China and Global Autonomous Driving Map Industry Research Report 2025 | Focus Of Competition in the Autonomous Driving Map Industry Shifts to Comprehensive Capabilities under Urban NOA - ResearchAndMarkets.com

The "Autonomous Driving Map (HD/LD/SD MAP, Online Reconstruction, Real-time Generative Map) Industry Report 2025" report has been added to ResearchAndMarkets.com's offering.

Research on Autonomous Driving Maps: Evolve from Recording the Past to Previewing the Future with 'Real-time Generative Maps'

'Mapless NOA' has become the mainstream solution for autonomous driving systems. This solution reduces the reliance on offline HD maps whose development has encountered challenges. The so-called 'mapless' essentially means the shift from 'map prior' to 'real-time map construction' and then further development into 'world models', while ADAS algorithms tend to be 'data-driven' instead of being 'rule-driven'.

A mapless solution, very similar to the early SLAM technology, actually builds a vector map online and then matches it with offline LD maps to obtain positioning and navigation information at the same time. The early SLAM technology relied heavily on LiDAR. As BEV emerges, SLAM technology has been gradually eliminated, but it is still used in scenarios such as underground parking lots.

After 2025: With the introduction of new technologies such as 3D Gaussian sputtering and NeRF (Neural Radiance Fields), autonomous driving maps will 'preview the future" instead of only 'recording the past'. 'World models' extract spatiotemporal patterns from massive driving data through self-supervised learning, integrate multimodal sensor data (cameras, LiDAR, etc.) and real-time crowd-source data, build a dynamically updated environmental knowledge base, and accomplish online reasoning of road topology, semantic information and traffic rules.

'World models' leverage historical scenario information and preset conditions to predict the future changes in intelligent driving scenarios and the response of the ego vehicle.

Development trends of autonomous driving maps: Low-cost automated mapping, application of vectorized HD map construction technologies such as MapTR and VectorMapNet

Baidu MapAuto 6.5 is the first 3D lane-level map and all-scenario human-machine co-driving map in China, providing comprehensive data services. Baidu MapAuto 6.5, based on Baidu's integrated data collection vehicles, multi-source data input (closed loop of automotive and roadside data), and map generation foundation models with billions of parameters, has improved the efficiency of map production exponentially, effectively supported the rapid updates of Baidu map data, and offered powerful and comprehensive data services.

Development trends of autonomous driving maps: Integration with driving world models (DWMs)

NavInfo has proposed to add the spatiotemporal cognition capability of maps to the intelligent driving technology driven by world models, that is, 'let world models inherit the spatiotemporal cognition of maps' - 'Maps have evolved from static layers to dynamic data engines that are indispensable in the world-model-driven stage. They are irreplaceable 'prior sensors' in application scenarios such as improving the intelligence level of a single vehicle, reducing computing power constraints and responding to emergency warnings.'

DWMs are the core components of the next-generation autonomous driving systems. By predicting the spatiotemporal evolution of dynamic driving scenarios, they help vehicles perceive the environment more accurately, understand interaction logic, and optimize decision-making.

DWMs build continuous learning and prediction capabilities for the physical world by integrating HD map data, real-time sensor information (such as cameras, LiDAR), vehicle status data (such as speed, steering), and external environment data (such as traffic flow, weather). The goal is to enable autonomous driving systems to secure the trinity of 'understanding, prediction, and planning' through a closed data loop.

Core value of DWMs:

Scenario deduction: Generate the physical rationality and spatiotemporal consistency of future scenarios based on historical observations, and support autonomous driving systems to predict potential risks (such as bizarre accidents (for example, when there is a vehicle or obstacle blocking the view ahead, a non-motorized vehicle or pedestrian suddenly jumps out from the roadside, and the driver fails to avoid it in time, often causing an accident), dynamic changes in construction areas).

Key Topics Covered:

Definition and Classification of Autonomous Driving Maps

• Definition and Classification of Autonomous Driving Maps
• Definition of Autonomous Driving Maps
• Autonomous Driving Maps Evolve from Recording the Past to Previewing the Future with 'Real-time Generative Maps'
• Evolution of Autonomous Driving Algorithms and Map Construction, 2020-2026E
• Classification of Autonomous Driving Maps: Navigation Maps (SD Maps)
• Definition of Autonomous Driving Maps: Navigation Maps (SD Maps)
• Installations of Navigation Maps (SD Maps) in Vehicles
• Classification of Autonomous Driving Maps: Lightweight Maps (LD Maps)
• Definition of Lightweight Maps (LD Maps)
• Lightweight Maps (LD Maps) Are Required to Provide Basic Data for "Mapless" Intelligent Driving Solutions
• Development of Lightweight Maps (LD Maps): Integration of SD Maps and HD/LD Maps
• Lightweight Map (LD Map) Solutions: Map Providers Reduce Costs and Increase Update Frequency
• Lightweight Map (LD Map) Solutions: Some Providers Build Maps Online via Algorithms
• QCraft's Urban NOA Adopts NavInfo HD Lite
• MAXIEYE's Automatic Mapping Memory
• Installations of Lightweight Maps (LD Maps) in Vehicles
• Classification of Autonomous Driving Maps (3): HD Maps
• Definition of Autonomous Driving Maps: HD Maps
• Complementarity between HD Maps and Perception Can Improve the Safety of Urban NOA
• HD Map Development Path
• Application of HD Maps in 'Light Map' Solutions
• OEMs' Attitude towards HD Maps
• Classification of Autonomous Driving Maps: New Technologies such as NeRF Online Reconstruction and Real-time Generative Maps
• Application Trends of New Online Mapping Technologies
• Classification of Autonomous Driving Maps: Evolution to DWMs
• Summary of DWMs Worldwide as of January 2025
• Technical Features of DWMs
• Impact of DWMs on Autonomous Driving Maps
• Autonomous Driving Map Policies and Regulations
• National Regulations
• Local Regulations

