This study examines how vulnerability is produced for human operators of Tesla's Full Self-Driving (FSD), a Level 2 semi-autonomous vehicle (SAV) system, by applying Florencia Luna's layered vulnerability framework. While existing road safety models conceptualize vulnerability as a fixed attribute of external road users, emerging evidence suggests that semi-autonomous vehicle operators themselves experience dynamic and situational vulnerability as they supervise automated systems that they do not fully control. To investigate this phenomenon, we conducted semi-structured interviews with 17 active FSD users, analyzing their accounts through a combined deductive-inductive coding process aligned with Luna's framework. Findings reveal three interacting layers of operator vulnerability, namely psychological, operational, and social. Vulnerability emerged not from any single layer but from how these layers converged in specific situations, creating fluctuating supervisory demands and uneven capacity to recognize and manage risk. The findings extend debates on contextual trust calibration, automation complacency, and meaningful human control by demonstrating how factors commonly treated as liabilities such as trust or informal learning, can both increase and mitigate vulnerability depending on context. This analysis determines the need for design and regulatory interventions that address psychological, operational, and social conditions together rather than in isolation, and highlights how responsibility is implicitly shifted onto individual operators within inadequately supported supervisory regimes.

翻译：本研究通过应用弗洛伦西亚·卢纳的分层脆弱性框架，探讨了特斯拉全自动驾驶系统（一种二级半自动驾驶车辆系统）如何为其人类操作者产生脆弱性。现有道路安全模型将脆弱性概念化为外部道路使用者的固定属性，而新兴证据表明，半自动驾驶车辆操作者在监督其无法完全控制的自动化系统时，自身亦经历动态且情境化的脆弱性。为探究此现象，我们对17名活跃的全自动驾驶用户进行了半结构化访谈，并通过结合演绎与归纳的编码程序（与卢纳框架保持一致）分析了他们的叙述。研究结果揭示了操作者脆弱性的三个相互作用层面：心理层、操作层和社会层。脆弱性并非源于单一层面，而是来自这些层面在特定情境中的交汇，从而产生波动的监督需求及不均衡的风险识别与管理能力。研究结果通过论证通常被视为不利因素（如信任或非正式学习）如何依据情境既可加剧亦可缓解脆弱性，拓展了关于情境化信任校准、自动化自满及有意义人类控制的讨论。本分析表明，需要采取设计与监管干预措施，以协同而非孤立的方式应对心理、操作和社会条件，并揭示了在支持不足的监督机制中责任如何被隐性地转移至个体操作者。