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.

Sleep-related eye symptoms and their potential for identifying driver sleepiness

The majority of individuals appear to have insight into their own sleepiness, but there is some evidence that this does not hold true for all, for example treated patients with obstructive sleep apnoea. Identification of sleep-related symptoms may help drivers determine their sleepiness, eye symptoms in particular show promise. Sixteen participants completed four motorway drives on two separate occasions. Drives were completed during daytime and night-time in both a driving simulator and on the real road. Ten eye symptoms were rated at the end of each drive, and compared with driving performance and subjective and objective sleep metrics recorded during driving. ‘Eye strain’, ‘difficulty focusing’, ‘heavy eyelids’ and ‘difficulty keeping the eyes open’ were identified as the four key sleep-related eye symptoms. Drives resulting in these eye symptoms were more likely to have high subjective sleepiness and more line crossings than drives where similar eye discomfort was not reported. Furthermore, drivers having unintentional line crossings were likely to have ‘heavy eyelids’ and ‘difficulty keeping the eyes open’. Results suggest that drivers struggling to identify sleepiness could be assisted with the advice ‘stop driving if you feel sleepy and/or have heavy eyelids or difficulty keeping your eyes open’.

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.

Driver sleepiness on YouTube: A content analysis

Driver sleepiness is a major contributor to severe crashes and fatalities on our roads. Many people continue to drive despite being aware of feeling tired. Prevention relies heavily on education campaigns as it is difficult to police driver sleepiness. The video sharing social media site YouTube is extremely popular, particularly with at risk driver demographics. Content and popularity of uploaded videos can provide insight into the quality of publicly accessible driver sleepiness information. The purpose of this research was to answer two questions; firstly, how prevalent are driver sleepiness videos on YouTube? And secondly, what are the general characteristics of driver sleepiness videos in terms of (a) outlook on driver sleepiness, (b) tone, (c) countermeasures to driver sleepiness, and, (d) driver demographics. Using a keywords search, 442 relevant videos were found from a five year period (2nd December 2009–2nd December 2014). Tone, outlook, and countermeasure use were thematically coded. Driver demographic and video popularity data also were recorded. The majority of videos portrayed driver sleepiness as dangerous. However, videos that had an outlook towards driver sleepiness being amusing were viewed more often and had more mean per video comments and likes. Humorous videos regardless of outlook, were most popular. Most information regarding countermeasures to deal with driver sleepiness was accurate. Worryingly, 39.8% of videos with countermeasure information contained some kind of ineffective countermeasure. The use of humour to convey messages about the dangers of driver sleepiness may be a useful approach in educational interventions.

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.

Análise da sonolência na condução rodoviária a partir de sinais EEG

A Sociedade Europeia de Pesquisa do Sono realizou muito recentemente um estudo, onde mostrou que a prevalência média de adormecimento ao volante nos últimos 2 anos foi de 17%. Além disto, tem sido provado por todo o mundo que a sonolência durante a condução é uma das principais causas de acidentes de trânsito. Torna-se assim conveniente, o desenvolvimento de sistemas que analisem a suscetibilidade de um determinado condutor para adormecer no trânsito, bem como de ferramentas que monitorem em tempo real o estado físico e mental do condutor, para alertarem nos momentos críticos. Apesar do estudo do sono se ter iniciado há vários anos, a maioria das investigações focaram-se no ciclo normal do sono, estudando os indivíduos de forma relaxada e de olhos fechados. Só mais recentemente, têm surgido os estudos que se focam nas situações de sonolência em atividade, como _e o caso da condução. Uma grande parte Dos estudos da sonolência em condução têm utilizado a eletroencefalografia (EEG), de forma a perceber se existem alterações nas diferentes bandas de frequência desta, que possam indicar o estado de sonolência do condutor. Além disso, a evolução da sonolência a partir de alterações no piscar dos olhos (que podem ser vistas nos sinais EEG) também tem sido alvo de grande pesquisa, tendo vindo a revelar resultados bastante promissores. Neste contexto e em parceria com a empresa HealthyRoad, esta tese está integrada no projeto HealthyDrive, que visa o desenvolvimento de um sistema de alerta e deteção de sinais de fadiga e sonolência nos condutores de veículos automóveis. A contribuição desta tese no projeto prendeu-se com o estudo da sonolência dos indivíduos em condução a partir de sinais EEG, para desta forma investigar possíveis indicadores dos diferentes níveis desta que possam ser utilizados pela empresa no projeto. Foram recolhidos e analisados 17 sinais EEG de indivíduos em simulação de condução. Além disso foram desenvolvidos dois métodos de análise destes sinais: O primeiro para a deteção e análise dos piscar de olhos a partir de EEG, o segundo para análise do espetro de potência. Ambos os métodos foram utilizados para analisar os sinais recolhidos e investigar que tipo de relação existe entre a sonolência do condutor e as alterações nos piscares dos olhos, bem como as alterações do espetro do EEG. Os resultados mostraram uma correlação entre a duração do piscar de olhos e a sonolência do condutor. Com o aumento da sonolência velicou-se um aumento da duração do piscar, desencadeado principalmente pelo aumento na duração de fecho, que chegou aos 51.2%. Em relação ao espectro de potência, os resultados sugerem que a potência relativa de todas as bandas analisadas fornecem informações relevantes sobre a sonolência do condutor. Além disso, o parâmetro (_+_)/_ demostrou estar relacionado com variações da sonolência, diminuindo com o seu avanço e aumentando significativamente (111%) no instante em que os condutores adormeceram.

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

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

Quantitative and Qualitative Trade-Off Analysis of Drowsy Driver Detection Methods: Single Electrode Wearable EEG Device, Multi-Electrode Wearable EEG Device, and Head-Mounted Gyroscope

Drowsy driving impairs motorists’ ability to operate vehicles safely, endangering both the drivers and other people on the road. The purpose of the project is to find the most effective wearable device to detect drowsiness. Existing research has demonstrated several options for drowsiness detection, such as electroencephalogram (EEG) brain wave measurement, eye tracking, head motions, and lane deviations. However, there are no detailed trade-off analyses for the cost, accuracy, detection time, and ergonomics of these methods. We chose to use two different EEG headsets: NeuroSky Mindwave Mobile (single-electrode) and Emotiv EPOC (14- electrode). We also tested a camera and gyroscope-accelerometer device. We can successfully determine drowsiness after five minutes of training using both single and multi-electrode EEGs. Devices were evaluated using the following criteria: time needed to achieve accurate reading, accuracy of prediction, rate of false positives vs. false negatives, and ergonomics and portability. This research will help improve detection devices, and reduce the number of future accidents due to drowsy driving.

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.