Driver’s Mental Models of Advanced Vehicle Technologies: A Proposed Framework for Identifying and Predicting Operator Errors

Advanced vehicle technologies are increasingly more accessible and available in vehicles. These current and future systems, despite promising added safety, convenience, and efficiency to drivers and road users, have an inherently higher level of complexity than the driving systems that most drivers are used to operating. In order to maximize the promised benefits, drivers will need to have a good understanding of these systems—referred to as mental models—in order to use them safely and appropriately. Previous research has identified drivers’ gaps in knowledge of advanced vehicle technologies. Beyond users’ knowledge of a system, understanding and defining a user’s mental model is critical for many aspects of advanced vehicle technologies, including the design of, training for, and use of these systems. However, characterizing a driver’s mental model is still a significant challenge. Moreover, these gaps and challenges will only be further accentuated with more complexity in vehicle automation, especially with higher levels of automation. This research was conducted to better elucidate advanced vehicle technologies from a user control perspective, to examine driver interaction with such complex systems, and to characterize driver mental models in this context. This is achieved through: (i) a review of the current state and complexities of one advanced vehicle technology—Adaptive Cruise Control (ACC)—and its associated documentation, (ii) a review and synthesis of existing literature on mental models and error-making, (iii) the development of a task analysis for driver-automation interactions, and (iv) the building of a framework to help examine user interactions with complex systems to identify sources and probabilities of error commission. This document also reports on an examination of the limitations of various ACC systems in the current market in the context of manufacturer’s reporting of such limitations.

• Dataset URL:
  • https://doi.org/10.7910/DVN/LDO7PP
• Record URL:
  • http://safersim.nads-sc.uiowa.edu/final_reports/AAAFTS-SaferSim_UMass_Yr1_Report%20FINAL.pdf
• 
• Summary URL:
  • https://aaafoundation.org/drivers-mental-models-of-advanced-vehicle-technologies-a-proposed-framework-for-identifying-and-predicting-operator-errors/
• Summary URL:
  • https://aaafoundation.org/wp-content/uploads/2022/01/21-1003-AAAFTS_SaferSim-Fact-Sheet_FINAL.pdf
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/60329
• Dataset URL:
  • https://rosap.ntl.bts.gov/view/dot/61532
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. ©2021, AAA Foundation for Traffic Safety, SAFER-SIM. Supporting datasets available at: https://doi.org/10.7910/DVN/LDO7PP; https://rosap.ntl.bts.gov/view/dot/61532
• Corporate Authors:

  University of Massachusetts, Amherst

  Amherst, MA  United States  01003

  Safety Research Using Simulation University Transportation Center (SaferSim)

  University of Iowa
  Iowa City, IA  United States  52242

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  AAA Foundation for Traffic Safety

  Washington, DC  United States 
• Authors:
  • Pradhan, Anuj K
  • 0000-0002-7612-4208
  • Pai, Ganesh
  • 0000-0002-5151-9860
  • Knodler, Michael
  • 0000-0002-6517-4066
  • Fitzpatrick, Cole
  • 0000-0002-9873-1391
  • Horrey, William J
  • 0000-0002-9533-4411
• Publication Date: 2021-2

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; References; Tables;
• Pagination: 37p

Subject/Index Terms

• TRT Terms: Automation; Autonomous intelligent cruise control; Autonomous vehicles; Driver errors; Drivers; Knowledge
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01764808
• Record Type: Publication
• Contract Numbers: 69A3551747131
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Feb 17 2021 10:44AM