Safe-D: Safety through Disruption

Countermeasures to Detect and Combat Inattention While Driving Partially Automated Systems



Vehicle manufacturers are introducing increasingly sophisticated vehicle automation systems to improve driving efficiency, comfort, and safety. Despite these improvements, partially and fully automated vehicles introduce new safety risks to the driving environment. Driver inattention can contribute to increased risk, especially when control transfers from automation to the human driver. To combat inattention and ensure safe and timely transitions of control, this study investigated the effectiveness of a vehicle cuing system that engages different sensory modalities (e.g., visual, auditory, and tactile) and both simple and complex cue messages to announce the need for manual takeover. Twenty-four participants completed a driving simulator study involving scripted driving sections with and without partial automation. Participants navigated six scripted automation failure events, some preceded by takeover cues. Measures of driving performance, safety, secondary task performance, and physiological indices of workload did not differ significantly based on display type or complexity. However, a clear trend showed that, compared to events not associated with takeover cues, driver reaction time to automation failure is substantially faster when preceded by cues of any type or complexity. This study provides evidence of the benefit of supporting driver situational awareness, safety, and performance by issuing cues and guiding drivers in taking control when the vehicle system predicts a likely automation failure.

Project Highlights

  • Featuring the created simulation (with triggerable automation failures) at the USA Science & Engineering Fair in April 2018

Final Report

01-002 Final Report

EWD/T2 Products

USA Science & Engineering fair – student teams, including TTI research intern Lea Huntington, presented the simulation environment and engaged thousands of students from around the US

Partially-supported the thesis work of Carolina Rodriguez Paras, who received her PhD in 2021. PhD Thesis: “Physiological Indicators of Cognitive Workload as Detectors of Performance Redlines”

A total of six students from Texas A&M University worked on this project: Carolina Rodriguez Paras, PhD student in Industrial & Systems Engineering, Johnathan McKenzie, PhD in Industrial & Systems Engineering, Pasakorn Choterungruengkorn, MS student in Industrial & Systems Engineering, Patricio Rodriguez Paras, MS student in Industrial & Systems Engineering, Lea Huntington, BS in Industrial & Systems Engineering, Christian Estrella, BS in Industrial & Systems Engineering, Andres Crucetta Nieto, BS in Industrial & Systems Engineering.


Suh, Y. and Ferris, T. (2018). On-road evaluation of in-vehicle interface characteristics and their effects on performance of visual detection on the road and manual entry. Human Factors, 61(1), 105-118. DOI: 10.1177/0018720818790841

McKenzie, J., Zahed, K., Warner, J., Uster, H., and Ferris, T.K. (2018). Survey and modeling approach to predicting driver turnover in long-haul trucking. Proceedings of the Human Factors and Ergonomics Society 62nd Annual Meeting. Philadelphia, PA, October. 1383-1383.

Rodriguez Paras, C., Ferris, T.K. (2018). A model for characterizing startle in driving contexts. Proceedings of the Human Factors and Ergonomics Society 62nd Annual Meeting. Philadelphia, PA, October.

Huntington, L. & Ferris, T. “ Countermeasures to Detect and Combat Inattention While Driving Partially Automated Systems” Poster. Poster is available here.

Final Dataset

The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/ZXVADS

Research Investigators (PI*)

Thomas Ferris (TTI/TAMU)*
Miao Song (VTTI/VT)
Mike Mollenhauer (VTTI/VT)

Project Information

Start Date: 2017-05-25
End Date: 2020-04-30
Status: Complete
Grant Number: 69A3551747115
Total Funding: $178,162
Source Organization: Safe-D National UTC
Project Number: 01-002

Safe-D Theme Areas

Automated Vehicles

Safe-D Application Areas

Driver Factors and Interfaces
Planning for Safety
Vehicle Technology

More Information

UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135

Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061