A Framework for Ethical Decision-Making in Automated Vehicles through Human Reasons-based Supervision

TL;DR

This paper proposes a human reasons-based supervision framework for automated vehicles that detects misalignments with human ethical considerations and prompts trajectory re-evaluation, aiming for more human-centered decision-making.

Contribution

It introduces a novel supervision framework that integrates human reasons into AV decision-making, enhancing ethical responsiveness in real-time driving scenarios.

Findings

Simulation shows improved ethical responsiveness in AV trajectories.

Framework effectively detects misalignments with human reasons.

Supports real-time adaptation in dynamic environments.

Abstract

Ethical dilemmas are a common challenge in everyday driving, requiring human drivers to balance competing priorities such as safety, efficiency, and rule compliance. However, much of the existing research in automated vehicles (AVs) has focused on high-stakes "trolley problems," which involve extreme and rare situations. Such scenarios, though rich in ethical implications, are rarely applicable in real-world AV decision-making. In practice, when AVs confront everyday ethical dilemmas, they often appear to prioritise strict adherence to traffic rules. By contrast, human drivers may bend the rules in context-specific situations, using judgement informed by practical concerns such as safety and efficiency. According to the concept of meaningful human control, AVs should respond to human reasons, including those of drivers, vulnerable road users, and policymakers. This work introduces a novel human reasons-based supervision framework that detects when AV behaviour misaligns with expected human reasons to trigger trajectory reconsideration. The framework integrates with motion planning and control systems to support real-time adaptation, enabling decisions that better reflect safety, efficiency, and regulatory considerations. Simulation results demonstrate that this approach could help AVs respond more effectively to ethical challenges in dynamic driving environments by prompting replanning when the current trajectory fails to align with human reasons. These findings suggest that our approach offers a path toward more adaptable, human-centered decision-making in AVs.