| 1. |
- Hallvig, D., et al.
(author)
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Sleepy driving on the real road and in the simulator - A comparison
- 2013
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575 .- 1879-2057. ; 50, s. 44-50
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Journal article (peer-reviewed)abstract
- Sleepiness has been identified as one of the most important factors contributing to road crashes. However, almost all work on the detailed changes in behavior and physiology leading up to sleep related crashes has been carried out in driving simulators. It is not clear, however, to what extent simulator results can be generalized to real driving. This study compared real driving with driving in a high fidelity, moving base, driving simulator with respect to driving performance, sleep related physiology (using electroencephalography and electrooculography) and subjective sleepiness during night and day driving for 10 participants. The real road was emulated in the simulator. The results show that the simulator was associated with higher levels of subjective and physiological sleepiness than real driving. However, both for real and simulated driving, the response to night driving appears to be rather similar for subjective sleepiness and sleep physiology. Lateral variability was more responsive to night driving in the simulator, while real driving at night involved a movement to the left in the lane and a reduction of speed, both of which effects were absent in the simulator. It was concluded that the relative validity of simulators is acceptable for many variables, but that in absolute terms simulators cause higher sleepiness levels than real driving. Thus, generalizations from simulators to real driving must be made with great caution.
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| 2. |
- Hallvig, David, et al.
(author)
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Real driving at night - predicting lane departures from physiological and subjective sleepiness
- 2014
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In: Biological Psychology. - : Elsevier BV. - 0301-0511 .- 1873-6246. ; 101, s. 18-23
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Journal article (peer-reviewed)abstract
- Only limited information is available on how driving performance relates to physiological and subjective sleepiness on real roads. This relation was the focus of the present study. 33 volunteers drove for 90min on a rural road during the afternoon and night in an instrumented car, while electroencephalography and electrooculography and lane departures were recorded continuously and subjective ratings of sleepiness were made every 5min (Karolinska Sleepiness Scale - KSS). Data was analyzed using Bayesian multilevel modeling. Unintentional LDs increased during night driving, as did KSS and long blink durations(LBD). Lateral position moved to the left . LDs were predicted by self-reported sleepiness and LBDs across time and were significantly higher in individuals with high sleepiness. Removal of intentional LDs, enhanced the KSS/LD relation. It was concluded that LDs, KSS, and LBDs are strongly increased during night driving and that KSS predicts LDs.
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| 3. |
- Sandberg, David, 1980, et al.
(author)
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The impact of sleepiness on lane positioning in truck driving
- 2013. - 1
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In: Driver Distraction and Inattention. - Farnham : Ashgate. - 9781409425854 - 9781315578156 ; , s. 405-416, s. 405-416
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Book chapter (peer-reviewed)abstract
- This chapter concerns the detection of sleepiness in truck drivers. Data obtained from a driver sleepiness study involving real-world driving are used in order to analyse the performance of several sleepiness indicators based on driving behavior; such as, for example, variability in lateral position and heading angle. Contrary to the results obtained for passenger cars, for heavy trucks it is found that indicators based on variability provide little or no information; their performance does not rise significantly above chance levels.However, the data indicate that there is a significant difference in the average lane position for sleepy and alert drivers, respectively, such that a sleepy driver generally places the vehicle closer (by about 0.2 m) to the centre of the road than an alert driver. The analysis also shows a significant, monotonous, increase in average lateral position (measured from the right, outer, lane boundary towards the lane centre) between the four cases of (i) daytime alert driving, (ii) daytime sleepy driving, (iii) night-time alert driving and (iv) nighttime sleepy driving.
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| 4. |
- Sandberg, David, 1980, et al.
