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Summary

This study introduces a new ethical framework for automated driving systems (ADSs) to build public trust. It proposes a taxonomy for ADS decision-making, covering driving style, rule compliance, and risk distribution.

Keywords:
ADC Model of moral judgmentAutonomous vehicle decision-makingAutonomous vehicle ethicsTraffic moral judgment

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Area of Science:

  • AI Ethics
  • Autonomous Systems
  • Moral Philosophy

Background:

  • Automated Driving Systems (ADSs) require ethical considerations to foster stakeholder trust.
  • A comprehensive ethical framework for ADS decision-making, integrating multiple ethical aspects and their consistency, is currently lacking.
  • Existing discussions often lack a structured approach to ADS ethical decision-making.

Purpose of the Study:

  • To address the gap in comprehensive ethical frameworks for ADS decision-making.
  • To propose a novel taxonomy for analyzing ethical judgments in automated driving.
  • To establish distinguishable ethical settings for key components of traffic moral judgment.

Main Methods:

  • Utilizing the Agent-Deed-Consequences (ADC) model of moral judgment.
  • Developing a taxonomy for ADS decision-making.
  • Identifying and categorizing components of traffic moral judgment.

Main Results:

  • A taxonomy for ADS decision-making based on the ADC model has been established.
  • Three primary components of traffic moral judgment were identified: driving style, traffic rules compliance, and risk distribution.
  • Distinguishable ethical settings were suggested for each identified traffic component.

Conclusions:

  • The proposed taxonomy provides a structured approach to ethical decision-making in ADSs.
  • The framework helps in merging and investigating the consistency of multiple ethical considerations.
  • This work contributes to the development of more trustworthy and ethically aligned automated driving systems.