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Autonomous Driving Map (HD/LD/SD MAP, Online Reconstruction, Real-time Generative Map) Industry Report 2025
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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.

The evolution of autonomous driving maps:
Before 2022: The industry chain focused on HD maps that value geometric accuracy, while traditional ADAS algorithms relied on preset rules to process environmental perception;
2023-2024: With the development of mapless NOA, lightweight maps (LD maps) with topology, semantics and freshness were promoted and applied;
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.

Baidu MapAuto 6.5 can provide three types of data: SD (navigation maps), LD (lightweight autonomous driving maps) and HD (HD maps). In March 2025, Leapmotor released LEAP 3.5, which is a technical architecture equipped with the LD data of Baidu Maps.

Low-cost automated mapping is an important development direction of Baidu Maps, with core technologies including BEV static road scenario reconstruction and automated feature extraction.

Baidu's BEV static road scenario reconstruction uses Instance Query and Point Query similar to Huazhong University of Science and Technology's MapTR to detect road elements and element outline fixed points. It adopts a method similar to the Auto-regressive decoder in Tsinghua's VectorMapNet to output the topological relationship between feature points.

MapTR is suitable for real-time mapping of urban roads, L2+ ADAS, and embedded platforms with limited hardware resources. Its fixed-length setpoint output is convenient for connection with the planning and control module;
VectorMapNet is ideal for scenarios likemodeling of complex interchanges on highways, map generation research in the field of scientific research, and special scenarios that require variable-length fine modeling (such as construction areas).

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 functions of DWMs:
Environmental understanding: Accurately locate the vehicle position through autonomous driving maps and real-time perception data, and recognize key information such as lane lines, traffic signs, and obstacles.
Dynamic prediction: Predict the behavior trajectory of other traffic participants (vehicles, pedestrians), and predict potential risks (such as cutting in, sudden braking).
Global planning: Generate the optimal driving path and driving strategy based on long-term environment simulation (such as generalization of scenarios under different weather and road conditions).

Technical features of DWMs:
Continuously optimize the models based on data, continuous input of massive high-quality data and AI algorithms (such as deep learning and reinforcement learning).
Achieve closed-loop iteration and self-evolution of the models through a complete closed loop of data collection → model training → simulation verification → deployment optimization.
Integrate reality with virtuality and accelerate model generalization by combining simulation environments (such as digital twins) with real road test data.

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);
Multimodal fusion: Integrate multimodal data such as 2D images, 3D point clouds, and Occupancy grids to improve environmental modeling accuracy (such as 98.7% BEV geometric consistency in nuScenes data set tests);
Decision-making optimization: Achieve human-like driving capabilities through reinforcement learning and prediction, real difference fine-tuning (The measured traffic efficiency on Beijing's Fifth Ring Road increased by 28%).

Development trends of autonomous driving maps: OEMs explore and deploy NeRF technology in autonomous driving map reconstruction

At present, many OEMs have begun to explore or deploy NeRF technology in the field of autonomous driving maps, especially in dynamic scenario reconstruction and HD map generation.
NeRF technology can reconstruct 2D images into 3D scenarios, and then produce HD maps to achieve high-precision vehicle positioning and map matching;
NeRF technology can synthesize complex autonomous driving scenarios, enrich autonomous driving training data, and help autonomous driving systems perform efficient data enhancement;
NeRF technology can simulate harsh scenarios such as extreme weather and serious traffic accidents, and use simulated data to restore real harsh scenarios to improve the safety of autonomous driving

The AD Max 3.0 of Li Auto has built a triple perception architecture consisting of "static BEV + dynamic BEV + NeRF enhanced occupancy". By deeply combining NeRF technology with occupancy networks, it handles insufficient long-distance perception resolution which exists in traditional pure vision solutions:
Static BEV network: Transformer fuses data from multiple cameras to generate a bird's-eye view of the road structure. When some cameras fail, NeRF helps reconstruct the road edges and lane lines in the missing areas.
Dynamic BEV network: Thanks to the spatiotemporal attention mechanism tracking traffic participants and NeRF's spatiotemporal continuity modeling, the speed and acceleration estimation error of moving objects is less than 0.3m/s.
Occupancy network upgrade: The original Occupancy output resolution improves from 0.2m to 0.1m, sub-pixel details are generated through NeRF's radiation field rendering, and 30cm high curbstones and 5cm diameter manhole covers can be recognized

OEMs such as Xpeng, Mercedes-Benz, and Li Auto have taken the lead in mass production and application of NeRF technology, while Tesla, BMW, etc. are exploring deeper application through technical cooperation. In the future, with the improvement of hardware computing power (such as the Blackwell architecture) and open source ecology, NeRF is expected to become the underlying standard technology of autonomous driving maps, promoting the industry to evolve towards "real-time generative maps".

