An investigation of driver behavior on urban general road and in tunnel areas
The objective of this study is to examine experience-related differences in microscope driving behavior as drivers performed six separate maneuvers, namely 1) driving on general urban roads, 2) approaching a tunnel portal, 3) driving through a tunnel's threshold zone, 4) driving in the interior tunnel zone, 5) driving in the zone ahead the tunnel exit and 6) driving after the tunnel exit. An on-road experiment was conducted with 20 drivers in two groups. The first group was made up of new licensed drivers, and the second group contained the more experienced drivers. The study consisted of one between-subject (experience) and five within-subject variables (drive environment type). The drivers' behavior was measured through Mean Glance Duration, AOI Attention Ration, Horizontal Eye Activity, Vertical Eye Activity, Percentage of Eyelid Closure, and Heart Rate Variability. With respect to the relevant psychological measures, the results show that in general more attention is focused on the far left-hand side of the road and the near front road when driving through tunnel areas when compared with driving on general roads. In addition, the psychological measurements indicate that tunnel's dark narrow environment causes anxiety on driving for lower heart rate variation coefficient (RRCV). New licensed drivers were more severely affected by the tunnel environment than the experienced drivers.
Applications of Eye Tracking for Region of Interest HEVC Encoding
Joose Sainio : Applications of Eye Tracking for Region of Interest HEVC Encoding Tampere University of Technology Master of Science Thesis, 50 pages, 1 Appendix page September 2018 Master’s Degree Programme in Information Technology Major: Embedded Systems Examiner: Ass. Prof. Jarno Vanne Keywords: video coding, High Efficiency Video Coding (HEVC), eye tracking, region of interest (ROI), Kvazaar HEVC encoder The increase in video streaming services and video resolutions has exploded the volume of Internet video traffic. New video coding standards, such as High Efficiency Video Coding (HEVC) have been developed to mitigate this inevitable video data explosion with better compression. The aim of video coding is to reduce the video size while maintaining the best possible perceived quality. Region of Interest (ROI) encoding particularly addresses this objective by focusing on the areas that humans would pay the most attention at and encode them with higher quality than the non-ROI areas. Methods for finding the ROI, and video encoding in general, take advantage of the Human Visual System (HVS). Computational HVS models can be used for the ROI detection but all current state-of-the-art models are designed for still images. Eye tracking data can be used for creating and verifying these models, including models suitable for video, which in turn calls for a reliable way to collect eye tracking data. Eye tracking glasses allow the widest range of possible scenarios out of all eye tracking equipment. Therefore, the glasses are used in this work to collect eye tracking data from 41 different videos. The main contribution of this work is to present a real-time system using eye tracking data to enhance the perceived quality of the video. The proposed system makes use of video recorded from the scene camera of the eye tracking glasses and Kvazaar open-source HEVC encoder for video compression. The system was shown to provide better subjective quality over the native rate control algorithm of Kvazaar. The obtained results were evaluated with Eye tracking Weighted PSNR (EWPSNR) that represents the HVS better than traditional PSNR. The system is shown to achieve up to 33% bit rate reduction for the same EWPSNR and on average 5-10% reduction depending on the parameter set. Additionally, the encoding time is improved by 8-20%.
Are pilots prepared for a cyber-attack? A human factors approach to the experimental evaluation of pilots’ behavior
The increasing prevalence of technology in modern airliners brings not just advantages, but also the potential for cyber threats. Fortunately, there have been no significant attacks on civil aircraft to date, which allows the handling of these emerging threats to be approached proactively. Although an ample body of research into technical defense strategies exists, current research neglects to take the human operator into account. In this study, we present an exploratory experiment focusing on pilots confronted with a cyber-attack. Results show that the occurrence of an attack affects all dependent variables: pilots' workload, trust, eye-movements, and behavior. Pilots experiencing an attack report heavier workload and weakened trust in the system than pilots whose aircraft is not under attack. Further, pilots who experienced an attack monitored basic flying instruments less and their performance deteriorated. A warning about a potential attack seems to moderate several of those effects. Our analysis prompts us to recommend incorporating cyber-awareness into pilots' recurrent training; we also argue that one has to consider all affected personnel when designing such training. Future research should target the development of appropriate procedures and training techniques to prepare pilots to correctly identify and respond to cyber-attacks.
