Autonomous vehicles (AVs) are characterized by pervasive datafication and surveillance through sensors like in-cabin cameras, LIDAR, and GPS. Drawing on 16 semi-structured interviews with AV drivers analyzed using constructivist grounded theory, this study examines how users make sense of vehicular surveillance within everyday datafication. Findings reveal drivers demonstrate few AV-specific privacy concerns, instead normalizing monitoring through comparisons with established digital platforms. We theorize this indifference by situating AV surveillance within the `surveillance ecology' of platform environments, arguing the datafied car functions as a mobile extension of the `leaky home' -- private spaces rendered permeable through connected technologies continuously transmitting behavioral data. The study contributes to scholarship on surveillance beliefs, datafication, and platform governance by demonstrating how users who have accepted comprehensive smartphone and smart home monitoring encounter AV datafication as just another node in normalized data extraction. We highlight how geographic restrictions on data access -- currently limiting driver log access to California -- create asymmetries that impede informed privacy deliberation, exemplifying `tertiary digital divides.' Finally, we examine how machine learning's reliance on data-intensive approaches creates structural pressure for surveillance that transcends individual manufacturer choices. We propose governance interventions to democratize social learning, including universal data access rights, binding transparency requirements, and data minimization standards to prevent race-to-the-bottom dynamics in automotive datafication.

翻译：自动驾驶汽车（AVs）通过车内摄像头、激光雷达和GPS等传感器实现普遍的数据化与监控。本研究基于对16名自动驾驶汽车驾驶员进行的半结构化访谈，采用建构主义扎根理论进行分析，探讨用户如何在日常数据化背景下理解车辆监控。研究发现，驾驶员对自动驾驶汽车并无特别的隐私担忧，反而通过将其与成熟的数字平台进行对比，将监控行为常态化。我们将自动驾驶汽车的监控置于平台环境的“监控生态”中加以理论化，认为数据化汽车是“渗漏之家”的移动延伸——即通过持续传输行为数据的互联技术，使私人空间变得可渗透。本研究通过展示已接受智能手机和智能家居全面监控的用户如何将自动驾驶汽车数据化视为常态化数据提取的另一个节点，为监控认知、数据化及平台治理研究提供了学术贡献。我们强调，当前对数据访问的地理限制（如仅限加州访问驾驶员日志）造成了信息不对称，阻碍了知情隐私决策，这体现了“三级数字鸿沟”。最后，我们探讨了机器学习对数据密集型方法的依赖如何形成超越单个制造商选择的结构性监控压力。为此，我们提出通过普及数据访问权、具有约束力的透明度要求及数据最小化标准等治理干预措施，以民主化社会学习，防止汽车数据化领域的“竞次”动态。