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Related Experiment Video

Updated: Nov 10, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Blaming automated vehicles in difficult situations.

Matija Franklin1, Edmond Awad2, David Lagnado1

  • 1Department of Experimental Psychology, University College London, London WC1E 6BT, UK.

Iscience
|April 2, 2021
PubMed
Summary
This summary is machine-generated.

People blame automated vehicles (AVs) more than human drivers, even when the situation is equally difficult. This increased blame for AVs persists regardless of the situation

Keywords:
Artificial IntelligencePsychologyResearch Methodology Social Sciences

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Transportation Technology

Background:

  • Automated vehicles (AVs) are advancing towards widespread adoption, but their mistakes, some fatal, raise public concerns.
  • Understanding public perception of AV errors is crucial for their integration into society.
  • Existing research lacks a clear comparison of blame attribution between AVs and human drivers for similar errors.

Purpose of the Study:

  • To investigate if perceived situation difficulty or novelty influences blame attributed to AV errors.
  • To compare public blame attribution for AV mistakes versus human driver mistakes.
  • To identify psychological factors affecting public acceptance of AV technology.

Main Methods:

  • Two studies were conducted to assess public reactions to AV and human driving errors.
  • Participants evaluated blame based on varying levels of situation difficulty and novelty.
  • Comparative analysis of blame attributed to AVs versus human drivers was performed.

Main Results:

  • Blame attribution for both AVs and human drivers was significantly influenced by situation difficulty.
  • Despite situational complexities, participants consistently assigned more blame to AVs than to human drivers.
  • Perceived novelty of the situation did not significantly alter blame attribution patterns.

Conclusions:

  • Public perception of AVs is characterized by a higher degree of blame compared to human drivers, irrespective of objective difficulty.
  • Psychological barriers, such as an inherent bias against automated systems, may impede AV acceptance.
  • Findings offer critical insights for developing strategies to foster public trust in automated vehicle technology.