Objectives While exploring the vehicle-road-cloud integration and automotive intelligence, it turns out that spatiotemporal data of intelligent connected vehicle (ICV) has emerged as a crucial data element in driving productivity growth and technological transformation. Spatiotemporal data is highly sensitive, multi-source, and heterogeneous. They are dynamically circulated among multiple stakeholders, including automotive enterprises, map providers, and intelligent driving solution vendors. ICV spatiotemporal data implicates both user privacy and information security. It is often necessary to set up governance mechanism for ICV spatiotemporal data. Currently there is still no modelling framework for spatiotemporal data governance, and the systematic governance mechanism is still missing. Both theoretical and practical guidance is needed for policymakers and industry stakeholders.
Methods A data governance model for ICV spatiotemporal data is proposed. It follows a meta-modelling approach and is compatible with the governance model from the general information domain. The model identifies three core entities that are the spatiotemporal data and processing as governance objects, governance bodies, and governance environment. It also defines six fundamental relationships among core entities that are body delegation, object derivation, environment cascading, dependency payload, regulation control, and monitoring evaluation. By leveraging the governance model and provenance that enables lineage tracking across data processing workflows, we present a reference framework for governance implementation across various stages of ICV spatiotemporal data lifecycle, which cover from spatiotemporal data collection, processing, storage, transmission, usage, sharing, cross-border transfer, and destruction. An empirical study is further conducted by analyzing three representative governance cases from the ICV industry, each reflecting distinct configurations of governance bodies and relationships.
Results Applying the proposed governance model to the ICV domain yields three actionable recommendations for the governance of ICV spatiotemporal data: (1) Governance cooperation between automotive enterprises and authorized map service providers is needed. The option leverages the existing qualifications and regulatory experience of certified map providers to offer automotive enterprises a quick way to meet governance requirements. (2) Automotive enterprises should implement independent governance. The option provides flexibility for automotive enterprises to build spatiotemporal data governance workflow, yet it requires the automotive enterprises to get authorization for map production and create their own map infrastructure. (3) National third-party governance is needed. The option allows the government to authorize third-parties to enforce governance, and create a national spatial data infrastructure for ICV to support the governance. This can help avoid redundant investments across the industry. These recommendations offer enterprises a range of options to select approaches aligned with their specific operational contexts, technical capabilities, and strategic priorities, which pave the way for more inclusive and effective governance approaches.
Conclusions The proposed model and reference framework comprehensively account for diverse stakeholders and practical scenarios within the ICV governance ecosystem. It provides a theoretical foundation for developing systematic governance mechanisms for ICV spatiotemporal data. The findings also suggest that the development of a national crowd-sourced mapping infrastructure covering maps and driving scene libraries presents a promising direction for advancing both data governance and automated driving capabilities. It will facilitate coordinated technological development and provide a sustainable mechanism for data sharing across the automotive industry.
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