1 Definition and Classification of Autonomous Driving Maps
1.1 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

1.2 Classification of Autonomous Driving Maps (1): Navigation Maps (SD Maps)
Definition of Autonomous Driving Maps: Navigation Maps (SD Maps)
Installations of Navigation Maps (SD Maps) in Vehicles

1.3 Classification of Autonomous Driving Maps (2): 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
Classification of Lightweight Maps (LD Maps) 
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 (1)
Lightweight Map (LD Map) Solutions: Some Providers Build Maps Online via Algorithms (2)
Application Cases of Urban NOA Based on Lightweight Maps (LD Maps): QCraft's Urban NOA Adopts NavInfo HD Lite
Application Cases of Urban NOA Based on Lightweight Maps (LD Maps): MAXIEYE’s Automatic Mapping Memory
Installations of Lightweight Maps (LD Maps) in Vehicles (1)
Installations of Lightweight Maps (LD Maps) in Vehicles (2)

1.4 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 (1)
HD Map Development Path (2)
Application of HD Maps in "Light Map" Solutions
OEMs' Attitude towards HD Maps

1.5 Classification of Autonomous Driving Maps (4): New Technologies such as NeRF Online Reconstruction and Real-time Generative Maps
Application Trends of New Online Mapping Technologies (1)
Application Trends of New Online Mapping Technologies (2)
Application Trends of New Online Mapping Technologies (3)
Application Trends of New Online Mapping Technologies (4)

1.6 Classification of Autonomous Driving Maps (5): Evolution to DWMs
Summary of DWMs Worldwide as of January 2025
Technical Features of DWMs
Impact of DWMs on Autonomous Driving Maps (1)
Impact of DWMs on Autonomous Driving Maps (2)

1.7 Autonomous Driving Map Policies and Regulations
National Regulations (1)
National Regulations (2)
National Regulations (3)
Local Regulations (1)
Local Regulations (2)
Local Regulations (3)

2 Status Quo and Competitive Landscape of Autonomous Driving Map Market
2.1 Automotive Map Market Size
Global Automotive Map Market Size
Global Automotive Map Market for Passenger Cars and Commercial Vehicles
Global Automotive Map Market Landscape (by Type)
Global Automotive Map Market Landscape (by Region) 
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

2.2 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  (1): Domestic Map Providers (1)
Players in Autonomous Driving Map Market  (1): Domestic Map Providers (2)
Players in Autonomous Driving Map Market  (2): OEMs
Players in Autonomous Driving Map Market  (3): Foreign Map Providers
Layout Concept of Map Providers Driven by Urban NOA
Layout Strategy of Map Providers Driven by Urban NOA (1)
Layout Strategy of Map Providers Driven by Urban NOA (2)
Layout Strategy of Map Providers Driven by Urban NOA (3)

2.3 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 (1)
Summary of Autonomous Driving Map Business Models: Domestic Map Providers (2)
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

3 Trends and New Technology Application in Autonomous Driving Map Industry 
3.1 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 (1): SD Map Features Are the Key and Value Input
How to Access Intelligent Driving Maps in End-to-end Technology (2): Initial Query Input

3.2 Autonomous Driving Map Reconstruction: Automatic Annotation System and Video Clips 
Automatic Annotation System (Tesla as an Example)
Pavement Reconstruction Process (1)
Pavement Reconstruction Process (2)
Pavement Reconstruction Process (3)
Automatic Annotation Can Solve the Occlusion Problem of Moving Objects

3.3 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

3.4 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

3.5 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

3.6 Autonomous Driving Map Reconstruction: 3D Gaussian Splashing
Autonomous Driving Algorithms Need "Intermediate Expression Maps"
3D Gaussian Splashing (Intermediate Expression Maps for Autonomous Driving) (1)
3D Gaussian Splashing (Intermediate Expression Maps for Autonomous Driving) (2)

4 Autonomous Driving Map Application and Technology Layout of OEMs
4.1 Demand for Maps in Different Autonomous Driving Scenarios
Main Application Scenarios of Autonomous Driving Maps
Main Application Scenarios of Autonomous Driving Maps: Demand of Passenger Car NOA for Autonomous Driving Maps 
Main Application Scenarios of Autonomous Driving Maps: Demand of Autonomous Passenger Cars (L3/L4) for Autonomous Driving Maps 
Main Application Scenarios of Autonomous Driving Maps: Demand of Passenger Cars with Low-speed Automated Parking for Autonomous Driving Maps 
Main Application Scenarios of Autonomous Driving Maps: Demand of Unmanned Cargo Transport for Autonomous Driving Maps 

