Safe-D: Safety through Disruption

Human Factors of Driving Automation: Evasive Manuever Event Response Evaluation


An increasing number of conditionally automated driving (CAD) systems are being developed by major automotive manufacturers. In a CAD system, the automated system is in control of the vehicle within its operational design domain. Therefore, in CAD the vehicle is capable of tactical control of the vehicle and can maneuver evasively by braking or steering to avoid objects. During these evasive maneuvers, the driver may attempt to take back control of the vehicle by intervening. A driver interrupting a CAD vehicle while properly performing an evasive maneuver presents a potential safety risk. To investigate this issue, 36 participants were recruited to participate in a Wizard-of-Oz research study. The participants experienced one of two evasive maneuvers on a test track. The evasive maneuver required the CAD system to brake or steer to avoid a box placed in the lane of travel of the test vehicle. Drivers glanced toward the obstacle but did not intervene or prepare to intervene in response to the evasive maneuver. Importantly, the drivers who chose to intervene did so safely. These findings suggest that after experiencing a CAD vehicle for a brief period, most participants trusted the system enough to not intervene during a system-initiated evasive maneuver.

Project Highlights

  • This work supported the completion of Master’s thesis, by Nicholas Britten entitled, “An On-Road Assessment of Driver Secondary Task Engagement and Performance during Assisted & Automated Driving”.
  • Nicholas was selected as a 2021 Dwight D. Eisenhower Transportation Fellow. As part of this fellowship, Nicholas attended the Transportation Research Board (TRB) Annual Meeting and was selected to present his Master’s research during the Eisenhower Fellows poster session.

Final Report

VTTI 00-033 Human Factors of Driving Automation: Evasive Maneuver Event Response Evaluation

EWD & T2 Products

Course module: A course module based on this research project was developed. The course module covers the research background, methodology, and results, and discusses the limitations and future research opportunities of this research project. Students are asked to:

    • How they would design a study to explore this research problem including what variables they would select and how they would analyze them
    • (Optional step) Provide the class a link to the anonymized dataset on the Safe-D website and have the class use their proposed analysis or the researchers’ planned analysis
    • What conclusions they think can be drawn from the study based on the study results
    • What study limitations they can identify and for future research ideas

Student Impact Statement: This project funded a Master’s student, Nicholas Britten, and served as the basis for his master’s thesis. Nicholas received his M.S. from the Virginia Tech Industrial and Systems Engineering Department 2020.

Dataset on the Dataverse: This dataset includes the driver responses to the maneuver and the location and duration of the driver’s eye glance before and after the maneuver.

Master’s Thesis: Britten, N. (2021). An On-Road Assessment of Driver Secondary Task Engagement and Performance during Assisted & Automated Driving (Master’s Thesis, Virginia Tech).


Britten, N., Johns, M., Hankey, J., & Kurokawa, K. (2023). Do you trust me? Driver responses to automated evasive maneuvers. Frontiers in Psychology14, 1128590.

Britten, N. (2022, October 12). On-road assessment of driver mode awareness of assisted and automated driving [Paper presentation]. Human Factors and Ergonomics Society Annual Meeting 66, Atlanta, GA, United States.

Britten, N. (2022, January). An On-Road Assessment of Driver Secondary Task Engagement and Performance during Assisted & Automated Driving.[Eisenhower fellow poster session]. Transportation Research Board Annual Meeting, Washington, D.C., United States.

Final Dataset

The final datasets for this project will be located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/0FL714.

Research Investigators (PI*)

Jon Hankey (VTTI)*
Sheldon Russel (VTTI/VT)
Nick Britten (Student-VT)

Project Information

Start Date: 2021-01-05
End Date: 2023-02-05
Status: Complete
Grant Number: 69A3551747115
Total Funding: $2,050,000
Source Organization: Safe-D National UTC
Project Number: VTTI-00-033

Safe-D Theme Areas

Automated Vehicles

Safe-D Application Areas

Risk Assessment
Driver Factors and Influences
Performance Measures
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

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