Determining Eye Blink Rate Level Utilizing Sitting Postural Behavior Data
Dry eye syndrome (DES) affects many white-collars workers worldwide. Though it is known that low eye blink rate (EBR) is associated with the risk of DES, it is difficult to improve EBR through self-correction. One way to increase EBR is to warn the worker of low EBR using an external system. Existing EBR measurement devices have limitations, such as physical discomfort or invasiveness, which hinder their acceptance. For a solution that overcomes these limitations, this study aimed to develop a classification system that differentiates the levels of EBR using posture and postural variability data obtained from chair-embedded distance and pressure sensors. Additionally, this study attempted to investigate the relationship between EBR, posture, and postural variability. Participants completed three seated computer tasks, in which eye blink and postural sensor data were collected. The EBR classification system was developed by using a machine learning method; the accuracy of the EBR classification system was 93% across the three task types and study participants. The low EBR level was found to be associated with smaller postural variability and a tendency for the worker to hold a forward-leaning sitting posture. The EBR classification system developed in this study is expected to contribute to the prevention of DES.
Do people with Parkinson’s disease look at task relevant stimuli when walking? An exploration of eye movements
Eye movements are impaired by Parkinson’s disease (PD) although limited research has explored if PD affects the relevance of visual fixations when walking. Visual fixations may provide crucial contextual information for safe navigation and important insights into fall risk. This study aimed to: investigate visual fixations made while walking under a range of conditions in PD; identify their task relevance; and explore their relationship with clinical features. Thirty-eight people with mild-moderate PD and forty age-matched control participants completed a straight walk with (i) no additional stimuli and (ii) with additional stimuli (visual cues or a high contrast obstacle), whilst wearing a mobile eye-tracker. Fixations were extracted and classified by location and relevance. PD participants made proportionally fewer task-relevant fixations (floor, walls and additional stimuli ahead), caused by significantly more task-irrelevant fixations (floor, walls and ceiling away from waking path) during normal walking (p = 0.014). These group differences were not apparent with visual cues (p = 0.359). During obstacle crossing trials, PD made significantly more task-relevant fixations than controls (p = 0.007). Reduced bilateral visual acuity was associated with fewer fixations in PD. Our findings suggest that people with PD visually explore complex environments less efficiently likely owing to underlying PD pathology. Visual exploration improved with the addition of salient stimuli (for example visual cues or an obstacle) and thus developing and optimising visual interventions could prove critical to improving locomotor safety and reducing falls risk in home environments.
Do you see what I see? Mobile eye-tracker contextual analysis and inter-rater reliability
Mobile eye-trackers are currently used during real-world tasks (e.g. gait) to monitor visual and cognitive processes, particularly in ageing and Parkinson’s disease (PD). However, contextual analysis involving fixation locations during such tasks is rarely performed due to its complexity. This study adapted a validated algorithm and developed a classification method to semi-automate contextual analysis of mobile eye-tracking data. We further assessed inter-rater reliability of the proposed classification method. A mobile eye-tracker recorded eye-movements during walking in five healthy older adult controls (HC) and five people with PD. Fixations were identified using a previously validated algorithm, which was adapted to provide still images of fixation locations (n = 116). The fixation location was manually identified by two raters (DH, JN), who classified the locations. Cohen’s kappa correlation coefficients determined the inter-rater reliability. The algorithm successfully provided still images for each fixation, allowing manual contextual analysis to be performed. The inter-rater reliability for classifying the fixation location was high for both PD (kappa = 0.80, 95% agreement) and HC groups (kappa = 0.80, 91% agreement), which indicated a reliable classification method. This study developed a reliable semi-automated contextual analysis method for gait studies in HC and PD. Future studies could adapt this methodology for various gait-related eye-tracking studies.