4.2 OEMs’ Choice of Autonomous Driving Maps
OEMs’ Choice of Autonomous Driving Maps (1)
OEMs’ Choice of Autonomous Driving Maps (2)
OEMs’ Choice of Autonomous Driving Maps (3)
OEMs’ Choice of Autonomous Driving Maps (4)
OEMs’ Choice of Autonomous Driving Maps (5)

4.3 Installations of Autonomous Driving Maps by OEMs
Installations of Intelligent Driving Maps in Production Passenger Cars of Independent Brands (1)
Installations of Intelligent Driving Maps in Production Passenger Cars of Independent Brands (2)
Installations of Intelligent Driving Maps in Production Passenger Cars of Independent Brands (3)
...................
Installations of Intelligent Driving Maps in Production Passenger Cars of Independent Brands (10)
Installations of Intelligent Driving Maps in Production Passenger Cars of Independent Brands (11)
Installations of Intelligent Driving Maps in Production Passenger Cars of Independent Brands (12)
Installations of Intelligent Driving Maps in Production Passenger Cars of Joint Venture Brands (1)
Installations of Intelligent Driving Maps in Production Passenger Cars of Joint Venture Brands (2)

4.4 Tesla
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: “End-to-end” Technology Route
Autonomous Driving Software: Algorithm Iteration
Autonomous Driving Software:  Perception Technology of Occupancy Networks
Autonomous Driving Software: Pure Visual Solutions (1)
Autonomous Driving Software: Pure Visual Solutions (2)
Real-time Construction and Updates of HD Maps with AI Technology
FSD Uses SD Maps (1)
FSD Uses SD Maps (2)

4.5 Xiaomi  
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
SU7 Uses HD Maps as Safety Redundancy
Autonomous Driving Maps: From HD Maps to End-to-end
End-to-end Foundation Models Use a "Three-layer Modeling" Architecture to Build Physical World Models
End-to-end Foundation Models Use a "Three-layer Modeling" Architecture
Data Closed Loop: Physical World Modeling 

4.6 Xpeng
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
XNGP Is Upgraded to a "Mapless" Solution (1)
Autonomous Driving Software: Next-generation Perception Architecture - “X Net”
XNGP Is Upgraded to a "Mapless" Solution (2)
XNGP Is Upgraded to a "Mapless" Solution (3)
Autonomous Driving Software: Self-developed Fully Automatic Annotation System Based on XNet
HD Map Solutions
Autonomous Driving Software: Cloud Foundation Models  
Cloud Training Base: "World Base Model" R&D (1)
Cloud Training Base: "World Base Model" R&D (2)
Cloud Training Base: "World Base Model" R&D (3)
Cloud Training Base: "World Base Model" R&D (4)
Cloud Training Base: "World Base Model" R&D (5)

4.7 Li Auto
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
AD Max 3.0 Is Upgraded to a "Mapless" Solution
Online Mapping Technology (1)
Online Mapping Technology (2)
Closed Loop Simulation System (1)
Closed Loop Simulation System (2)
Closed Loop Simulation System (3)

4.8 NIO
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Navigation World Models (NWMs) (1)
Autonomous Driving Software: Navigation World Models (NWMs) (2)

4.9 Harmony Intelligent Mobility Alliance (HIMA)
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: ADS 4.0 (1)
Autonomous Driving Software: ADS 4.0 (2)
Autonomous Driving Software: Features of ADS 3.0 (1)
Autonomous Driving Software: Features of ADS 3.0 (2)
Autonomous Driving Software: ADS SE 
Autonomous Driving Software: Comparison between ADS SE and ADS (Advanced Version)
Autonomous Driving Software: Mapless Solutions
Autonomous Driving Software: Petal Maps
Autonomous Driving Software: Mapless Solutions
Autonomous Driving Software: Petal Maps
AI Technology Application: Automotive World Behavior Models

4.10 SAIC IM
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Cooperate Deeply with Momenta in NOA
Autonomous Driving Software: IM AD 3.0 (1)
Autonomous Driving Software: IM AD 3.0 (2)
Autonomous Driving Software: IM AD 3.0 (3)
Autonomous Driving Software: "Production-ready" Robotaxi 3.0
HD Map Application
Online Mapping Technology

