語用論(Pragmatics)を考える

We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the driver's cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiological and performance measures show high correspondence suggesting that remote eye tracking might provide reliable driver cognitive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.

http://www.andrewkun.com/papers/2010/ETRA10_Palinko_Estimating_Cognitive_Load_final.pdf

Estimating cognitive load using remote eye tracking in a driving simulator

Understanding where people are looking is an informative social cue. In this work, we present Gaze360, a large-scale remote gaze-tracking dataset and method for robust 3D gaze estimation in unconstrained images. Our dataset consists of 238 subjects in indoor and outdoor environments with labelled 3D gaze across a wide range of head poses and distances. It is the largest publicly available dataset of its kind by both subject and variety, made possible by a simple and efficient collection method. Our proposed 3D gaze model extends existing models to include temporal information and to directly output an estimate of gaze uncertainty. We demonstrate the benefits of our model via an ablation study, and show its generalization performance via a cross-dataset evaluation against other recent gaze benchmark datasets. We furthermore propose a simple self-supervised approach to improve cross-dataset domain adaptation. Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting. Our dataset and models will be made available at http://gaze360.csail.mit.edu.

https://openaccess.thecvf.com/content_ICCV_2019/papers/Kellnhofer_Gaze360_Physically_Unconstrained_Gaze_Estimation_in_the_Wild_ICCV_2019_paper.pdf

Gaze360: Physically Unconstrained Gaze Estimation in the Wild

As the automotive industry moves toward the car of the future, technology companies are developing cutting-edge systems, in vehicle and out, that aim to make driving safer, more pleasant, and more convenient. While we are already seeing some successful video-based augmented reality (AR) auxiliary displays (e.g., center-mounted backup aid systems), the application opportunities of optical see-through AR as presented on a drivers' windshield are yet to be fully tapped; nor are the visual perceptual and attention challenges fully understood. As we race to field AR applications in transportation, we should first consider the perceptual and distraction issues that are known in both the AR and transportation communities, with a focus on the unique and intersecting aspects for driving applications. This paper describes the some opportunities and driver challenges associated with AR applications in the automotive domain. We first present a basic research space to assist in these inquiries, which delineates head-mounted from heads-up and center-mounted displays; video from optical see-through displays; and world-fixed from screen-fixed AR graphics. We then address benefits of AR related to primary, secondary, and tertiary driver tasks as well as driver perception and cognition challenges inherent in automotive AR systems.

Behind the Glass: Driver Challenges and Opportunities for AR Automotive Applications

Review of eye-related measures of drivers’ mental workload

Prior research has shown that when drivers look away from the road to view a personal navigation device (PND), driving performance is affected. To keep visual attention on the road, an augmented reality (AR) PND using a heads-up display could overlay a navigation route. In this paper, we compare the AR PND, a technology that does not currently exist but can be simulated, with two PND technologies that are popular today: an egocentric street view PND and the standard map-based PND. Using a high-fidelity driving simulator, we examine the effect of all three PNDs on driving performance in a city traffic environment where constant, alert attention is required. Based on both objective and subjective measures, experimental results show that the AR PND exhibits the least negative impact on driving. We discuss the implications of these findings on PND design as well as methods for potential improvement.

/pdf/augmented-reality-vs-street-views-a-driving-simulator-study-40646otff4.pdf

Augmented reality vs. street views: a driving simulator study comparing two emerging navigation aids

Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naive subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.

Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration

Nowadays, personal navigation devices (PNDs) that provide GPS-based directions are widespread in vehicles. These devices typically display the real-time location of the vehicle on a map and play spoken prompts when drivers need to turn. While such devices are less distracting than paper directions, their graphical display may distract users from their primary task of driving. In experiments conducted with a high fidelity driving simulator, we found that drivers using a navigation system with a graphical display indeed spent less time looking at the road compared to those using a navigation system with spoken directions only. Furthermore, glancing at the display was correlated with higher variance in driving performance measures. We discuss the implications of these findings on PND design for vehicles.

/pdf/glancing-at-personal-navigation-devices-can-affect-driving-80sar7gzic.pdf

Glancing at personal navigation devices can affect driving: experimental results and design implications

Pupil diameter is an important measure of cognitive load. However, pupil diameter is also influenced by the amount of light reaching the retina. In this study we explore the interaction between these two effects in a simulated driving environment. Our results indicate that it is possible to separate the effects of illumination and visual cognitive load on pupil diameter, at least in certain situations.

/pdf/exploring-the-effects-of-visual-cognitive-load-and-4ezp6qv9bg.pdf

Oskar Palinko

Papers

Estimating cognitive load using remote eye tracking in a driving simulator

Augmented reality vs. street views: a driving simulator study comparing two emerging navigation aids

Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration

Glancing at personal navigation devices can affect driving: experimental results and design implications

Exploring the effects of visual cognitive load and illumination on pupil diameter in driving simulators