The study explores the impact of light conditions on driver sleepiness. In a driving simulator experiment, 20 drivers drove both during daytime in an alert condition and then later at night after being awake since early morning. Light conditions were manipulated by driving in a simulated nighttime scenario in a dark room (1 lx) versus driving in simulated daylight in a room lit with light emitting diodes combining blue light with a yellow phosphor giving a two peaked spectrum (212 lx). Both the daylight and the darkness scenarios were driven daytime and nighttime in a 2 × 2 design. Sleepiness was measured during the four 1-hour drives in terms of subjective sleepiness ratings, divided attention ability, driving performance, heart rate variability and blink behaviour. Significant differences were found in all measured sleepiness indicators between the daytime and nighttime drives, and in most indicators for time-on- task. No significant main effects were found between simulated daylight and darkness. A psychomotor vigilance test conducted before and after each drive also showed no significant effects for lighting condition. Further research, preferably using longitudinal studies in more realistic settings on real roads, is needed to determine which behaviours and which cognitive processes that are affected when driving in daylight versus darkness.

This paper presents initial work on a context-dependent driver distraction detection algorithm called AttenD2.0, which extends the original AttenD algorithm with elements from the Minimum Required Attention (MiRA) theory. Central to the original AttenD algorithm is a time buffer which keeps track of how often and for how long the driver looks away from the forward roadway. When the driver looks away the buffer is depleted and when looking back the buffer fills up. If the buffer runs empty the driver is classified as distracted. AttenD2.0 extends this concept by adding multiple buffers, thus integrating situation dependence and visual time-sharing behaviour in a transparent manner. Also, the increment and decrement of the buffers are now controlled by both static requirements (e.g. the presence of an on-ramp increases the need to monitor the sides and the mirrors) as well as dynamic requirements (e.g., reduced speed lowers the need to monitor the speedometer). The algorithm description is generic, but a real-time implementation with concrete values for different parameters is showcased in a driving simulator experiment with 16 bus drivers, where AttenD2.0 was used to ensure that drivers are attentive before taking back control after an automated bus stop docking and depot procedure. The scalability of AttenD2.0 relative to available data sources and the level of vehicle automation is demonstrated. Future work includes expanding the concept to real-world environments by automatically integrating situational information from the vehicles environmental sensing and from digital maps.

The European Rail Traffic Management System (ERTMS) will replace national standards with the aim to promote cross-border traffic and enhance efficiency. The transition involves a shift from lineside signalling to mostly in- cabin information via a Driver Machine Interface (DMI). Previous research indicates that this may lead to a decrease in driver attention to the outside world and to a decrease in workload, leading to boredom. Using a train simulator, 41 participants drove the same track with the ERTMS system and the Swedish national standard (ATC) while wearing eye- tracking equipment. Subjective workload and boredom assessments were made after each drive. An analysis of the first set of reduced data (15 participants) showed that the formal attentional requirements like the monitoring of speed changes and signals were fulfilled in almost all cases, regardless of system. Overall, however, the data indicate that in line with previous research the drivers focus their attention more to the inside of the train when using the ERTMS system. This is corroborated by the finding that horn blowing is slightly delayed with the ERTMS system. Perceived workload was generally low, with the ERTMS system experienced to be more boring. We draw the preliminary conclusion that while formal attentional requirements are fulfilled for both systems, the ERTMS system likely has a tendency to pull the drivers’ overall attention inwards. Given that for the ERTMS system most relevant information is presented inside of the train on the DMI, this is not surprising, but needs to be addressed by the authorities.

Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their purpose. The main idea is to consider not only the intention behind each glance, but to also account for what is relevant in the surrounding scene, regardless of whether the driver has looked there or not. In essence, the old approaches, unaware as they are of the larger context or motivation behind eye movements, have taken us as far as they can. We propose this more integrative approach to gain a better understanding of the complexity of drivers' informational needs and how they satisfy them in the moment.

Objective:

To assess the attentional demand of different contextual factors in driving.

Background:

The attentional demand on the driver varies with the situation. One approach for estimating the attentional demand, via spare capacity, is to use visual occlusion.

Method:

Using a 3 × 5 within-subjects design, 33 participants drove in a fixed-base simulator in three scenarios (i.e., urban, rural, and motorway), combined with five fixed occlusion durations (1.0, 1.4, 1.8, 2.2, and 2.6 s). By pressing a microswitch on a finger, the driver initiated each occlusion, which lasted for the same predetermined duration within each trial. Drivers were instructed to occlude their vision as often as possible while still driving safely.

Results:

Stepwise logistic regression per scenario indicated that the occlusion predictors varied with scenario. In the urban environment, infrastructure-related variables had the biggest influence, whereas the distance to oncoming traffic played a major role on the rural road. On the motorway, occlusion duration and time since the last occlusion were the main determinants.

Conclusion:

Spare capacity is dependent on the scenario, selected speed, and individual factors. This is important for developing workload managers, infrastructural design, and aspects related to transfer of control in automated driving.

Application:

Better knowledge of the determinants of spare capacity in the road environment can help improve workload managers, thereby contributing to more efficient and safer interaction with additional tasks.

