measures for observational and interventional driving studies.     

In the eye of the beholder: A simulator study of the impact of Google Glass on driving performance

This study examined whether, and to what extent, driving is affected by reading text on Google Glass. Reading text requires a high level of visual resources and can interfere with safe driving. However, it is currently unclear if the impact of reading text on a head-mounted display, such as Google Glass (Glass), will differ from that found with more traditional head-down electronic devices, such as a dash-mounted smartphone. A total of 20 drivers (22–48 years) completed the Lane Change Test while driving undistracted and while reading text on Glass and on a smartphone. Measures of lateral vehicle control and event detection were examined along with subjective workload and secondary task performance. Results revealed that drivers’ lane keeping ability was significantly impaired by reading text on both Glass and the smartphone. When using Glass, drivers also failed to detect a greater number of lane change signs compared to when using the phone or driving undistracted. In terms of subjective workload, drivers rated reading on Glass as subjectively easier than on the smartphone, which may possibly encourage greater use of this device while driving. Overall, the results suggest that, despite Glass allowing drivers to better maintain their visual attention on the forward scene, drivers are still not able to effectively divide their cognitive attention across the Glass display and the road environment, resulting in impaired driving performance.     

重型长途货运汽车夜间驾驶疲劳测试研究

为了预防重型长途货运汽车驾驶员夜间的驾驶疲劳,在京珠高速公路进行实车试验,采集驾驶员的历时心率数据,并以心率增加率和疲劳综合评价值为指标分析和评价驾驶疲劳情况。结果表明：驾驶仓栅式载货车属于轻级劳动强度,疲劳综合评价值为1.300;而驾驶半挂车属于中级劳动强度,疲劳综合评价值为1.624~1.929。进一步通过安全阈值分析得出,重型货车驾驶员每次连续驾驶的时间不宜超过3.0 h。研究结果为夜间重型货车驾驶员合理安排驾驶时间,有效预防驾驶疲劳提供了参考依据。     

Age and inconsistency in driving performance

Research in cognitive neuropsychology suggests that investigation of the within-person variability, or inconsistency, of cognitive performance may provide valuable insights into ageing mental processes. It is rare though, for this interest in intraindividual variability to extend to everyday activities. As this may provide important information about driving behaviour, we therefore assessed age differences in driving inconsistency in younger (n = 24, M age = 21.29 years) and older (n = 21, M age = 71.24 years) persons who drove in residential, urban and motorway conditions in a fully immersive driving simulator. In measures of headway (maintaining a safe distance to a preceding vehicle) and lateral lane position, older drivers exhibited significantly greater performance inconsistency, and this was particularly marked in the faster motorway condition. Older drivers also recorded greater perceived mental demands associated with driving, and greater within-person variability across a range of cognitive measures. The findings suggest that age-related deficits in attentional and executive control may affect the consistency of driving performance in older persons. Discussion considers interventions to introduce in-vehicle systems to help maintain attention in older drivers, and to intervene when safety-critical boundaries are exceeded.     

Evaluation of experience-based fatigue countermeasures

Background and objective

Strategies adopted by drivers in order to cope with fatigue and falling asleep at the wheel include a variety of activities that may invigorate the body and/or the mind. The objectives of the current study were to examine the effectiveness of an energy drink and a non-traditional manual-dexterity/mastication activity as fatigue countermeasures.

Method

Twenty subjects participated in this driving simulator study. Each driving session lasted 2 h and each driver drove under three conditions: after consumption of an energy drink, while engaged in a self-paced manual-dexterity/mastication secondary task (shelling and eating sunflower seeds), and in a control condition with neither. Fatigue effects were assessed on three dimensions: subjective evaluations (using the Swedish Occupational Fatigue Inventory), a physiological indicator (heart rate variability), and driving performance measures (speed, steering, and lane deviations).

