Users’ Understanding of Automated Vehicles and Perception to Improve Traffic Safety — Results from a National Survey

As development and testing of automated vehicle (AV) technologies grow, many advanced driver assistance systems are rapidly being deployed (Zmud & Reed, 2019). This movement is largely due to their potential benefits in reducing crashes and crash severity (e.g., Fagnant and Kockelman, 2015; Benson et al., 2018). However, empirical evidence for these safety benefits remains inconclusive (Sivak and Schoette, 2015; Strayer et al., 2017; Noy et al., 2018), and the general public remains uneasy about these technologies. Although there is a relatively large volume of literature studying people’s perceptions of AVs, there has been limited attention paid to the source of their distrust and discomfort. Hence, the AAA Foundation for Traffic Safety (AAAFTS) conducted a nationwide survey assessing: (1) People’s understanding of AVs; (2) Their expectations and concerns about AVs, and; (3) Rationales behind their distrust and discomfort toward AVs. Survey results indicated that people generally perceived higher levels of vehicle automation as potentially more effective than lower levels in preventing crashes related to specific driving behaviors (e.g., distracted driving) and situations (e.g., traffic congestion). However, concerns about AV technologies increased as the level of technology increased, with Society of Automotive Engineers (SAE) Level 5 automation receiving the greatest degree of distrust. Focus group discussions and post-survey interviews revealed that respondents’ concerns and distrust stem from unfamiliarity with the technology and perceived unreliability with current AV technologies. This study stresses the need for technology to be safer and more reliable, as well as the role of public awareness and education to increase people’s trust.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Research Brief
  • Features: Figures; References; Tables;
  • Pagination: 8p

Subject/Index Terms

Filing Info

  • Accession Number: 01727640
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 21 2020 9:48AM