Approximately 20-30% of all road fatalities are related to driver sleepiness. A long-lasting goal in driver state research has therefore been to develop a robust sleepiness detection system. Since the alertness level is reflected in autonomous nervous system activity, it has been suggested that various heart rate variability (HRV) metrics can be used as features for driver sleepiness classification. Since the heart rate is modulated by many different factors, and not just by sleepiness, it is relevant to question the high driver sleepiness classification accuracies that have occasionally been presented in the literature. The main objective of this paper is thus to test how well a sleepiness classification system based on HRV features really is. A unique data set with 86 drivers, obtained while driving on real roads in real traffic, both in alert and sleep deprived conditions, was used to train and test a support vector machine (SVM) classifier. Subjective ratings based on the Karolinska sleepiness scale (KSS) was used as ground truth to divide the data into three classes (alert, somewhat sleepy and severely sleepy). Even though nearly all the 24 investigated HRV metrics showed significant differences between sleepiness levels, the SVM results only reached a mean accuracy of 61 %, with the worst results originating from the severely sleepy cases. In summary, the high classification performance that may arise in studies with high experimental control could not be replicated under realistic driving conditions. Future works should focus on how various confounding factors should be accounted for when using HRV based metrics as input to a driver sleepiness detection system.

Purpose

To examine eye tracking data and compare visual behavior in individuals with normal hearing (NH) and with moderate hearing loss (HL) during two types of driving conditions: normal driving and driving while performing a secondary task.

Methods

24 participants with HL and 24 with NH were exposed to normal driving and to driving with a secondary task (observation and recall of 4 visually displayed letters). Eye movement behavior was assessed during normal driving by the following performance indicators: number of glances away from the road; mean duration of glances away from the road; maximum duration of glances away from the road; and percentage of time looking at the road. During driving with the secondary task, eye movement data were assessed in terms of number of glances to the secondary task display, mean duration of glances to the secondary task display, and maximum duration of glances to the secondary task display. The secondary task performance was assessed as well, counting the number of correct letters, the number of skipped letters, and the number of correct letters ignoring order.

Results

While driving with the secondary task, drivers with HL looked twice as often in the rear-view mirror than during normal driving and twice as often as drivers with NH regardless of condition. During secondary task, the HL group looked away from the road more frequently but for shorter durations than the NH group. Drivers with HL had fewer correct letters and more skipped letters than drivers with NH.

Conclusions

Differences in visual behavior between drivers with NH and with HL are bound to the driving condition. Driving with a secondary task, drivers with HL spend as much time looking away from the road as drivers with NH, however with more frequent and shorter glances away. Secondary task performance is lower for the HL group, suggesting this group is less willing to perform this task. The results also indicate that drivers with HL use fewer but more focused glances away than drivers with NH, they also perform a visual scan of the surrounding traffic environment before looking away towards the secondary task display.

Driver sleepiness contributes to a considerable proportion of road accidents, and a fit-for-duty test able to measure a drivers sleepiness level might improve traffic safety. The aim of this study was to develop a fit-for-duty test based on eye movement measurements and on the sleep/wake predictor model (SWP, which predicts the sleepiness level) and evaluate the ability to predict severe sleepiness during real road driving. Twenty-four drivers participated in an experimental study which took place partly in the laboratory, where the fit-for-duty data were acquired, and partly on the road, where the drivers sleepiness was assessed. A series of four measurements were conducted over a 24-h period during different stages of sleepiness. Two separate analyses were performed; a variance analysis and a feature selection followed by classification analysis. In the first analysis it was found that the SWP and several eye movement features involving anti-saccades, pro-saccades, smooth pursuit, pupillometry and fixation stability varied significantly with different stages of sleep deprivation. In the second analysis, a feature set was determined based on floating forward selection. The correlation coefficient between a linear combination of the acquired features and subjective sleepiness (Karolinska sleepiness scale, KSS) was found to be R = 0.73 and the correct classification rate of drivers who reached high levels of sleepiness (KSS andgt;= 8) in the subsequent driving session was 82.4% (sensitivity = 80.0%, specificity = 84.2% and AUC = 0.86). Future improvements of a fit-for-duty test should focus on how to account for individual differences and situational/contextual factors in the test, and whether it is possible to maintain high sensitive/specificity with a shorter test that can be used in a real-life environment, e.g. on professional drivers.

We have developed a physiological signal platform where presently phonocardiographic (PCG) and electrocardiographic (ECG) signals can be acquired and on which our signal analysis techniques can be implemented. The platform can also be used to store patient data, to enable comparison over time and invoke distance consultation if necessary. Our studies so far indicate that with our signal analysis techniques of heart sounds we are able to separate normal subject from those with aortic stenosis and mitral insufficiency. Further we are able to identify the third heart sound. The platform is being tested in a primary health care setting.

BACKGROUND AND OBJECTIVE: Patients have reported that they perceive their own heart sounds differently after open cardiac surgery than before the surgery. The present study was designed to investigate whether changes in heart sounds can be quantitatively measured. METHOD: Heart sounds were recorded from 57 patients undergoing coronary artery bypass graft (CABG) surgery and from a control group of 10 subjects. The so-called Hjorth descriptors and the main frequency peak were compared before and after surgery to determine whether the characteristics of the heart sounds had changed. RESULTS: At a group level, the first heart sound was found to be significantly different after CABG surgery. Generally, the heart sounds shifted toward a lower frequency after surgery in the CABG group. No significant changes were found in the control group. CONCLUSIONS: Heart sounds are altered after CABG surgery. The changes are objectively quantifiable and may also be subjectively perceived by the patients.