Bruce Mehler
Research Scientist / Engineer
- Boston MA UNITED STATES
Bruce Mehler’s research interests are in driver monitoring, attention support, and adaptation to new technologies and user interfaces.
Media
Social
Biography
Research Focus
Human-Centered Systems & Quality of Life
Designing systems, services, and policies that enhance human well-being across work, home, and society.
Areas of Expertise
Education
Boston University
MA
Psychology
1983
University of Washington
BS
Psychology
1976
Affiliations
- MIT AgeLab : Research Scientist
- Human Factors & Ergonomics Society
- SAE International
- Society for Psychophysiological Research
Languages
- English
Media Appearances
Building an understanding of how drivers interact with emerging vehicle technologies
MIT News online
2024-11-22
“Cultivating public trust in AI will be the most significant factor for the future of assisted and automated vehicles,” says Bryan Reimer, AVT Consortium founder and a research engineer at the MIT AgeLab within the MIT Center for Transportation and Logistics (CTL). “Without trust, technology adoption will never reach its potential, and may stall. Our research aims to bridge this gap by understanding driver behavior and translating those insights into safer, more intuitive systems that enable safer, convenient, comfortable, sustainable and economical mobility.”
Tesla drivers become less attentive when using Autopilot, study finds
Mashable online
2021-09-21
The model for the study is based upon glance data from 290 human-initiated autopilot disengagement epochs. Essentially, it replicated the observed glance pattern of drivers. MIT's Alberto Morando, Pnina Gershon, Bruce Mehler, and Bryan Reimer conducted the study by following Tesla Model S and X owners for fractions of a year or more, all based in the greater Boston area.
Study measures how fast humans react to road hazards
MIT News online
2019-08-07
Joining Wolfe on the paper are: Bobbie Seppelt, Bruce Mehler, Bryan Reimer, of the MIT AgeLab, and Ruth Rosenholtz of the Department of Brain and Cognitive Sciences and CSAIL.
Speaking Engagements/Featured Conversations
The Road to Vehicle Automation: How Far Are We?
In this episode, we’re joined by the co-directors of the Advanced Vehicle Technology (AVT) Consortium, hosted within the MIT AgeLab at the MIT Center or Transportation & Logistics: Dr. Bryan Reimer, Dr. Pnina Gershon, and Dr. Bruce Mehler. They explore key insights from their recent research, the role of data in shaping safer and smarter mobility solutions, and how the consortium is addressing critical questions around driver behavior, automation readiness, and industry collaboration as they celebrate their 10th anniversary year. Watch the full episode: https://www.youtube.com/watch?v=ktovjEipLIY
Research Papers
Environmental context is associated with differences in driver behavior around partial automation alerts
Journal of Safety ResearchAlexandra S Mueller, Pnina Gershon, Samantha H Haus, Jessica B Cicchino, Bruce Mehler, Bryan Reimer
2026-09-01
Introduction: The study’s objective was to understand how driver behavior around Tesla Autopilot alerts varies with environmental context. Methods: Using on-road data from 13 drivers who drove a 2020 Model 3 as their personal vehicle for 1 month, epochs were captured around alerts that were thought to be associated with environmental features rather than driver behavior. The presence of roadway features in the 10 s leading up to the beginning of these alerts were used to cluster the alerts by the road scenario. Hand, eye glance, and secondary task behavior surrounding alerts were compared across clusters.
Driver behavior around driver readiness alerts versus attention reminders while using Tesla Autopilot
Journal of Safety ResearchAlexandra S Mueller, Pnina Gershon, Samantha H Haus, Jessica B Cicchino, Bruce Mehler, Bryan Reimer
2026-09-01
Abstract Introduction: Tesla’s Autopilot attention reminders typically follow escalation sequences that start with visual-only alerts, but some alerts bypass this escalation pattern and begin with the visual-audible alert phase. This study’s aim was to understand what triggered those alerts and how drivers behave around them over time. Method: We analyzed 283 alerts that bypassed the visual-only phase from 13 drivers who used a 2020 Tesla Model 3 as their personal vehicle for 4 weeks. We identified roadway features and driver behaviors that were present around these alerts. Results: Alerts most often occurred on high-speed, limited-access roads and during the daytime.
From distraction to support: evolving perspectives on driver attention support and the case for a holistic, context-aware framework
Transportation Research Part F: Traffic Psychology and BehaviourBryan Reimer, Linda Angell, Alexandria M Noble, Bruce Mehler, Lee Skrypchuk, Steven Feit, Gregory M Fitch
2026-07-01
Since the original development of driver distraction guidelines, scientific understanding of driver workload, attention threading, situation awareness, and the influence of driving context has significantly evolved—driven largely by insights from naturalistic driving studies and other research. Concurrently, vehicle technologies have advanced, integrating new forms of internal and external sensing, increased computational power, larger and often multiple screens, multi-modal interfaces, and feedback systems. This paper reviews and builds upon prior research and guidelines, particularly those coming out of the U.S., as that is the background of the majority of the authors, to propose a new conceptual framework for attention support.