Assessing the Alignment of Automated Vehicle Decisions with Human Reasons

2025-07-31

AI Summary

Autonomous vehicles (AVs) often exhibit ethical decision-making behaviors that diverge from human expectations during routine driving. Method: This paper proposes a human-reason-informed trajectory evaluation framework that formalizes the “meaningful human control” (MHC) tracking condition—previously conceptual—as a computable, quantitative model. Unlike rigid rule-based systems, it integrates compliance, comfort, and efficiency via a weighted priority scheme coupled with an exclusion-avoidance coordination mechanism. Embedded as a modular evaluation layer within existing motion planning pipelines, it enables transparent, interpretable ethical alignment. Contribution/Results: Evaluated on realistic overtaking scenarios inspired by real-world driving, the framework successfully exposes and balances trade-offs among competing objectives. It demonstrates both practical utility and conceptual novelty in enhancing human-aligned, explainable decision-making for everyday AV operations.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

A key challenge in deploying automated vehicles (AVs) is ensuring they make appropriate decisions in ethically challenging everyday driving situations. While much attention has been paid to rare, high-stakes dilemmas such as trolley problems, similar tensions also arise in routine scenarios, such as navigating empty intersections, where multiple human considerations, including legality and comfort, often conflict. Current AV planning systems typically rely on rigid rules, which struggle to balance these competing considerations and can lead to behaviour that misaligns with human expectations. This paper proposes a novel reasons-based trajectory evaluation framework that operationalises the tracking condition of Meaningful Human Control (MHC). The framework models the reasons of human agents, such as regulatory compliance, as quantifiable functions and evaluates how well candidate AV trajectories align with these reasons. By assigning adjustable weights to agent priorities and integrating a balance function to discourage the exclusion of any agent, the framework supports interpretable decision evaluation. Through a real-world-inspired overtaking scenario, we show how this approach reveals tensions, for instance between regulatory compliance, efficiency, and comfort. The framework functions as a modular evaluation layer over existing planning algorithms. It offers a transparent tool for assessing ethical alignment in everyday scenarios and provides a practical step toward implementing MHC in real-world AV deployment.

Problem

• Ensuring AV decisions align with human ethical expectations
• Balancing conflicting human considerations in routine driving scenarios
• Developing interpretable frameworks for ethical AV trajectory evaluation

Innovation

• Reasons-based trajectory evaluation framework
• Quantifiable human agent reasons modeling
• Modular ethical alignment evaluation layer