Measure Driver Situational Awareness through Eye-Tracking Technology

he AdVitam research project, conducted by HumanTech, aims to evaluate drivers’ situational awareness
during autonomous driving. Using Pupil Invisible eye-tracking glasses, the project collected precise eye-
tracking data from drivers in a simulated driving environment. These glasses provide detailed insights
into the driver’s visual attention and focal points. [1]

In the collected data obtained through the simulated driving environment, drivers were also required to
perform a secondary task using a tablet.This additional aspect adds complexity to the analysis and
measurement of drivers’ situational awareness during autonomous driving.

The main objective of the AdVitam project is to analyze the collected data and measure drivers’ sit-
uational awareness during autonomous driving. In addition to quantifying the amount of time drivers
spend on different areas of interest (AOI) such as the road (vehicle environment), the dashboard, and a
tablet, there are several other interesting metrics to analyze. These metrics can provide deeper insights
into the drivers’ attentional patterns and situational awareness during autonomous driving scenarios.

The analysis of these metrics in the AdVitam project is aimed at achieving a comprehensive under-
standing of drivers’ situational awareness.This analysis approach will contribute to the improvement
of autonomous driving systems’ design and development.By gaining insights into drivers’ situational
awareness, the project aims to enhance the safety, efficiency, and overall effectiveness of future interac-
tions between drivers and vehicles.


Measuring drivers’ situational awareness (SA) is of utmost importance for several reasons:

1.Safety:Situational awareness is crucial for safe driving.It refers to the driver’s understanding
of their surroundings, including the road conditions, traffic, and potential hazards. By measuring
drivers’ SA, we can identify any gaps or deficiencies in their awareness, which can help in developing
strategies and interventions to improve safety on the road.

2.Human-Machine Interaction:As autonomous driving technology advances, the interaction
between humans and machines becomes increasingly critical.Measuring drivers’ SA during au-
tonomous driving allows us to evaluate how well they comprehend and adapt to the capabilities
and limitations of the automated systems.This information helps in designing effective human-
machine interfaces and improving the overall interaction between drivers and autonomous vehicles.

3.Performance Optimization:Measuring SA provides insights into how drivers allocate their
attention and cognitive resources during different driving scenarios.By understanding drivers’
attention patterns, we can optimize the design of autonomous systems, providing appropriate in-
formation and alerts at the right time to enhance performance and decision-making.

4.Trust and Acceptance:Trust plays a vital role in the acceptance and adoption of autonomous
driving technology. If drivers have a high level of SA and trust in the technology, they are more
likely to accept and effectively engage with autonomous driving features.

5.Training:Measuring SA can also inform driver training programs.By identifying areas where
drivers may lack awareness or have misconceptions, targeted training programs can be developed
to enhance their SA skills, promoting safer and more efficient driving practices.
Overall, measuring drivers’ situational awareness provides valuable insights into their understanding of
the driving environment, their interaction with autonomous systems, and their overall performance on
the road.

General information
  • Date: 14.07.2023
  • Type: Bachelor project
  • Responsible: Elena Mugellini


  • Julian Derighetti
Marine Capallera
Senior Researcher
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Quentin Meteier
Senior Researcher
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Laurent Rime