In-vehicle filming of driver fatigue on YouTube: Vlogs, crashes and bad advice

Background: Driver fatigue contributes to 15-30% of crashes, however it is difficult to objectively measure. Fatigue mitigation relies on driver self-moderation, placing great importance on the necessity for road safety campaigns to engage with their audience. Popular self-archiving website YouTube.com is a relatively unused source of public perceptions. Method: A systematic YouTube.com search (videos uploaded 2/12/09 - 2/12/14) was conducted using driver fatigue related search terms. 442 relevant videos were identified. In-vehicle footage was separated for further analysis. Video reception was quantified in terms of number of views, likes, comments, dislikes and times duplicated. Qualitative analysis of comments was undertaken to identify key themes. Results: 4.2% (n=107) of relevant uploaded videos contained in-vehicle footage. Three types of videos were identified: (1) dashcam footage (n=82); (2) speaking directly to the camera - vlogs (n=16); (3) passengers filming drivers (n=9). Two distinct types of comments emerged, those directly relating to driver fatigue and those more broadly about the video or its uploader. Driver fatigue comments included: attribution of behaviour cause, emotion experienced when watching the video and personal advice on staying awake while driving. Discussion: In-vehicle footage related to driver fatigue is prevalent on YouTube.com and is actively engaged with by viewers. Comments were mixed in terms of criticism and sympathy for drivers. Willingness to share advice on staying awake suggests driver fatigue may be seen as a common yet controllable occurrence. This project provides new insight into driver fatigue perception, which may be considered by safety authorities when designing education campaigns.

Sleepy driving and pulling over for a rest : investigating individual factors that contribute to these driving behaviours

Driver sleepiness is a substantial crash risk factor and as such, is a major contributor to crash statistics. A number of individual factors (i.e., psychological factors) have been suggested to influence driving while sleepy. However, few studies have examined the influence of these individual factors for sleepy driving in combination. The current study sought to examine how various demographic factors, attitudes, perceived legitimacy, personality constructs, and risk taking variables were associated with self-reported likelihood of driving sleepy and pulling over and resting when sleepy. The results show that being a younger driver, having positive attitudes towards driving sleepy, and high levels of emotional stability were related to self-reported likelihood of driving sleepy. Whereas, being an older driver and having negative attitudes towards driving sleepy were associated with self-reported likelihood of pulling over and resting when sleepy. Overall, the obtained results suggest that the age and attitudes of the driver have greater influence than personality traits or risk taking factors. Campaigns focused on changing attitudes to reflect the dangerousness of sleepy driving could be important for road safety outcomes.

Moderate sleep restriction in treated older male OSA participants: Greater impairment during monotonous driving compared with controls

Objectives To examine the effects on monotonous driving of normal sleep versus one night of sleep restriction in continuous positive airway pressure (CPAP) treated obstructive sleep apnoea (OSA) patients compared with age matched healthy controls. Methods Nineteen CPAP treated compliant male OSA patients (OSA-treated patients (OPs)), aged 50–75 years, and 20 healthy age-matched controls underwent both a normal night’s sleep and sleep restriction to 5 h (OPs remained on CPAP) in a counterbalanced design. All participants completed a 2 h afternoon monotonous drive in a realistic car simulator. Driving was monitored for sleepiness-related minor and major lane deviations, with ‘safe’ driving time being total time driven prior to first major lane deviation. EEGs were recorded continuously, and subjective sleepiness ratings were taken at regular intervals throughout the drive. Results After a normal night’s sleep, OPs and controls did not differ in terms of driving performance or in their ability to assess the levels of their own sleepiness, with both groups driving ‘safely’ for approximately 90 min. However, after sleep restriction, OPs had a significantly shorter (65 min) safe driving time and had to apply more compensatory effort to maintain their alertness compared with controls. They also underestimated the enhanced sleepiness. Nevertheless, apart from this caveat, there were generally close associations between subjective sleepiness, likelihood of a major lane deviation and EEG changes indicative of sleepiness. Conclusions With a normal night’s sleep, effectively treated older men with OSA drive as safely as healthy men of the same age. However, after restricted sleep, driving impairment is worse than that of controls. This suggests that, although successful CPAP treatment can alleviate potential detrimental effects of OSA on monotonous driving following normal sleep, these patients remain more vulnerable to sleep restriction.

Crash risk perception of sleepy driving and its comparisons with drink driving and speeding: Which behavior is perceived as the riskiest?