Results

The subjective and physiological measures showed a significant effect of both treatments in counteracting the effects of fatigue when compared to the control condition. The results of the driving performance measures indicated that the energy drink was effective in counteracting fatigue, while the secondary task was as effective as the energy drink in counteracting fatigue on measures that did not rely on hand movements.

Conclusions

Drinking an energy drink prior to the driving task has a significant, positive effect in counteracting fatigue, though it may have long-term negative rebound effects. The manual-dexterity/mastication secondary task can temporarily counteract the subjective and physiological effects of fatigue while driving, but can interfere with vehicle handling.     

The influence of daily sleep patterns of commercial truck drivers on driving performance

Fatigued and drowsy driving has been found to be a major cause of truck crashes. Lack of sleep is the number one cause of fatigue and drowsiness. However, there are limited data on the sleep patterns (sleep duration, sleep percentage in the duration of non-work period, and the time when sleep occurred) of truck drivers in non-work periods and the impact on driving performance. This paper examined sleep patterns of 96 commercial truck drivers during non-work periods and evaluated the influence these sleep patterns had on truck driving performance. Data were from the Naturalistic Truck Driving Study. Each driver participated in the study for approximately four weeks. A shift was defined as a non-work period followed by a work period. A total of 1397 shifts were identified. Four distinct sleep patterns were identified based on sleep duration, sleep start/end point in a non-work period, and the percentage of sleep with reference to the duration of non-work period. Driving performance was measured by safety-critical events, which included crashes, near-crashes, crash-relevant conflicts, and unintentional lane deviations. Negative binomial regression was used to evaluate the association between the sleep patterns and driving performance, adjusted for driver demographic information. The results showed that the sleep pattern with the highest safety-critical event rate was associated with shorter sleep, sleep in the early stage of a non-work period, and less sleep between 1 a.m. and 5 a.m. This study also found that male drivers, with fewer years of commercial vehicle driving experience and higher body mass index, were associated with deteriorated driving performance and increased driving risk. The results of this study could inform hours-of-service policy-making and benefit safety management in the trucking industry.     

经验因素与车道类型对汽车驾驶员变道决策的影响

目的考察驾驶经验与车道类型对驾驶员变道决策的影响以及不同经验驾驶员的眼动注意搜索模式。方法使用Tobii眼动仪和驾驶场景视频收集了35名驾驶员的变道决策反应时、变道频率及眼动数据。结果驾驶经验与车道类型对驾驶员变道决策反应时交互作用显著;驾驶经验、车道类型与兴趣区对驾驶员总注视时间交互作用显著,同时驾驶员前方视野总注视时间对其边侧车道变道决策反应时有负向预测作用。结论中间车道条件下,实习驾驶员变道决策反应时更慢;边侧车道条件下结论相反;中间车道条件下,实习驾驶员对于侧视镜的总注视时间更长;边侧车道条件下,实习驾驶员对于前方视野的总注视时间更长;边侧车道条件下,实习驾驶员对于前方视野注视时间越长,变道决策反应时越快,这可以被视为实习驾驶员变道决策反应时更快的主要视觉注意机制。本研究对驾驶员改善变道行为和安全驾驶具有重要意义。     

Short and long sleep duration and risk of drowsy driving and the role of subjective sleep insufficiency

