Operationalising Meaningful Human Control for Reason-Responsive Decision-Making in Automated Vehicles

Abstract

Automated vehicles (AVs) are expected to improve road safety, efficiency, and accessibility, yet their behaviour can at times appear overly cautious, rigid, or counter-intuitive, undermining trust and public acceptance. Existing approaches to address this problem, ranging from ethical decision-making models to behaviour imitation and interaction-based design, often lack a principled account of why certain behaviours should occur in specific contexts. This dissertation argues that these limitations stem from the absence of a unified framework that links human reasons to automated-vehicle decision-making in a transparent and evaluable manner.

To address this challenge, the thesis adopts the philosophical framework of Meaningful Human Control (MHC), which requires that automated systems both track relevant human reasons and allow responsibility for outcomes to be meaningfully traced to human agents. While MHC has been widely discussed at a conceptual level, its technical operationalisation in automated driving remains underdeveloped. This dissertation advances MHC by translating its normative principles into an integrated framework that connects ethical reasoning, engineering implementation, and empirical evaluation.

The dissertation first investigates which human reasons are relevant for automated-vehicle manoeuvre planning in ethically ambiguous, everyday traffic situations. Empirical findings from interviews with AV experts show that such reasons are inherently multi-layered, context-dependent, and often simultaneous, spanning normative, strategic, tactical, and operational considerations. Rather than functioning as fixed values or isolated cost terms, human reasons are shown to form context-sensitive relationships between underlying motivations and expected vehicle behaviour. These insights provide an empirically grounded basis for structuring and prioritising human reasons in automated-vehicle decision-making.

Building on this foundation, the dissertation develops a technical approach for embedding human reasons within automated-vehicle control architectures. Human reasons are translated into formal, machine-readable representations by drawing on insights from human-factors research and are integrated through a supervisory evaluation layer that operates alongside existing motion planning and control frameworks. This approach enables transparent trajectory evaluation and adaptive behavioural adjustment without requiring the design of new controllers, thereby demonstrating a practical pathway for operationalising MHC in real-time decision-making systems.

Finally, the dissertation examines whether meaningful human control can be empirically assessed in practice. Qualitative studies with users of partially automated driving systems reveal how the tracking and tracing conditions of MHC manifest dynamically in drivers’ experiences of safety, trust, responsibility, and intervention readiness. Complementary simulator experiments show that objective behavioural telemetry can capture aspects of tracking at the level of concrete interaction events, while tracing cannot be inferred from behaviour alone. Together, these findings demonstrate that meaningful human control is not merely a normative or post-hoc concept, but an empirically observable property of ongoing human–automation interaction when evaluated through a multi-layer framework combining subjective perception and objective data.

Overall, this dissertation advances the technical operationalisation of meaningful human control by systematically linking human reasons, automated-vehicle decision-making, and empirical evaluation. The proposed framework provides researchers, designers, and policymakers with concrete tools to assess and support reason-aligned automated-vehicle behaviour, contributing to the development of automated driving systems whose behaviour is more transparent, context-sensitive, and reasonable in everyday traffic situations.