A Framework for Human-Reason-Aligned Trajectory Evaluation in Automated Vehicles

TL;DR

This paper presents a reasons-based trajectory evaluation framework for automated vehicles that aligns decision-making with human considerations like legality, efficiency, and comfort, demonstrating sensitivity to priority weights.

Contribution

It introduces a novel framework operationalising Meaningful Human Control by quantifying human reasons and evaluating trajectory alignment, addressing routine ethical conflicts in AVs.

Findings

Different trajectories depend on agent reason weights.

Small priority shifts cause discrete changes in actions.

The framework effectively models human considerations in AV planning.

Abstract

One major challenge for the adoption and acceptance of automated vehicles (AVs) is ensuring that they can make sound decisions in everyday situations that involve ethical tension. Much attention has focused on rare, high-stakes dilemmas such as trolley problems. Yet similar conflicts arise in routine driving when human considerations, such as legality, efficiency, and comfort, come into conflict. Current AV planning systems typically rely on rigid rules, which struggle to balance these competing considerations and often lead to behaviour that misaligns with human expectations. This paper introduces a reasons-based trajectory evaluation framework that operationalises the tracking condition of Meaningful Human Control (MHC). The framework represents human agents reasons (e.g., regulatory compliance) as quantifiable functions and evaluates how well candidate trajectories align with them. It assigns adjustable weights to agent priorities and includes a balance function to discourage excluding any agent. To demonstrate the approach, we use a real-world-inspired overtaking scenario, which highlights tensions between compliance, efficiency, and comfort. Our results show that different trajectories emerge as preferable depending on how agents reasons are weighted, and small shifts in priorities can lead to discrete changes in the selected action. This demonstrates that everyday ethical decisions in AV driving are highly sensitive to the weights assigned to the reasons of different human agents.