The Race for Autonomous Driving Data Dominance: Key Countries and Corporate Strategies


Whoever Controls Driving Data, Dominates Autonomous Driving

Overview of Autonomous Driving Big Data

The competition among global companies to secure dominance in the future automotive industry is intensifying. To ensure safety in autonomous driving environments, companies utilize various sensors to collect data, emphasizing the importance of real-time large-scale data analysis. The quantity and quality of data are critical for enhancing AI-based autonomous driving vehicle perception, decision-making, and control performance.

Types of Data in Autonomous Driving

Autonomous driving vehicles generate two major types of data:

  • Structured Data (Numerical): Distance traveled, travel time, speed, traffic volume, GPS coordinates, etc.

  • Unstructured Data (Images, Videos): Data collected from cameras, 2D/3D LiDAR, radar, and other sensors.

The autonomous driving market is experiencing rapid growth. Global autonomous vehicle markets are projected to expand from approximately $7.1 billion in 2020 to $1 trillion by 2035 (KPMG, 2020). Likewise, the automotive big data market is expected to grow from approximately $3.6 billion in 2020 to $7.8 billion by 2025 (Mordor Intelligence, 2020). Particularly, the Asia-Pacific region is expected to see the fastest growth in the autonomous driving data market, doubling its size from 2020 to 2025.

To advance autonomous driving R&D, high-precision (HD) mapping data and driving data are as crucial as sensor and communication technologies. Leading firms like Tesla and Google’s Waymo are enhancing their self-driving algorithms by leveraging extensive driving data.

National Policies and Initiatives

Key countries such as the U.S. and the EU are establishing autonomous driving data-sharing platforms to enhance technology development. From 2017 to 2020, major global economies launched AI national strategies, reinforcing policies for driving data sharing and trading. These policies aim to stimulate the ecosystem by promoting data accessibility and utilization.

Global Policies for Autonomous Driving Data Sharing

  1. United States

    • Program: Autonomous Driving Data Sharing Initiative (Launched in late 2018)

    • Description: Sharing driving and accident data, along with traffic infrastructure and sign information.

  2. European Union

    • Program: European Common Data Space (Started in February 2020)

    • Description: Data-sharing initiative across nine industries, including mobility, with a focus on platform and standardization.

  3. United Kingdom

    • Program: UK Autonomous Driving Data Trading Project (Launched in early 2020)

    • Description: Government-led collaboration among academia and industries to facilitate data trading, collection, processing, and infrastructure provision.

  4. China

    • Program: Baidu's Apollo Open-Source Platform (Started in April 2017)

    • Description: A coalition of 50 global companies sharing driving data and open-source code.

  5. Japan

    • Program: METI’s Autonomous Driving Data Utilization Framework (Launched in 2017)

    • Description: Government and industry partnerships for data-sharing infrastructure development.

South Korea’s Data-Sharing Strategies

South Korea is also actively developing autonomous driving data-sharing initiatives across three government departments:

  1. Ministry of SMEs and Startups: Establishing a data-sharing platform for regulatory sandbox trials, such as the Sejong Regulatory Free Zone pilot project since 2019.

  2. Ministry of Land, Infrastructure, and Transport: Utilizing real-world road tests in C-ITS projects (Daejeon-Sejong corridor) and operating a data-sharing center for autonomous driving AI training.

  3. Ministry of Science and ICT: Running the AI Hub’s “Data Dam” initiative to provide 23 types of public datasets for autonomous driving R&D.

Corporate Data Collection and Utilization Trends

Global companies are forming strategic alliances to advance autonomous driving technology.

  • Google Waymo collaborates with UC Berkeley to share autonomous driving ecosystem data. Given the numerous environmental and safety variables in real-world driving, diverse and extensive data collection is crucial.

  • Tesla and GM leverage sales of driver-assist-feature-equipped vehicles to collect vast amounts of real-world driving data, which they continuously use for software updates and improvements.

  • Both companies follow a strategy of "Vehicle Sales → Data Collection → Technology Enhancement" to accelerate performance improvement and commercialization.

Competitive Landscape: Tesla vs. Waymo

The competition for autonomous driving data dominance is led by Tesla and Google’s Waymo. While Waymo has focused on controlled test environments and high-definition maps since its R&D began in 2009, Tesla has prioritized real-world driving data collection.

  • Waymo: Primarily focuses on testing C-ITS and HD Map-based driving, while gradually deploying robotaxi services in select cities, such as Phoenix, Arizona.

  • Tesla: Since 2014, Tesla has continuously updated its Full Self-Driving (FSD) option and Autopilot feature, leveraging data from over 700,000 vehicles worldwide to enhance accuracy.

Why Data Sharing is Essential

As seen in global cases, driving data is indispensable for advancing autonomous driving technology. Training AI for autonomous driving and ensuring safety require extensive driving footage and high-precision map data. Without adequate data, achieving higher autonomy levels (e.g., Level 2 to Level 3) is significantly challenging.

Since autonomous driving requires significant investment and has high barriers to entry regarding data and infrastructure, joint efforts from leading companies and government support are necessary. South Korean SMEs and startups face difficulties in competing with global giants due to limited access to large-scale data.

By enhancing government support and fostering industry collaboration, Korea can leverage its technological strength and establish a competitive autonomous driving ecosystem. South Korea has already been recognized as one of the top five startup nations (World Economic Forum, 2020), with its Autonomous Driving Readiness Index ranked 7th (KPMG, 2020). With proper data infrastructure and policy support, Korea's potential in this field is immense.

Conclusion: Establishing a Data-Driven Ecosystem

Data-sharing initiatives can facilitate a virtuous cycle where increased data utilization → advanced R&D → innovative applications drive the ecosystem forward. Reducing barriers to entry in data access and infrastructure can empower domestic companies, fostering technological innovation and new industry applications.

South Korea’s AI Hub Data Dam project, which provides 23 types of autonomous driving datasets, and the Korea Startup Promotion Agency’s challenge programs for startups to leverage shared data are steps in the right direction. However, more extensive efforts are needed to strengthen Korea’s position in the global autonomous driving race.

Autonomous driving markets, like other Fourth Industrial Revolution technologies, follow a winner-takes-all model. To maintain competitiveness, South Korea must strategically nurture its domestic autonomous driving ecosystem through robust data policies and infrastructure development.

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About the Author: Drytree

About the Author: Drytree (Jaehoon Lee) I am a senior researcher specializing in deep tech startups, technology commercialization, and R&BD strategy at a national research institute in South Korea. With a deep curiosity about the rapid evolution of technology and its impact on society, I have been writing under the pen name Drytree since 2021, aiming to make complex technological trends more accessible and foster informed discussions on innovation and policy. My academic background is diverse, spanning Political Science (B.A.), International Politics (M.A.), and currently, a Ph.D. in Technology Management & Entrepreneurship . I have published 13 academic papers in domestic and international journals, covering topics such as patent analysis, technology security, and industrial policy . Books & Publications I have authored several books exploring technology, startups, and geopolitical implications of innovation , including: Elon Musk’s DOGE: The Dawn of a New Economic Exper...
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