Areen Alsaid
University of Michigan-Dearborn, MI·Engineering
Automated vehicles could make roads safer, improve mobility and cut emissions, but only if people trust them the right amount. Drivers who trust automation too much can become complacent and crash.
- Fields
Engineering
Psychology
Computer Science
- Topics
human-automation interaction
trust in automation
trust calibration
- Grants
- 1 known grant, through Sep 2027
What they work on
- Develop a framework for how driver trust transfers between automation levels and driving contexts, using survey studies that also assess the role of environmental factors.
- Estimate how similarity in human-machine interface design across automation levels affects trust transfer, using a driving simulator study that records behavioral, physiological and eye-tracking data in real time.
- Build and validate a dynamic computational model that predicts how trust changes over time from the active automation level, environmental context and interface design, trained on survey and simulator data.
More about the research
Drivers who trust it too little may not use it at all. The problem is hardest in partially automated cars, where the human and the car share control and the balance shifts from moment to moment. This project asks how trust carries over, or "transfers," when a driver moves between different levels of automation and different driving situations.
Why it matters
Miscalibrated trust is a major barrier to the safe use of automated and partially automated vehicles. A validated model of how driver trust shifts across automation levels and interface designs could help vehicle designers build adaptive dashboards and displays that keep drivers neither overconfident nor distrustful. That could reduce complacency-related crashes and help automated vehicles deliver their safety, mobility and emissions benefits.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ziran Wang
Purdue University, IN·Engineering·Early-career
Determine how visual attention, spoken language and physical actions reveal a vehicle occupant's cognitive state during interaction with automation, using behavioral sensing.
Jane Huang
University of Notre Dame, IN·Computer Science
Design human-centered interfaces that help teams supervise autonomous drone swarms in time-critical tasks such as disaster response, using cognitive work analysis and iterative user-centered design.
Chong Pak
Clemson University, SC·Psychology
Determine how individual differences in working memory and attention control (the ability to focus and resist distraction) relate to how well people perform with automated systems, using newly developed, validated attention control measures.
The school
University of Michigan-Dearborn: 3 labs on PI Pages, $12M a year on research, 20 research doctorates a year.
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