4.11 Leapmotor
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Automotive Autonomous Driving Software: LEAP 3.5 
Gradual Evolution towards Light Map Solutions
Low-cost Map Solutions
Latest Application Dynamics of Baidu LD Maps: Access to LEAP 3.5  

4.12 Geely & ZEEKR
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Pan-World Models
Autonomous Driving Software: G-Pilot
Autonomous Driving Software: Multimodal Foundation Models
Autonomous Driving Software: G-AES 
Autonomous Driving Software: SEA 2.0 (1)
Autonomous Driving Software: SEA 2.0 (2)
Autonomous Driving Software: Zeekr×Mapbox Real-time Cloud Navigation System

4.13 Dongfeng Voyah
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Intelligent Driving Solutions Based on Navigation Maps (SD Maps)
Autonomous driving software: Baidu Maps V20 Visual Lane-level Navigation
Intelligent Driving Map Application
Application of Intelligent Driving Maps in Dongfeng Forthing StarSea

4.14 Changan Automobile
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Dubhe Intelligent Driving (1)
Autonomous Driving Software: Dubhe Intelligent Driving (2)
Autonomous Driving Software: Dubhe Intelligent Driving (3)
Avita’s Autonomous Driving Software: Huawei Petal Maps

4.15 Chery
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Chery STERRA's Intelligent Driving Solution Tends to “Get Rid of Maps”
Autonomous Driving Software: Intelligent Driving Software Business Layout and Planning
Autonomous Driving Software: Technical Features of Chery Pilot 4.0 
Autonomous Driving Software: Falcon Intelligent Driving Series (1)
Autonomous Driving Software: Falcon Intelligent Driving Series (2)
Autonomous Driving Software: Falcon Intelligent Driving Series (3)
Autonomous Driving Software: Falcon Intelligent Driving Series (4)

4.16 Great Wall Motor
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Coffee Pilot Ultra 
Autonomous Driving Software: SEE End-to-end Foundation Models
Autonomous Driving Software: AutoNavi Map × Great Wall Motor Joint Mobility Innovation Lab   

4.17 GAC Motor
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Five Major Intelligent Driving Platforms
Autonomous Driving Software: VLA
Autonomous Driving Software: Perception Algorithm of ADiGO PILOT 
Autonomous Driving Software: "Edge-cloud" Light Map Solutions
"Mapless Intelligent Driving" Solutions Relying on Navigation Maps (SD Maps)
Aion's HD Map Solution
Aion's Electronic Vision System
Aion's HD Map Curvature and Slope
Online Mapping Patent Application

4.18 Volkswagen
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Self-developed High-Level AI Intelligent Driving System
Autonomous Driving Software: Product Route for "Smart Driving Equality" (1)
Autonomous Driving Software: Product Route for "Smart Driving Equality" (2)
Autonomous Driving Software: Product Route for "Smart Driving Equality" (3)
Autonomous Driving Software: Product Route for "Intelligent Driving Equality" (4)

4.19 Mercedes-Benz
Evolution of Autonomous Driving Software and Map Solutions
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Autonomous Driving Software Development Model
Autonomous Driving Software: L2++ "Mapless" Advanced Intelligent Driving

4.20 BMW
Autonomous Driving Software Solution and Supply Chain Construction
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: Features of L3 Personal Pilot  
Autonomous Driving Software: L2+ and L3 Autonomous Driving Systems
Autonomous Driving Software: Intelligent Driving Planning for BMW Vision Neue Klasse 

4.21 Toyota
Autonomous Driving Software Solution and Supply Chain Construction
Online Map Construction and Real-time Generative Map Layout
Autonomous Driving Software: All-scenario Intelligent Driving of bZ3X 
Autonomous Driving Software: L4 Autonomous Driving Evolution

5 Autonomous Driving Map Providers
5.1 Baidu Maps
Committed to Building Maps Suitable for Autonomous Driving
Automotive Map System
Automotive Maps (1): Navigation Maps (SD Maps) 
Automotive Maps (1): Navigation Maps (SD Maps) V21 Is Upgraded to Intelligent Driving Navigation
Automotive Maps (2): Autonomous Driving Maps (1)
Automotive Maps (2): Autonomous Driving Maps (2)
Automotive Maps (2): The First Intelligent Parking Navigation System Seamlessly Connects to Parking Spaces
Automotive Maps (3): HD Maps 
Maps Are One of the Core Competitiveness of Autonomous Driving Systems
Core Value of “Familiar Route Mode” (1): Safety
Core Value of “Familiar Route Mode” (2): Comfort
Core Value of “Familiar Route Mode” (3): Efficiency
Low-cost Intelligent Driving Map Construction Technology (1): Map Construction
Low-cost Intelligent Driving Map Construction Technology (1): BEV Static Road Scenario Reconstruction (1)
Low-cost Intelligent Driving Map Construction Technology (1): BEV Static Road Scenario Reconstruction (2)
Low-cost Intelligent Driving Map Construction Technology (2): Automated Feature Extraction
Compared with HD Maps, Autonomous Driving Maps Are Less Burdensome
The Latest Application of HD Maps: Access to Tesla
The Latest Application of LD Maps: Access to LEAP 3.5

