Distracted Drivers 2026
Hosted by Dr. Timothy Wright
Dr. Timothy Wright, Senior Principal Research Associate at Dunlap & Associates, Inc. holds MS and PhD degrees in Cognitive Psychology from Florida State University. Dr. Wright’s research interests span domains of attention, distracted driving, vulnerable road users, and transportation safety generally. Throughout his 10-year career, he has served as principal investigator or co-principal investigator on projects that together represent over 6 million dollars. These projects involved the development and implementation of a variety of different approaches both in the laboratory and the field. These projects included demonstration programs, literature reviews, and field studies exploring factors related to the safety of both passenger vehicle drivers and pedestrians/bicyclists. In recognition of his accomplishments, he served as committee member to the Transportation Research Board’s Committee on Simulation and Measurement of Vehicle and Operator Performance and elected the Vice President and subsequently President of the New England Chapter of the Human Factors and Ergonomics Society.
Effects of Control Modality on Driver Performance During Secondary Tasks
Mr. John Cliburn
Contemporary vehicles increasingly rely on touchscreen-based interfaces, a design shift that may undermine control distinguishability and require drivers to divert visual attention from the roadway to complete secondary tasks. The present study investigates performance differences between touchscreen and tactile controls during a continuous longitudinal and lateral primary tracking task. Participants completed a counterbalanced within-subjects experiment interacting with either a touchscreen interface or a functionally matched tactile interface. The touchscreen interface included sliders, buttons, and toggle buttons implemented in Unity, whereas the tactile interface consisted of physical rotary encoders, momentary buttons, and latching buttons. The primary tracking task required participants to drive a vehicle sprite along a winding path using a steering wheel and pedals. During driving, participants responded to periodic secondary task prompts occurring at random intervals. Preliminary analyses from seven participants revealed no significant differences between interface conditions in glance duration, total eyes-off-road time, or mean response time. However, response times in the tactile condition showed considerably greater variability, potentially reflecting a shift toward slower but more sight-free interaction strategies for some interface elements. Subjective reports further suggested faster development of proficiency with tactile controls. These findings highlight potential differences in learning and interaction strategies across modalities.
Evidence Based Insights on Driver Attention and Alert Effectiveness
Dr. Alyssa Ryan and Dr. Saquib M. Haroon
Distracted driving contributes to thousands of crashes each year, with human error at the core of most roadway incidents. This proposed presentation examines distraction through the lens of human factors, highlighting findings from studies on novel sources of distraction, including drone operations, as well as experimental work identifying effective alert strategies for regaining driver attention for the best response outcome using driving simulation. By connecting cognitive, visual, and auditory demands with real-world crash statistics, the presentation discusses how system design and technology can both mitigate and exacerbate distraction.
Uses of Artificial Intelligence for Behavioral Safety in the Trucking Context
Mr. Nanda Srinivasan
In 2023, 5,472 people were killed in traffic crashes involving large trucks. Drivers of large trucks involved in fatal crashes are less likely to be alcohol-impaired (4% compared to 24% for passenger car drivers) and are less likely to have license suppressions or revocations (6.4% compared to 16.4% for passenger cars). However, there are many common factors associated with both truck and passenger drivers. This presentation will examine behaviors such as distraction and drowsy driving in the context of use of AI to change driver behavior and culture. The overall context of distraction and drowsy driving will be presented along with specific approaches to using newer technology to help improve safety and reducing risky driving. General overview, analysis, and summary of over 3 billion hours of video data on drivers and driving habits, particularly with respect to distraction and drowsy driving will be presented. Lessons learned will be of enormous importance in ongoing discussions in the safety professional community on enhancing traffic safety culture, The presentation includes current and future research in the area.
Task Analysis of Five On-Road Vehicles
Mr. Ben Tankersley
Distracted driving remains a critical behavioral safety issue contributing to roadway accidents. Auburn University, in collaboration with Toyota’s Collaborative Safety Research Center, is conducting research to quantify the behavioral risks associated with modern in-vehicle interfaces. The purpose of this research was to understand how the interaction model (cognitive and visual demands of the in-vehicle interface that may precipitate driver error) might impact the specific behavioral demands placed on the driver and resulting behavioral safety. This work lays the foundation for a systematic methodology to map the specific behavioral demands placed on the driver by the in-vehicle interface. This approach allows us to contrast interactions to determine which designs best support safe driving behaviors and identify which design characteristics may promote unsafe driver behaviors. We selected the vehicles by targeting very different interaction models (mostly buttons, mostly touchscreen, and combinations thereof). In this detailed task analysis, we collected information about how drivers input information into the in-vehicle center console, the types of feedback that they receive from the vehicle, and the individual steps required to complete each task. We conducted qualitative and quantitative analysis comparing the five vehicles. From this data set, we draw initial conclusions about the implications of this work for the design of new in-vehicle information systems that encourage safe driver behaviors by minimizing distraction. This work will support future research that will ultimately offer data-driven recommendations for behavioral safety interventions and system designs intended to minimize voluntary and involuntary driver distraction.
Drivers’ Opinions of and Experiences Using Subaru’s DriverFocus
Ms. Aimee E. Cox
Driver monitoring systems have the potential to reduce crashes and injuries from driver distraction and drowsiness. This study measured the use and acceptance of Subaru’s DriverFocus, a camera-based system that can monitor drivers outside of automation use. An online survey was conducted in September 2024 among 3,475 United States-based owners of Subaru models equipped with DriverFocus. Most owners were knowledgeable about DriverFocus and 87% said they drove with it on most or every time they drove the vehicle. Drivers who used the system were more likely to have received at least one distraction alert than to have received a drowsiness alert (97% vs 37%) in the previous 30 days. Drivers perceived some alerts as false positives, and the perceived false negative rate was low. Most drivers agreed that they would want the system again (70%), and those who drove with it on agreed it made them a safer driver (64%) and helped them avoid distractions (63%). Receiving too many alerts and annoyance were the top reasons cited by the 4% of drivers who turned the system off under their profile settings. Most Subaru owners of vehicles equipped with DriverFocus appear to see safety benefits from the system. Drivers indicated experience with some false positive alerts, but misconceptions about what behaviors should trigger a distraction alert appears to have distorted this perception. Because drivers who receive many alerts are likely the ones to benefit most from the system, we offer suggestions for improving its acceptability to make it more effective. Manufacturers using camera-based monitoring during use of partial automation could expand this functionality for use during normal driving. Helping drivers better understand the behaviors that the system is designed to detect may increase acceptance.