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This paper introduces a systems-theoretic framework for assessing psychological safety in autonomous vehicles (AVs), addressing the psychological barriers that hinder societal acceptance of AV technology. By extending the Systems-Theoretic Accident Model and Processes (STAMP) to include psychological constructs like trust and perceived control, the authors develop a hazard analysis method called AV-PsySafe, which identifies psychological hazards and prioritizes risks through a Psychological Safety Integrity Level (PsySIL). Validation in real-world scenarios shows that this framework can be effectively applied by practitioners, yielding actionable insights into psychological risks associated with human-AV interactions.
A novel framework reveals that psychological safety risks in autonomous vehicles can be systematically assessed and prioritized, bridging the gap between human factors and technology.
Despite rapid technological advances, the societal acceptability of autonomous vehicles (AVs) remains limited by psychological barriers that extend beyond traditional concerns of physical safety. While factors such as trust and perceived safety are known to influence user acceptance, there is a lack of formalized mechanisms and engineering methods to systematically identify, assess, and mitigate psychological risks arising from human-AV interactions. To address this gap, this work proposes and validates a systems-theoretic framework for the assessment of psychological safety in autonomous vehicles. First, a comprehensive psychological safety risk model is defined, extending the Systems-Theoretic Accident Model and Processes (STAMP) to incorporate key psychological constructs such as trust, perceived control, predictability, and perceived support. Based on this model, a hazard analysis method (AV-PsySafe) is developed to systematically identify psychological hazards, unsafe control actions, and loss scenarios, while introducing a Psychological Safety Integrity Level (PsySIL) to support risk prioritization. Second, the applicability and relevance of the framework are evaluated through its deployment in realistic autonomous vehicle scenarios. A structured validation approach is implemented, including a methodological guide, standardized analysis templates, and the collection of analyst feedback. The results demonstrate that the framework can be consistently applied by practitioners, producing meaningful insights into psychological risks. Overall, this work establishes both the theoretical foundations and practical feasibility of a unified approach to co-assessing psychological and physical safety in autonomous systems, contributing to more human-centred and trustworthy AV development.