(author)
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The Characteristics of Sleepiness During Real Driving at Night - A Study of Driving Performance, Physiology and Subjective Experience
- 2011
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In: Sleep. - 1550-9109 .- 0161-8105. ; 34:10, s. 1317-1325
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Journal article (peer-reviewed)abstract
- Study Objectives: Most studies of sleepy driving have been carried out in driving simulators. A few studies of real driving are available, but these have used only a few sleepiness indicators. The purpose of the present study was to characterize sleepiness in several indicators during real driving at night, compared with daytime driving. Design: Participants drove 55 km (at 90km/h) on a 9-m-wide rural highway in southern Sweden. Daytime driving started at 09: 00 or 11: 00 (2 groups) and night driving at 01: 00 or 03: 00 (balanced design). Setting: Instrumented car on a real road in normal traffic. Participants: Eighteen participants drawn from the local driving license register. Interventions: Daytime and nighttime drives. Measurement and Results: The vehicle was an instrumented car with video monitoring of the edge of the road and recording of the lateral position and speed. Electroencephalography and electrooculography were recorded, together with ratings of sleepiness every 5 minutes. Pronounced effects of night driving were seen for subjective sleepiness, electroencephalographic indicators of sleepiness, blink duration, and speed. Also, time on task showed significant effects for subjective sleepiness, blink duration, lane position, and speed. Sleepiness was highest toward the end of the nighttime drive. Night driving caused a leftward shift in lateral position and a reduction of speed. The latter two findings, as well as the overall pattern of sleepiness indicators, provide new insights into the effects of night driving. Conclusion: Night driving is associated with high levels of subjective, electrophysiologic, and behavioral sleepiness.
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| 5. |
- Anund, Anna, et al.
(author)
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Factors associated with self-reported driver sleepiness and incidents in city bus drivers
- 2016
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In: Industrial Health. - : National Institute of Occupational Safety and Health. - 0019-8366 .- 1880-8026. ; 54:4, s. 337-346
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Journal article (peer-reviewed)abstract
- Driver fatigue has received increased attention during recent years and is now considered to be a major contributor to approximately 15-30% of all crashes. However, little is known about fatigue in city bus drivers. It is hypothesized that city bus drivers suffer from sleepiness, which is due to a combination of working conditions, lack of health and reduced sleep quantity and quality.The overall aim with the current study is to investigate if severe driver sleepiness, as indicated by subjective reports of having to fight sleep while driving, is a problem for city based bus drivers in Sweden and if so, to identify the determinants related to working conditions, health and sleep which contribute towards this. The results indicate that driver sleepiness is a problem for city bus drivers, with 19% having to fight to stay awake while driving the bus 2-3 times each week or more and nearly half experiencing this at least 2-4 times per month. In conclusion, severe sleepiness, as indicated by having to fight sleep during driving, was common among the city bus drivers. Severe sleepiness correlated with fatigue related safety risks, such as near crashes.
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| 6. |
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| 7. |
- Phillips, Ross Owen, et al.
(author)
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Fatigue in transport : a review of exposure, risks, checks and controls*
- 2017
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In: Transport reviews. - : Routledge. - 0144-1647 .- 1464-5327. ; , s. 1-25
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Journal article (peer-reviewed)abstract
- Human fatigue continues to threaten safe transport. There are claims that employers of operators should do more to mitigate the risks, and several regulators are promoting fatigue-risk management in the context of safety management systems (SMS). The current paper reviews fatigue-related risk and exposure factors and control measures for operators of land- and sea-based transport forms. Our review identifies 13 types of measures for the monitoring or control of fatigue risks: optimal staffing; optimal schedule design; optimisation of breaks/naps; monitoring of actual hours worked; optimisation of work content; monitoring and feedback of actual sleep; health screening and treatment; promotion of recovery from work; fitness-for-duty testing; monitoring of fatigue symptoms while operating; control of fatigue while operating; performance monitoring and assistance; and fatigue-proofing. We also identify two systemic measures needed to anchor risk mitigation in SMS: organisational learning and training/other. By structuring monitoring and control measures along Dawson and McCulloch’s [Managing fatigue: It’s about sleep. Sleep Medicine Reviews, 9(5), 365–380] fatigue-risk trajectory, a framework is obtained that acts as a guide for fatigue-risk management by transport employers. To inform transport managers further, evaluations are needed of the effectiveness of individual control measures as well as whole fatigue-risk management interventions.
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