- Objective Driver sleepiness is a major crash risk factor, but may be under-recognized as a risky driving behavior. Sleepy driving is usually rated as less of a road safety issue than more well-known risky driving behaviors, such as drink driving and speeding. The objective of this study was to compare perception of crash risk of sleepy driving, drink driving, and speeding. - Methods In total, 300 Australian drivers completed a questionnaire that assessed crash risk perceptions for sleepy driving, drink driving, and speeding. Additionally, the participants perception of crash risk was assessed for five different contextual scenarios that included different levels of sleepiness (low, high), driving duration (short, long), and time of day/circadian influences (afternoon, night-time) of driving. - Results The analysis confirmed that sleepy driving was considered a risky driving behavior, but not as risky as high levels of speeding (p < .05). Yet, the risk of crashing at 4 am was considered as equally risky as low levels of speeding (10 km over the limit). The comparisons of the contextual scenarios revealed driving scenarios that would arguably be perceived as quite risky due to time of day/circadian influences were not reported as high risk. - Conclusions The results suggest a lack of awareness or appreciation of circadian rhythm functioning, particularly the descending phase of circadian rhythm that promotes increased sleepiness in the afternoon and during the early hours of the morning. Yet, the results suggested an appreciation of the danger associated with long distance driving and driver sleepiness. Further efforts are required to improve the community’s awareness of the impairing effects from sleepiness and in particular, knowledge regarding the human circadian rhythm and the increased sleep propensity during the circadian nadir.

Sleep-related crash characteristics: Implications for applying a fatigue definition to crash reports

Sleep-related (SR) crashes are an endemic problem the world over. However, police officers report difficulties in identifying sleepiness as a crash contributing factor. One approach to improving the sensitivity of SR crash identification is by applying a proxy definition post hoc to crash reports. To identify the prominent characteristics of SR crashes and highlight the influence of proxy definitions, ten years of Queensland (Australia) police reports of crashes occurring in ≥100 km/h speed zones were analysed. In Queensland, two approaches are routinely taken to identifying SR crashes. First, attending police officers identify crash causal factors; one possible option is ‘fatigue/fell asleep’. Second, a proxy definition is applied to all crash reports. Those meeting the definition are considered SR and added to the police-reported SR crashes. Of the 65,204 vehicle operators involved in crashes 3449 were police-reported as SR. Analyses of these data found that male drivers aged 16–24 years within the first two years of unsupervised driving were most likely to have a SR crash. Collision with a stationary object was more likely in SR than in not-SR crashes. Using the proxy definition 9739 (14.9%) crashes were classified as SR. Using the proxy definition removes the findings that SR crashes are more likely to involve males and be of high severity. Additionally, proxy defined SR crashes are no less likely at intersections than not-SR crashes. When interpreting crash data it is important to understand the implications of SR identification because strategies aimed at reducing the road toll are informed by such data. Without the correct interpretation, funding could be misdirected. Improving sleepiness identification should be a priority in terms of both improvement to police and proxy reporting.

Driver sleepiness—Comparisons between young and older men during a monotonous afternoon simulated drive

Young men figure prominently in sleep-related road crashes. Non-driving studies show them to be particularly vulnerable to sleep loss, compared with older men. We assessed the effect of a normal night's sleep vs. prior sleep restricted to 5 h, in a counterbalanced design, on prolonged (2 h) afternoon simulated driving in 20 younger (av. 23 y) and 19 older (av. 67 y) healthy men. Driving was monitored for sleepiness related lane deviations, EEGs were recorded continuously and subjective ratings of sleepiness taken every 200 s. Following normal sleep there were no differences between groups for any measure. After sleep restriction younger drivers showed significantly more sleepiness-related deviations and greater 4–11 Hz EEG power, indicative of sleepiness. There was a near significant increase in subjective sleepiness. Correlations between the EEG and subjective measures were highly significant for both groups, indicating good self-insight into increasing sleepiness. We confirm the greater vulnerability of younger drivers to sleep loss under prolonged afternoon driving.

The role of sleepiness, sleep disorders, and the work environment on heavy-vehicle crashes in 2 Australian states

