Pnina Gershon, PhD
Research Scientist, MIT AgeLab
- Cambridge MA UNITED STATES
Dr. Gershon's research aims to promote safe driving and effective human-vehicle interaction.
Media
Social
Biography
Dr. Gershon's research aims to promote safe driving and effective human-vehicle interaction by understanding how human physiology, psycho-social attributes, environmental characteristics and emerging technologies, influence driving behaviors. Her research utilizes simulation, field testing, naturalistic driving studies, and advanced analytics to examine the role of automation and advanced driver assistance technology on driver’s behavior and driving safety. Dr. Gershon has extensive experience in designing and leading research studies in the areas of alcohol and drug-impaired driving, fatigue, driver distraction, speeding, motorcycle conspicuity, and high-risk driver populations including young drivers, older drivers, and professional drivers.
Prior to joining MIT AgeLab, Dr. Gershon completed her postdoctoral fellowship at Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH) where she studied the contextual nature of risky driving behaviors among novice teenage drivers. Before that, Dr. Gershon was a postdoctoral fellow at Carnegie Mellon University working with Dr. Roberta Klatzky on haptic interaction and computational modeling of multimodal assistive technologies. Dr. Gershon received her Ph.D. in Industrial Engineering and Management (2011) from Ben-Gurion University in the Negev, Israel where she also served as the Research Manager of the Driving Simulation and Safety Laboratory.
Dr. Gershon is an invited member of the National Academies of Sciences Transportation Research Board's Committee on Operator Education and Regulation and a member in the Young Driver Subcommittee. Dr. Gershon received the NIH Fellows Award for Research Excellence (2018), NICHD Collaboration Award (2017) and Leading Women in Science Award (2008). Her research has been published in prestigious scientific journals and leading conferences as well as featured in the press including the New York Times, Reuters, NIH press and more.
Areas of Expertise
Education
Ben-Gurion University of the Negev
PhD
Industrial Engineering and Management, Human Factors Engineering
2011
Ben-Gurion University of the Negev
MSc
Industrial Engineering and Management, Human Factors Engineering
2006
Ben-Gurion University of the Negev
BSc
Life Sciences, Biochemistry
2002
Languages
- English
Media Appearances
How EV and driver assist technologies impact speed versus gas vehicles
Detroit Free Press online
2026-04-06
Pnina Gershon, a research scientist at MIT’s AgeLab and Center for Transportation and Logistics who conducted the research, said the point of the study was to simply observe how people realistically drive the vehicles in their daily lives, not solve a specific engineering problem.
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.
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. Results: Alerts were evenly distributed among three clusters, which we described as leftmost lane, rightmost lane, and nonmotorway road scenarios.
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
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. Gores, lane splits or merges, and single-lane roads were most likely to be present leading up to these alerts.
Decision support for partial automation: Driver behavior and trust with different versions of automated lane change
Journal of Safety ResearchSamuel S Monfort, David G Kidd, Jessica B Cicchino, Alexandra S Mueller, Ian Reagan, Pnina Gershon, Zach Noonan, Bruce Mehler, Bryan Reimer
2026-06-01
Introduction: Automated lane change systems allow drivers to receive support for maneuver execution and sometimes initiation. Research suggests that delegating action selection to automation can impair awareness and reduce readiness to intervene if automation fails. The current study was designed to compare manual lane changes with two automated variants: driver-initiated but system-executed, and system-initiated and -executed after driver confirmation.
Method: Fourteen drivers were provided with instrumented Tesla Model 3s for a 4-week study period, during which video of the driver, the cabin, and the forward roadway, as well as data on vehicle speed and position were recorded. Automated lane changes were compared with manual lane changes, with each automated event matched to a similar manual one through stratified random sampling
Awards
National Institute of Child Health & Human Development (NICHD) Collaboration Award
Issued by Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health & Human Development, National Institutes of Health
Jan 2017
Fellows Award for Research Excellence (FARE)
Issued by National Institutes of Health
Jan 2018