Automated and connected vehicle technologies, like truck platoons, offer tremendous promise for driving safety, efficiency, and productivity. Some projections even go as far to suggest that these technologies will eliminate all traffic fatalities. However, the benefits of these technologies will only be realized if they are designed for the human beings that interact with them. This interaction is particularly significant in cases where automation fails or hits an operational limit, where drivers may unexpectedly be asked to resume control of the vehicle often with little time to re-engage and react before a crash. One method of alleviating these problematic transitions is to integrate models of human behavior directly into the design process. The models can be used to predict human reactions and differentiate between scenarios where the driver can recover safely, and those where a crash is likely to occur. In this project we develop a model of human behavior during automation failures that may be integrated into current and future design processes for automated vehicles. We will use this model to generate a set of design guidelines for future automated vehicle following technologies that will promote safety and reduce automated driving crashes.
Alambeigi, H., McDonald, A.D., and Tankasala, R. (2020). Crash themes in automated vehicles: A topic modeling analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database. Presented at the 99th Annual Meeting of the Transportation Research Board. Washington, D.C. January, 2020.(Accepted)
Alambeigi, H., McDonald, A.D., and Tankasala, R. (2019). Crash themes in automated vehicles: A topic modeling analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database. Transportation Research Record, Journal of the Transportation Research Board. Submitted.
Alambeigi, H. & McDonald, A.D. (2020). Modeling post-takeover avoidance and stabilization steering control in automated vehicles. Submitted to the Annual meeting of the HFES.
Start Date: 1/1/2018
End Date: 12/31/2020
Grant Number: 69A3551747115
Total Funding: $353,915
Source Organization: Safe-D National UTC
Project Number: 03-036
McDonald AD, Alambeigi H, Engstrom J, et al. Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures. Hum Factors J Hum Factors Ergon Soc 2019. doi:10.1177/0018720819829572 (Published)
Driver Factors and Interfaces
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Texas A&M University
Texas A&M Transportation Institute
College Station, Texas 77843-3135
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061