Status Quo and Competitive Landscape of Autonomous Driving Map Market

• Automotive Map Market Size
• Global Automotive Map Market Size
• Global Automotive Map Market for Passenger Cars and Commercial Vehicles
• Global Automotive Map Market Landscape
• Map Installations of Chinese Passenger Cars by Autonomous Driving Level (by Price Range), 2023-2024
• Autonomous Driving Level of Chinese Passenger Cars, 2024-2030E
• SD/LD/HD Map Installations of Chinese Passenger Cars, 2024-2030E
• SD/LD/HD Map Market Size for Chinese Passenger Cars, 2024-2030E
• Autonomous Driving Level of Chinese Passenger Cars by Autonomous Driving Level, 2024-2030E
• Competitive Landscape of Automotive Map Market
• Competitive Landscape of Chinese Urban NOA Map Market for Passenger Cars, 2024
• Major Players in Autonomous Driving Map Market
• Players in Autonomous Driving Map Market: Domestic Map Providers
• Players in Autonomous Driving Map Market: OEMs
• Players in Autonomous Driving Map Market: Foreign Map Providers
• Layout Concept of Map Providers Driven by Urban NOA
• Layout Strategy of Map Providers Driven by Urban NOA
• Changes in Business Models of Autonomous Driving Map Providers amid the Trend of Urban NOA
• Classification of Autonomous Driving Map Business Models
• Summary of Autonomous Driving Map Business Models: Domestic Map Providers
• Summary of Autonomous Driving Map Business Models: Foreign Map Providers
• The Focus Of Competition in the Autonomous Driving Map Industry Shifts to Comprehensive Capabilities under Urban NOA
• Changes in Business Models of Map Suppliers amid the Development of Urban NOA

Trends and New Technology Application in Autonomous Driving Map Industry

• Evolution of Intelligent Driving Maps amid the End-to-end Trend
• Maps Are the Carriers of Standardized Location Data
• Integration of Maps and Scenarios in Intelligent Driving
• Evolution of Intelligent Driving Maps: Solutions with Maps VS Solutions without Maps
• Evolution of Intelligent Driving Maps: Advantages of Mapless Solutions
• The Value of Intelligent Driving Maps Is Re-evaluated amid the End-to-end Trend
• How to Access Intelligent Driving Maps in End-to-end Technology: SD Map Features Are the Key and Value Input
• How to Access Intelligent Driving Maps in End-to-end Technology: Initial Query Input
• Autonomous Driving Map Reconstruction: Automatic Annotation System and Video Clips
• Automatic Annotation System (Tesla as an Example)
• Pavement Reconstruction Process
• Automatic Annotation Can Solve the Occlusion Problem of Moving Objects
• Autonomous Driving Map Reconstruction: Application of NeRF Technology
• Application of NeRF in Autonomous Driving Includes Perception, 3D Reconstruction, Positioning and Map Construction, etc.
• NeRF's Application Potential in Autonomous Driving: Data Enhancement
• NeRF's Application Potential in Autonomous Driving: Model Training
• NeRF's Application Potential in Autonomous Driving: SLAM
• Technical Comparison between NeRF Static Maps and Dynamic Generative Maps
• The Combined Application of NeRF and Generative Maps Brings the Best Solution
• HD Map Technology Evolution: NeRF Reconstruction and Real-time Generative Map Application Will See a Turning Point in 2027-2028
• Accelerated Application of NeRF in Autonomous Vehicles
• Autonomous Driving Map Reconstruction: Voxel NeRF Produces MV-Map
• MV-Map Can Significantly Improve the Quality of HD Maps
• MV-Map Framework
• MV-Map Production Steps
• Autonomous Driving Map Reconstruction: 4D Spatiotemporal Features
• Application of 4D Spatiotemporal Features in Autonomous Driving: Combined with Intelligent Driving Maps to Improve Prediction Capabilities
• DriveWorld: a 4D Spatiotemporal Pre-training Algorithm for Autonomous Driving
• Application of 4D Spatiotemporal Features in Vehicles
• Autonomous Driving Map Reconstruction: 3D Gaussian Splashing
• Autonomous Driving Algorithms Need 'Intermediate Expression Maps
• D Gaussian Splashing (Intermediate Expression Maps for Autonomous Driving)

Autonomous Driving Map Application and Technology Layout of OEMs

• Tesla
• Xiaomi
• Xpeng
• Li Auto
• NIO
• SAIC IM
• Leapmotor
• Geely & ZEEKR
• Dongfeng Voyah
• Changan Automobile
• Chery
• Great Wall Motor
• GAC Motor
• Volkswagen
• Mercedes-Benz
• BMW
• Toyota

Autonomous Driving Map Providers

• Baidu Maps
• NavInfo
• Tencent
• Lange Technology
• EMG
• MXNAVI
• Leador
• BrightMap
• Huawei
• Roadgrids Technology
• Mapbox
• Kuandeng Technology
• HD Lite Maps

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