Automated vehicle technologies may significantly improve driving safety, but only if they are widely adopted and if drivers use them appropriately. Prior work suggests that intentions to adopt new technology and appropriately rely on it are often driven by the user’s expectations. In recent years, these expectations increasingly depend on news presented on social media. For example, recent polls suggest that the majority of Twitter users primarily use the site as a news source. The power of social media in creating and changing expectations suggests that it may be a disruptive tool for increasing the adoption and safe use of automated vehicle technology. In this project, we seek to understand the conversation about automated vehicles on Twitter through a network and natural language processing analysis. We further focus on responses and changes of opinion surrounding automated vehicle crashes. These analyses will identify a set of terms, key opinion generators, and hashtags that lead to the most accurate and positive responses to automated vehicles. In the final phase of the project, we will translate these findings into guidelines for automated vehicle crash responses to help public information officers structure their communications about crashes. Research has shown that avoiding misinformation and structuring communication leads to improved outcomes in emergencies and thus we expect these guidelines to facilitate automated vehicle safety.
Coming Soon!
One Pager for Public Information Officers– Social media guidelines for discussing automated vehicle incidents.
Jefferson, J. A. and McDonald, A.D. (2019). The automated vehicle social network: Analyzing tweets after a recent Tesla Autopilot crash. To be presented at the Human Factors and Ergonomics Society’s 2019 International Annual Meeting, Seattle, WA, October 2019.
Wei, R., Alambeigi, H., McDonald, A.D. (2020) Topic modeling social media data after fatal automated vehicle crashes. Submitted to the HFES Annual Meeting
Tony McDonald (TTI/TAMU)*
Bert Huang (VT)
Jacelyn Jefferson (TAMU)
Michelle Canton (TTI)
Shuangfei Fan (VT)
Start Date: 2019-03-01
End Date: 2020-05-31
Status: Active
Grant Number: 69A3551747115
Total Funding: $283,435
Source Organization: Safe-D National UTC
Project Number: 04-098
Big Data Analytics
Automated Vehicles
Driver Factors and Interfaces
Planning for Safety
Vehicle Technology
RiP URL
UTC Project Information Form
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
3135 TAMU
College Station, Texas 77843-3135
USA
Virginia Polytechnic Institute and State University
Virginia Tech Transportation Institute
3500 Transportation Research Plaza
Blacksburg, Virginia 24061
USA