Automated driving systems (ADSs) have the potential to fundamentally transform transportation by reducing crashes, congestion, and cost while improving traffic efficiency and access to mobility for the transportation-challenged population. However, people may not use ADS as intended due to their misunderstanding of such systems’ capabilities and limitations. Recent news articles, reporting Tesla drivers napping behind the wheel, suggest the need for a better understanding of how people are using ADSs as well as what benefits and consequence that such systems have on transportation safety. Therefore, this work aims to investigate the (1) limitations of automated longitudinal and lateral control features (e.g., adaptive cruise control and lane keeping assistance) found in real-world operation, (2) unintended use of such systems and their safety consequences, as well as (3) driver perception of these novel technologies. For this purpose, this study will leverage data collected from 50 participants who drove personally owned vehicles equipped with ADSs for 12 months. The work is expected to contribute to a greater understanding of the prevalence and safety consequences of ADS use on public roadways, as well as drivers’ perception of the early production ADS. The findings from this project may further inform the development of human-machine interfaces, training programs, and owners’ manuals to reduce unintended use of ADSs and negative consequences. The identified characteristics of the situations when the driving automation requested human drivers’ intervention or failed without alerts will further inform the development of testing scenarios to ensure ADS safety.
Research Findings & Lessons Learned (pptx): These slides provide an overview of the project along with methods, results, findings, implications, and lessons learned on how to improve upon the research.
Course Module (pptx): This presentation is a course module developed from this project to introduce students to the project and the dataset.
Student Impact Statement – Adam Novotny (pdf) and Rick Greatbatch (pdf): The student(s) working on this project provided an impact statement describing what the project allowed them to learn/do/practice and how it benefited their education.
Kim, H., Miao Song, & Doerzaph, Z. (2020). Real-World Use of Partially Automated Driving Systems and Driver Impressions. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Presented), Sage CA: Los Angeles, CA: SAGE Publications.
Yang, L., Furukawa, T., Zuo, L., Parker, R., & Doerzaph, Z. (2019). Level-of-Confidence Driven Automatic Emergency Stop to a Safe Roadside. 5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-zero-19). Blacksburg, VA.
A manuscript: Kim, H., Miao Song, & Doerzaph, Z. (2020). Real-World Use of Partially Automated Driving Systems and Driver Impressions. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Accepted)
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/98NBN7.
Start Date: 2019-11-01
End Date: 2020-07-30
Grant Number: 69A3551747115
Total Funding: $80,326.00
Source Organization: Safe-D National UTC
Project Number: VTTI-00-029
Planning for Safety
Operations and Design
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061