Driver state monitoring using consumer electronic devices: Innovation report
An impaired mental and physical state such as fatigue, high level of workload, or distraction, can make a driver prone to errors and lead to sub-optimal driving performance. If a human remains in full or partial control of a vehicle, drivers’ state is an important aspect of driving and cannot be neglected, given its significant impact on road safety. It is, therefore, beneficial for future automobiles to be fitted with a feature which enables detection of any physical and mental abnormalities in drivers’ state in real time using physiological and emotional indicators. Such a feature is often referred to as Driver State Monitoring (DSM) system. It is forecasted that DSM is expected to become a standard passenger car feature by 2025 and its integration is encouraged by the standards authorities. Previous research has predominantly considered the use of medical grade devices for the purpose of DSM. Instead, this research project has considered the potential use of Consumer Electronic Devices (CEDs) as part of DSM. However, the literature lacks evidence that this can be accomplished in a valid and reliable manner. Thus, the research, presented in this doctorate, aims to provide knowledge that describes feasibility and integration of CEDs into the vehicles for the purpose of DSM, from both technological and human factors perspectives. Firstly, this research project has produced a model of a hybrid DSM system. The model combines physiological and emotional sensing within CEDs. This can be used to enhance validity and reliability of DSM in a flexible and cost-efficient manner. The model also acknowledges barriers of introduction of hybrid DSM into the automotive market. Acceptance, one of the important adoption barriers of DSM technology, was studied and behaviour intention to use the system was statistically appraised using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. It was found that social influence is a significant factor affecting drivers’ behaviour intention to use hybrid DSM in the near future. On the other hand, it was demonstrated that there is no significant negative attitude towards the use of hybrid DSM technology due to apprehension, intimidation, or fear of making mistakes. These findings indicate viability of DSM in the driving context. To further deepen understanding of CED-based DSM, three driving simulator user trials were conducted. Overall, supporting evidence for adoption of CEDs in DSM was provided by utilising state of the art methodology in DSM while characterising sensory capabilities of CEDs. The studies were specifically aiming to (1) determine the reliability and validity of wearable CEDs to measure human physiology while driving, (2) provide supporting evidence for employing CEDs in physiological and emotional evaluation of common driving activities, and (3) explore the effect of cognitive and visual workload on drivers’ state and driving performance during the automated to manual control transition scenarios. All three studies have demonstrated evidence of CEDs being well suited to reliably monitor drivers’ state. For instance, it was demonstrated how an extent of workload can be reliably measured using heart rate variability, captured by means of CEDs in the driving context. This approach could enable cost-efficient access to drivers’ state outside of driving activities. To facilitate this, a modular and cost-effective mobile DSM toolkit was designed and developed in-house. The toolkit enabled driver-state-related data collection, filtering, on-board analysis, storage, and synchronisation. It can be concluded that this EngD has successfully demonstrated that CEDs can be used for the purpose of DSM.
Effects of mental demands on situation awareness during platooning: A driving simulator study
Previous research shows that drivers of automated vehicles are likely to engage in visually demanding tasks, causing impaired situation awareness. How mental task demands affect situation awareness is less clear. In a driving simulator experiment, 33 participants completed three 40-min runs in an automated platoon, each run with a different level of mental task demands. Results showed that high task demands (i.e., performing a 2-back task, a working memory task in which participants had to recall a letter, presented two letters ago) induced high self-reported mental demands (71% on the NASA Task Load Index), while participants reported low levels of self-reported task engagement (measured with the Dundee Stress State Questionnaire) in all three task conditions in comparison to the pre-task measurement. Participants’ situation awareness, as measured using a think-out-loud protocol, was affected by mental task demands, with participants being more involved with the mental task itself (i.e., to remember letters) and less likely to comment on situational features (e.g., car, looking, overtaking) when task demands increased. Furthermore, our results shed light on temporal effects, with heart rate decreasing and self-constructed mental models of automation growing in complexity, with run number. It is concluded that mental task demands reduce situation awareness, and that not only type-of-task, but also time-on-task, should be considered in Human Factors research of automated driving.
