Modelling and Implementation Approaches for Spatiotemporal Data Governance of Intelligent Connected Vehicles

YUE Peng; LIU Xiaoxue; LIU Ruixiang; CAI Chuanwei; HUANG Long; LI Jian; ZHANG Qingyu; JIANG Min

doi:10.13203/j.whugis20240353

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Modelling and Implementation Approaches for Spatiotemporal Data Governance of Intelligent Connected Vehicles

Cartography and GIS | 更新时间：2026-11-05

- Modelling and Implementation Approaches for Spatiotemporal Data Governance of Intelligent Connected Vehicles
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 4, Pages: 775-788(2026)
- 作者机构：
  
  1.武汉大学遥感信息工程学院，湖北武汉，430079
  2.重庆长安汽车股份有限公司，重庆，400023
  3.智能汽车安全技术全国重点实验室，重庆，400023
  4.湖北珞珈实验室，湖北武汉，430079
  5.自然资源部地图技术审查中心，北京，100830
  6.中汽智联技术有限公司，天津，300300
  7.深圳市迪派乐智图科技有限公司，广东深圳，518118
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240353
  CLC： P208
- Received：07 January 2025，
  
  Published：05 April 2026
- 稿件说明：
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乐鹏,刘晓雪,刘瑞祥，等.智能网联汽车时空数据监管建模与实施方法研究[J].武汉大学学报（信息科学版）,2026,51(4):775-788.

YUE Peng,LIU Xiaoxue,LIU Ruixiang,et al.Modelling and Implementation Approaches for Spatiotemporal Data Governance of Intelligent Connected Vehicles[J].Geomatics and Information Science of Wuhan University,2026,51(4):775-788.
乐鹏,刘晓雪,刘瑞祥，等.智能网联汽车时空数据监管建模与实施方法研究[J].武汉大学学报（信息科学版）,2026,51(4):775-788. DOI：10.13203/j.whugis20240353

YUE Peng,LIU Xiaoxue,LIU Ruixiang,et al.Modelling and Implementation Approaches for Spatiotemporal Data Governance of Intelligent Connected Vehicles[J].Geomatics and Information Science of Wuhan University,2026,51(4):775-788. DOI：10.13203/j.whugis20240353

摘要

在车路云一体化探索与汽车智能化的发展趋势下，智能网联汽车（intelligent connected vehicle

ICV）时空数据作为关键数据要素，已成为驱动国家和汽车行业实现产业升级与技术转型的重要新质生产力。时空数据高度敏感，涉及国家地理信息安全，开展面向多源异构、动态流通的ICV时空数据监管工作成为迫切需要。目前尚未有针对时空数据监管的模型框架，缺少机理研究。从现行数据监管模型出发，拓展到ICV领域，提出了以时空数据监管对象、监管主体、监管环境为核心实体的数据监管模型。面向时空数据全生命周期的各个环节，阐述了溯源关联的合规监管实施参考框架。借助行业具有代表性的时空数据监管案例，在监管模型的指导下，提出了多种监管实施选项，能够为中国ICV时空数据监管提供建议和参考。

Abstract

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.

关键词

Keywords

references

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