User meeting at the ZHAW Center for Aviation

Event details

We're excited to together with the ZHAW center for aviation host an onsite eye tracking user meeting for aviation training and performance research. Throughout this event we’ve invited three speakers to talk about their attention research using eye tracking. You’ll also be able to quiz the eye tracking experts and get practical guidance on getting the most out of our software and hardware.

The talks

Gaze analysis of air traffic controller using AI-based conflict detection

Celina Vetter, Research Assistant, ZHAW Center for Aviation

Description:  As air traffic grows and becomes more complex, air traffic control officers (ATCOs) need more technical support. Increasing automation to reduce ATCO workload is a straightforward solution, but how to ensure that ATCOs maintain good overall situation awareness (SA)? Keeping ATCOs in the loop and recent in their skills is critical for safety and resilience, especially when automation degrades requiring immediate human intervention. As a cornerstone for tuning human operators and automation, an AI-based SA system for en-route air traffic control tasks has been developed and evaluated for reliability and comparability with human SA in two experiments using human in-the-loop simulations. We investigate eye tracking (ET)-based parameters for ATCOs’ SA as a future means for sharing SA with a machine to make it knowledgeable about an ATCO’s visual attention, focus and possible gaps regarding relevant aspects of SA. We also examine the utility of the SA system in providing inputs for ATCOs’ SA and examines the experience of ATCOs in their first collaboration with an AI-based SA system. Results indicate challenges in objectively measuring ATCOs’ SA based on ET data. Inputs provided by AI-based SA system supported ATCOs’ performance-based SA. Participants’ survey answers showed that ATCOs are generally open and positive towards AI-based support tools. Future research needs to address how ATCOs’ SA and performance can be supported effectively with AI-based systems when traffic is further increased, and task load is excessive for humans alone.

The influence of increased automation on workload and gaze distribution. The mental workload of air traffic controllers in relation to their eyes.

Dr. Maik Friedrich, PhD., Researcher, Institute of Flight Guidance, German Aerospace Center

Description: Air traffic control officers work in towers and radar centers to keep traveling by aircraft safe and efficient. Their performance depends on multiple factors, e.g., traffic demand, conflict situations, support tools, situation awareness and mental workload. Looking at the projected future increase in traffic demand and its influence on this factor, a valid solution could be the increase of automation at the ATCO workplace. To identify the influence of automation in such a complex environment a number of metric and measurements have to be consider. Instantaneous Self-Assessment (Brennen & Jordan, 1992) is a questionnaire measuring the subjective mental workload while working. Eye tracking metrics can indicate physiological effects as increased mental workload. In this work we combine different metrics to identify whether an ATCO performance increases on a workplace with more automation rather than the current system. Six active ATCOs performed in 21 simulations in a human in the loop experiment. The conditions included two workplace designs in high and low traffic scenarios. Taking other mental workload measures into account, increased automation showed better results for some indicators. However, the indicators were restricted by the level of mental workload and experimental limitations. This indicates that one ATCO with increased automation performed as least as safe as two ATCOs currently in a simulated environment.

Eye tracking assisted emotion detection

Yiwei Wang, PhD. candidate, ETH Zurich

Description: Emotion plays a major role in people’s daily experiences, especially during travelling. Recognizing travellers’ emotions can help travellers with self-reflection on their mental state. Previous research has studied emotion detection using physiological signals and eye movements. However, it is still unclear whether these methods can be applied to emotion detection during travelling. Moreover, pupil size has been shown as a key indicator of emotional arousal level, but it is also influenced by other factors like luminance and cognitive load. It has been a challenge to separate the multiple influencing factors when using pupil size data, especially for eye tracking studies in the wild like detecting travellers’ emotions, where ambient luminance greatly influences pupil size. In the presentation, research about using eye tacking to detect emotions will be presented.