Experimental sleep restriction increases sleepiness and impairs driving performance. However, it is unclear whether short sleep duration in the general population is associated with drowsy driving. The goal of the present study was to evaluate whether individuals in the general population who obtained sleep of 6 h or less are more likely to report drowsy driving, and evaluate the role of perceived sleep sufficiency. Data exploring whether subgroups of short sleepers (those who report the most or least unmet sleep need) show different risk profiles for drowsy driving are limited. From the 2009 Behavioral Risk Factor Surveillance System (N = 31,522), we obtained the following self-reported data: (1) sleep duration (≤5, 6, 7, 8, 9, or ≥10 h/night); (2) number of days/week of perceived insufficient sleep; (3) among drivers, yes/no response to: “During the past 30 days, have you ever nodded off or fallen asleep, even just for a brief moment, while driving?” (4) demographics, physical/mental health. Using 7 h/night as reference, logistic regression analyses evaluated whether self-reported sleep duration was associated with drowsy driving. Overall, 3.6% reported drowsy driving. Self-identified short-sleepers reported drowsy driving more often, and long sleepers, less often. Among those who perceived sleep as always insufficient, drowsy driving was reported more often when sleep duration was ≤5 h, 6 h, or ≥10 h. Among those who perceived sleep as always sufficient, drowsy driving was reported more often among ≤5 h and 6 h sleepers. Overall, drowsy driving was common, particularly in self-identified short-sleepers as a whole, as well as subgroups based on sleep insufficiency.     

Speed choice and driving performance in simulated foggy conditions

Driving in fog is a potentially dangerous activity that has been investigated in a number of different ways; however, most have focused on identifying the underlying perceptual changes that result in an inability to perceive speed of vehicle motion. Although the previous research has identified the perceptual changes associated with driving in fog and shows that people are highly likely to perceive their speed to be higher than it actually is, these research studies have not investigated driving behavior when drivers are allowed to maintain speed as they feel appropriate and make use of the vehicle's speedometer. In addition, much of the existing research focuses on speed perception and presents a limited view of other driving performance metrics in terms of lane keeping and event detection. The current study addresses these issues utilizing a driving simulator-based method where fog is simulated as a distance dependent contrast reduction while having participants drive at speeds they feel are appropriate. A number of different instructions and speed feedback mechanisms were tested in order to determine how drivers react when driving in varying levels of fog. Results also include lane keeping measures in order to assess whether drivers are willing to drive at speeds where their lane keeping performance is degraded due to the reduced visibility. Results indicate that, in general, drivers do not tend to slow down significantly until visibility distance is drastically reduced by fog; however, lane keeping ability is maintained throughout most of the range of visibility distances. Lane keeping ability was reduced only when fog results in visibility distances <30 m. Overall, the current study shows that drivers are willing and able to maintain vehicular control at high speed when driving in fog; however, it is important to note that drivers chose to drive at speeds where they would be incapable of stopping to avoid obstacles in the roadway even if they were to identify and react to the obstacle immediately at the border of visibility distance. This research suggests that safety benefits may be gained by convincing drivers to slow down more than they would on their own when driving in fog or enhancing a vehicle's ability to identify and react to hazards that are not visible to the driver. In order to further understand the effects of driving in fog, future naturalistic driving research should focus on identifying and mitigating risky behaviors associated with driving in foggy conditions.     

The effect of three low-cost engineering treatments on driver fatigue: A driving simulator study

Three engineering treatments were implemented in a driving simulator study to assess the effect of road-based measures on alleviating the symptoms of fatigue. Using results from previous research on the effect of circadian rhythms on fatigue-related crashes, two groups of male drivers were recruited for this study: young shift workers under the age of 35, who attended immediately after their night shift, and older drivers over the age of 45, who completed the study during the ‘post lunch dip’ period, after consuming lunch. Eye tracking (PERCLOS) and lateral driver performance measures were used to assess whether baseline measures of fatigue changed after drivers experienced each of the three treatments, which included variable message signs, chevrons and rumble strips. Results showed a marked difference in these measures between drivers’ baseline (not fatigued) and experimental (fatigued) visits. There were also some reductions in lateral deviation and eye closure (as measured by PERCLOS) when the treatments were encountered, but no marked difference between the three treatments. These results suggest that in addition to driver- and vehicle-based methods currently employed to mitigate the effects of fatigue, the inclusion of such engineering measures may help alleviate fatigue-related impairments in driving, particularly if such treatments are implemented during long stretches of straight monotonous roads which are known to be associated with fatigue-related crashes. However, positive effects of the treatments were short lived, prompting the need for further investigations on their optimal frequency of presentation and combination to achieve maximum impact from these low-cost, road-based treatments.     

