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.