5.2 NavInfo
Transformation of Autonomous Driving Map Business Models
Redefined Role of Autonomous Driving Maps: From Automotive Charging to Training  
Redefined Role of Autonomous Driving Maps: Safe Redundant Configuration
Automotive Map System
Automotive Maps (1): Navigation Maps (SD Maps)
Automotive Maps (2): Scenario Maps (1)
Automotive Maps (2): Scenario Maps (2)
Automotive Maps (3): HD Maps (1)
Automotive Maps (3): HD Maps (2)
Automotive Maps (3): HD Maps (3)
Automotive Maps (3): HD Maps (4)

5.3 AutoNavi (amap.com)
Automotive Maps (1): Navigation Maps (SD Maps)
Automotive Maps (2): The Latest Technological Progress of Autonomous Driving Maps
Automotive Maps (2): HQ Live MAP
Automotive Maps (2): All-domain Lane-level Navigation Installed on NIO ET9
Automotive Maps (3): HD Maps
Matching between HD Maps and SD Maps

5.4 Tencent
Automotive Maps (1): Navigation Maps (SD Maps)
Automotive Maps (2): Autonomous Driving Maps (Intelligent Driving Cloud Maps)
Automotive Maps (2): Autonomous Driving Maps (Intelligent Driving Maps)
Automotive Maps (2): Autonomous Driving Maps (Intelligent Driving Maps 8.0)
Automotive Maps (3): HD Maps
Application of Automotive Maps in Urban NOA: Horizon Continental Technology’s L2+ Intelligent Driving Solution -  Astra

5.5 Lange Technology
Intelligent Driving Map System
Competitive Advantages in Intelligent Driving Maps
Four-layer Intelligent Driving Map Model
Data Intelligence System with Weekly Updates
Intelligent Driving Map Mass Production and Delivery Solutions

5.6 EMG
Automotive Map Layout: Technology-driven + Ecological Binding
Automotive Maps (1): Parking Lot HD Maps (1)
Automotive Maps (1): Parking Lot HD Maps (2)
Automotive Maps (2): Autonomous Driving Maps (Vehicle-road-cloud Integrated Maps)
Automotive Maps (3): HD Map Cloud Platforms
Automotive Map Application: Autonomous Driving Simulation Testing

5.7 MXNAVI
Business Layout
Industrial Qualifications
Intelligent Driving Solutions in Urban Areas: Vehicle-cloud Integrated Route Memory
Automotive Maps (1): Crowd-source Map Technology
Automotive Maps (1): Progress of Crowd-source Map Platforms
Automotive Maps (1): Crowd-source Map Platforms Empower Urban NOA
Automotive Maps (1): Application Effect of Crowd-source Map Platforms   
Automotive Maps (2): HD Map Fusion Platforms

5.8 Leador
Autonomous Driving Technology Based on HD Maps
Application of Parking Lot HD Maps: Changan Automobile

5.9 Heading Data Intelligence
Map-based Product Line
Automotive Maps (1): HD Map Data
Automotive Maps (2): HD Map Engines

5.10 BrightMap
Automotive Maps (1): AVP HD Maps (1)
Automotive Maps (2): AVP HD Maps (1)

5.11 Huawei
Automotive Maps (1): Navigation Maps (SD Maps)
Automotive Maps (2): Online Mapping 
Automotive Maps (3): Autonomous Driving Map Data
Automotive Map Application: ADS 

5.12 Roadgrids Technology
Automatic Construction and Updates of Light HD Maps
Trade-offs of Light HD Map Elements
Light Map Closed-loop Solutions (1)
Light Map Closed-loop Solutions (2)

5.13 Mapbox
Automotive Maps: Navigation Maps (SD Maps)
Automotive Maps: HD Maps

5.14 Kuandeng Technology
 "Automotive Crowd-sourced Update" Solutions
"Roadside Crowd-sourced Update" Solutions
HD Lite Maps
Solutions for Matching between HD Maps and SD Maps: vehicles
Solutions for Matching between HD Maps and SD Maps: Cloud 

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