Impact of age and cognitive demand on lane choice and changing under actual highway conditions

Previous research suggests that drivers change lanes less frequently during periods of heightened cognitive load. However, lane changing behavior of different age groups under varying levels of cognitive demand is not well understood. The majority of studies which have evaluated lane changing behavior under cognitive workload have been conducted in driving simulators. Consequently, it is unclear if the patterns observed in these simulation studies carry over to actual driving. This paper evaluates data from an on-road study to determine the effects of age and cognitive demand on lane choice and lane changing behavior. Three age groups (20–29, 40–49, and 60–69) were monitored in an instrumented vehicle. The 40's age group had 147% higher odds of exhibiting a lane change than the 60's group. In addition, drivers in their 60's were less likely to drive on the leftmost lane compared to drivers in their 20's and 40's. These results could be interpreted as evidence that older adults adopt a more conservative driving style as reflected in being less likely to choose the leftmost lane than the younger groups and less likely to change lanes than drivers in their 40's. Regardless of demand level, cognitive workload reduced the frequency of lane changes for all age groups. This suggests that in general drivers of all ages attempt to regulate their behavior in a risk reducing direction when under added cognitive demand. The extent to which such self-regulation fully compensates for the impact of added cognitive demand remains an open question.     

雾对基于可见光的车辆定位性能的研究

分析雾环境下利用LED交通灯进行车辆定位的性能,讨论了接收角度、道路宽度、雾环境下接收端信噪比等对车辆定位范围的影响。仿真结果表明:信号最佳接收角度为25°;车辆距离LED交通灯20 m之内时,道路宽度对信号的接收功率影响较大;在靠近交通灯处,车辆位于第二车道上的定位距离比车辆位于第一车道的定位距离少2.2 m;晚间接收端的信噪比优于白天,晚间的定位范围大于白天;与晴朗天气相比,雾天信噪比下降较大,将极大影响车辆的可定位范围,因此为保证安全驾驶,车辆在雾天行驶过程中需要更多的制动时间。     

Driver distraction: the effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance

This paper presents the findings of a simulator study that examined the effects of distraction upon driving performance for drivers in three age groups. There were two in-vehicle distracter tasks: operating the vehicle entertainment system and conducting a simulated hands-free mobile phone conversation. The effect of visual clutter was examined by requiring participants to drive in simple and complex road environments. Overall measures of driving performance were collected, together with responses to roadway hazards and subjective measures of driver perceived workload. The two in-vehicle distraction tasks degraded overall driving performance, degraded responses to hazards and increased subjective workload. The performance decrements that occurred as a result of in-vehicle distraction were observed in both the simple and complex highway environments and for drivers in different age groups. One key difference was that older drivers traveled at lower mean speeds in the complex highway environment compared with younger drivers. The conclusions of the research are that both in-vehicle tasks impaired several aspects of driving performance, with the entertainment system distracter having the greatest negative impact on performance, and that these findings were relatively stable across different driver age groups and different environmental complexities.     

Driver's exposure to distractions in their natural driving environment

Unobtrusive video camera units were installed in the vehicles of 70 volunteer drivers over 1-week time periods to study drivers’ exposure to distractions. The video data were coded based on a detailed taxonomy of driver distractions along with important contextual variables and driving performance measures. Results show distractions to be a common component of everyday driving. In terms of overall event durations, the most common distractions were eating and drinking (including preparations to eat or drink), distractions inside the vehicle (reaching or looking for an object, manipulating vehicle controls, etc.), and distractions outside the vehicle (often unidentified). Distractions were frequently associated with decreased driving performance, as measured by higher levels of no hands on the steering wheel, eyes directed inside rather than outside the vehicle, and lane wanderings or encroachments. Naturalistic driving studies can provide a useful supplement to more controlled laboratory and field studies to further our understanding of the effects of all types of distractions on driving safety.