When both human and machine drivers make mistakes: Whom to blame?

The advent of automated and algorithmic technology requires people to consider them when assigning responsibility for something going wrong. The authors focus on a focal question: who or what should be responsible when both human and machine drivers make mistakes in human–machine shared-control vehicles? The authors examined human judgments of responsibility for automated vehicle (AV) crashes (e.g., the 2018 Uber AV crash) caused by the distracted test driver and malfunctioning automated driving system, through a sequential mixed-methods design: a text analysis of public comments after the first trial of the Uber case (Study 1) and vignette-based experiment (Study 2). Studies 1 and 2 found that although people assigned more responsibility to the test driver than the car manufacturer, the car manufacturer is not clear of responsibility from their perspective, which is against the Uber case’s jury decision that the test driver was the only one facing criminal charges. Participants allocated equal responsibility to the normal driver and car manufacturer in Study 2. In Study 1, people gave different and sometimes antagonistic reasons for their judgments. Some commented that human drivers in AVs will inevitably feel bored and reduce vigilance and attention when the automated driving system is operating (called “passive error”), whereas others thought the test driver can keep attentive and should not be distracted (called “active error”). Study 2’s manipulation of passive and active errors, however, did not influence responsibility judgments significantly. The authors' results might offer insights for building a socially-acceptable framework for responsibility judgments for AV crashes.

• Record URL:
  https://doi.org/10.1016/j.tra.2023.103637
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0965856423000575
• 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:
  • Zhai, Siming
  • Gao, Shan
  • 0000-0002-0731-7436
  • Wang, Lin
  • Liu, Peng
  • 0000-0003-4929-0531
• Publication Date: 2023-4

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 103637
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 170
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Crash causes; Driver errors; Human machine systems; Level 3 driving automation
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01879436
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 18 2023 5:04PM