发表机构
Capgemini Engineering; University of Oxford; University of the West of England（凯捷工程; 牛津大学; 西英格兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出并验证了扩展STAMP的心理安全系统理论框架，开发AV-PsySafe危险分析方法，经真实场景验证可有效评估自动驾驶汽车心理风险，为以人为本的AV开发提供支撑。
AI 中文摘要
尽管技术进步迅速，自动驾驶汽车（AV）的社会接受度仍受限于超出传统物理安全担忧的心理障碍。虽然信任和感知安全等因素会影响用户接受度已为人知，但目前缺乏系统化识别、评估和缓解人机交互产生的心理风险的形式化机制与工程方法。为解决这一缺口，本研究提出并验证了一种用于评估自动驾驶汽车中心理安全的系统理论框架。首先，定义了一个综合心理安全风险模型，扩展了系统理论事故模型与过程（STAMP），以纳入信任、感知控制、可预测性和感知支持等关键心理构建。基于该模型，开发了一种危险分析方法（AV-PsySafe），用于系统化识别心理危险、不安全控制行为和损失场景，同时引入心理安全完整性等级（PsySIL）以支持风险优先级排序。其次，通过在真实自动驾驶汽车场景中部署该框架，评估其适用性与相关性。实施了结构化验证方法，包括方法指南、标准化分析模板和分析师反馈收集。结果表明，从业者可一致应用该框架，生成关于心理风险的有意义见解。总体而言，本研究为协同评估自主系统中心理与物理安全的统一方法奠定了理论基础并验证了其可行性，推动更以人为本、更值得信赖的自动驾驶汽车开发。
英文摘要
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.
