The development of technologies has prompted a paradigm shift in the automotive industry, with an increasing focus on connected services and autonomous driving capabilities. This transformation allows vehicles to collect and share vast amounts of vehicle-specific and personal data. While these technological advancements offer enhanced user experiences, they also raise privacy concerns. To understand the ecosystem of data collection and sharing in modern vehicles, we adopted the ontology 101 methodology to incorporate information extracted from different sources, including analysis of privacy policies using GPT-4, a small-scale systematic literature review, and an existing ontology, to develop a high-level conceptual graph-based model, aiming to get insights into how modern vehicles handle data exchange among different parties. This serves as a foundational model with the flexibility and scalability to further expand for modelling and analysing data sharing practices across diverse contexts. Two realistic examples were developed to demonstrate the usefulness and effectiveness of discovering insights into privacy regarding vehicle-related data sharing. We also recommend several future research directions, such as exploring advanced ontology languages for reasoning tasks, supporting topological analysis for discovering data privacy risks/concerns, and developing useful tools for comparative analysis, to strengthen the understanding of the vehicle-centric data sharing ecosystem.

翻译：技术的发展促使汽车行业发生范式转变，日益关注互联服务和自动驾驶能力。这一转型使得车辆能够收集和共享大量车辆特定数据与个人数据。尽管这些技术进步提供了增强的用户体验，但也引发了隐私担忧。为理解现代车辆中的数据收集与共享生态系统，我们采用本体论101方法，整合了从不同来源提取的信息——包括使用GPT-4进行隐私政策分析、小规模系统性文献综述以及现有本体——构建了一个高层次的概念性基于图模型，旨在深入洞察现代车辆如何处理不同参与方之间的数据交换。该模型作为基础模型，具有灵活性和可扩展性，可进一步扩展用于建模和分析不同情境下的数据共享实践。我们开发了两个现实案例，以证明该方法在发现车辆相关数据共享隐私洞见方面的实用性和有效性。我们还提出了若干未来研究方向，例如探索用于推理任务的高级本体语言、支持拓扑分析以发现数据隐私风险/问题，以及开发用于比较分析的有效工具，从而深化对以车辆为中心的数据共享生态系统的理解。