Heavy-vehicle driving involves a challenging work environment and a high crash rate. We investigated the associations of sleepiness, sleep disorders, and work environment (including truck characteristics) with the risk of crashing between 2008 and 2011 in the Australian states of New South Wales and Western Australia. We conducted a case-control study of 530 heavy-vehicle drivers who had recently crashed and 517 heavy-vehicle drivers who had not. Drivers' crash histories, truck details, driving schedules, payment rates, sleep patterns, and measures of health were collected. Subjects wore a nasal flow monitor for 1 night to assess for obstructive sleep apnea. Driving schedules that included the period between midnight and 5:59 am were associated with increased likelihood of crashing (odds ratio = 3.42, 95% confidence interval: 2.04, 5.74), as were having an empty load (odds ratio = 2.61, 95% confidence interval: 1.72, 3.97) and being a less experienced driver (odds ratio = 3.25, 95% confidence interval: 2.37, 4.46). Not taking regular breaks and the lack of vehicle safety devices were also associated with increased crash risk. Despite the high prevalence of obstructive sleep apnea, it was not associated with the risk of a heavy-vehicle nonfatal, nonsevere crash. Scheduling of driving to avoid midnight-to-dawn driving and the use of more frequent rest breaks are likely to reduce the risk of heavy-vehicle nonfatal, nonsevere crashes by 2–3 times.

Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness.

National Highway Traffic Safety Administration, Washington, D.C.

Research on vehicle-based driver status/performance monitoring: seventh semi-annual research report.

National Highway Traffic Safety Administration, Washington, D.C.

The effectiveness of designated driver programs

Designated driver programs aim to reduce alcohol related crashes by encouraging and facilitating a safe means of transport for those who have been drinking and by influencing attitudes and knowledge. This review discusses the use and effectiveness of designated driver programs in preventing drink driving and ultimately reducing alcohol related road trauma. The limitations of studies examining designated driver programs and recommendations for further research are also discussed. The available evidence suggests that while designated driver campaigns can successfully increase the awareness and use of designated drivers, it is less clear whether these programs lead to a reduction in drink driving and/or alcohol related crashes. Differences in the way that designated driver programs have historically been implemented may account for the inconsistent evidence for their effectiveness in reducing drink driving. There are also a variety of methodological problems relating to the evaluation of designated driver programs which need to be addressed by future research.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Articulating cooperative systems and driver behaviour theories

There has been increased research interest in Co-operative Vehicle Infrastructure Systems (CVIS) from the eld of Intelligent Transport Systems (ITS). However most of the research have focused on the engineering aspects and overlooked their relevance to the drivers' behaviour. This paper argues that the priority for cooperative systems is the need to improve drivers decision making and reduce drivers' crash risk exposure to improve road safety. Therefore any engineering solutions need to be considered in conjuction with traffic psychology theories on driver behaviour. This paper explores the advantages and limitations of existing systems and emphasizes various theoretical issues that arise in articulating cooperative systems' capabilities and drivers' behaviour.

Predicting driver's hypovigilance on monotonous roads: literature review.

Drivers' ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks. Particularly, highway design reduces the driving task mainly to a lane-keeping one. It contributes to hypovigilance and road crashes as drivers are often not aware that their driving behaviour is impaired. Monotony increases fatigue, however, the fatigue community has mainly focused on endogenous factors leading to fatigue such as sleep deprivation. This paper focuses on the exogenous factor monotony which contributes to hypovigilance. Objective measurements of the effects of monotonous driving conditions on the driver and the vehicle's dynamics is systematically reviewed with the aim of justifying the relevance of the need for a mathematical framework that could predict hypovigilance in real-time. Although electroencephalography (EEG) is one of the most reliable measures of vigilance, it is obtrusive. This suggests to predict from observable variables the time when the driver is hypovigilant. Outlined is a vision for future research in the modelling of driver vigilance decrement due to monotonous driving conditions. A mathematical model for predicting drivers’ hypovigilance using information like lane positioning, steering wheel movements and eye blinks is provided. Such a modelling of driver vigilance should enable the future development of an in-vehicle device that detects driver hypovigilance in advance, thus offering the potential to enhance road safety and prevent road crashes.

Correlating eye gaze direction, depth and vehicle information on an interactive map for driver training

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. Experienced drivers have better hazard perception ability compared to inexperienced drivers. Eye gaze patterns have been found to be an indicator of the driver's competency level. The aim of this paper is to develop an in-vehicle system which correlates information about the driver's gaze and vehicle dynamics, which is then used to assist driver trainers in assessing driving competency. This system allows visualization of the complete driving manoeuvre data on interactive maps. It uses an eye tracker and perspective projection algorithms to compute the depth of gaze and plots it on Google maps. This interactive map also features the trajectory of the vehicle and turn indicator usage. This system allows efficient and user friendly analysis of the driving task. It can be used by driver trainers and trainees to understand objectively the risks encountered during driving manoeuvres. This paper presents a prototype that plots the driver's eye gaze depth and direction on an interactive map along with the vehicle dynamics information. This prototype will be used in future to study the difference in gaze patterns in novice and experienced drivers prior to a certain manoeuvre.