How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference

Traffic accidents caused by drivers’ over-reliance on SAE L2 advanced driver assistance systems (ADAS) have become a new type of accident worthy of attention. There is growing concern that the “over-hype” about automation technology produced by manufacturers or the media might affect the formation of public misconceptions about autonomous vehicles (AVs), further influencing their unsafe interaction behaviors with the system. The purpose of the authors' study was to explore whether and how “over-hype” in various contexts affects public misconceptions about AVs from the view of causality. The authors conducted a randomized controlled trial with a control group of 396 participants and a treatment group of 545 participants drawn from Zhenjiang, China. The authors applied Double Machine Learning (DML), ordered logit models, and significant difference tests to estimate the impact of “over-hype” in various contexts. The results found that the statement that “over-hype” caused public misconceptions was less rigorous. Significant causality varies among contexts of “over-hype” and aspects of misconceptions. Specifically, “over-hype” in fatal accident reports will lead to availability misconception about AVs, but not safety misconception, suggesting that participants believed AVs were available nowadays but not safe as human drivers. “Over-hype” that exaggerates ADAS capabilities has the greatest average treatment effect on public misconceptions, especially the safety misconception. The authors found that the young and the more educated are more likely to have safety and availability misconceptions under the “over-hype” of some contexts. However, the effects of “over-hype” varied less among groups with different socio-economic factors on the whole, which revealed the universality of the influence of “over-hype.” The results provided empirical evidence for regulating propaganda about AV technology and provided practical insights on how to introduce AV technology to the general public properly.

• Record URL:
  https://doi.org/10.1016/j.tra.2023.103757
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0965856423001775
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Cai, Yunhao
  • Jing, Peng
  • Wang, Baihui
  • Jiang, Chengxi
  • Wang, Yuan
• Publication Date: 2023-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 103757
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 175
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Machine learning; Perception; Public opinion; Traffic crashes; Traffic safety
• Geographic Terms: Zhenjiang (China)
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01902803
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 20 2023 3:43PM