Moral Complexity in Traffic: Advancing the ADC Model for Automated Driving Systems

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.