Table of Content

1 Overview of Automotive Cloud Services
1.1 Overview of Automotive Cloud Service Industry
1.1.1 Definition of Automotive Cloud
1.1.2 China’s Automotive Cloud Market Size
1.1.3 Classification of Automotive Cloud Platforms
1.1.4 Automotive Public Cloud Platforms in China
1.2 Main Types of Automotive Cloud Services
1.3 Competition Landscape of Automotive Cloud Services
1.4 Automotive Cloud Business Models in China
1.5 Application Scenarios of Automotive Cloud


2 Automotive Cloud Solutions
2.1 Autonomous Driving Cloud
2.1.1 Requirements of Autonomous Driving for Cloud: Cloud Services Support Autonomous Driving
2.1.1 Requirements of Autonomous Driving for Cloud: Cloud Services Support Simulation Testing
2.1.2 Application Scenarios of Autonomous Driving Cloud
2.1.3 Cloud Service + End-to-End Intelligent Driving: Case 1
2.1.3 Cloud Service + End-to-End Intelligent Driving: Case 2
2.1.4 Autonomous Driving Cloud Platform: Realizing Three Types of Functions
2.1.5 Example of Autonomous Driving Cloud Service Provider: AWS
2.1.5 Example of Autonomous Driving Cloud Service Provider: Huawei Cloud
2.2 Telematics Cloud
2.2.1 Application Scenarios of Telematics Cloud
2.2.2 Requirements of Telematics for Cloud: Monitoring, Early Warning, Diagnosis and Rescue
2.2.2 Requirements of Telematics for Cloud: Facilitating OTA Process Management
2.2.3 Example of Telematics Cloud Service Providers: Tencent Cloud
2.2.3 Example of Telematics Cloud Service Providers: PATEO
2.3 V2X Cloud
2.3.1 Overview of V2X Cloud
2.3.2 V2X Cloud Service Architecture: General Architecture
2.3.2 V2X Cloud Service Architecture: Segmented Architecture
2.3.3 In-vehicle Cloud Computing: Six Service Contents
2.3.3 In-vehicle Cloud Computing: Pain Points and Solutions
2.3.4 Example of V2X Cloud Service Providers: Baidu Cloud
2.3.4 Example of V2X Cloud Service Providers: SenseAuto
2.4 Digital Transformation
2.4.1 Overview of Digital Transformation
2.4.2 Requirements of Digital Transformation for Cloud
2.5 Cloud Data Closed Loop
2.5.1 Overview of Data Closed Loop
2.5.2 The Role of Cloud Platform in Data Closed Loop: Promoting Data Migration to the Cloud
2.5.2 The Role of Cloud Platform in Data Closed Loop: Reducing Costs and Increasing Efficiency
2.5.2 The Role of Cloud Platform in Data Closed Loop: Computing Power Requirements
2.5.3 Cloud Platform Data Closed Loop Case: AWS Cloud
2.5.3 Cloud Platform Data Closed Loop Case: Baidu Cloud
2.5.3 Cloud Platform Data Closed Loop Case: Volcano Engine
2.5.3 Cloud Platform Data Closed Loop Case: Alibaba Cloud
2.5.3 Cloud Platform Data Closed Loop Case: SAIC
2.6 AI + Cloud Services
2.6.1 Application Scenarios of AI + Cloud Service
2.6.2 Reference Architecture ofAI Intelligent Cloud
2.6.3 Application of AI in IaaS, PaaS, and MaaS
2.6.4 Integration of AI Cloud Computing and Intelligent Computing
2.6.5 Cloud AI Accelerator
2.6.6 Cooperative Deployment of AI Cloud and Devices
2.7 Cloud Information Security
2.7.1 Telematics Security Challenges
2.7.2 Cloud Security Scenarios
2.7.3 Cloud Information Threats
2.7.4 Cloud Information Security Architecture
2.7.5 Cloud Security Strategy: Cloud WAF
2.7.5 Cloud Security Strategy: Container Security
2.7.5 Cloud Security Strategy: Cloud Host Security
2.7.5 Cloud Security Strategy: Cloud Identity Management
2.7.5 Cloud Security Strategy: Micro-isolation
2.7.6 Typical Case of Cloud Security: Qi An Xin Technology
2.7.6 Typical Case of Cloud Security: Topsec
2.7.6 Typical Case of Cloud Security: VecenTek
2.7.6 Typical Case of Cloud Security: Infosec Technologies
2.8 SOA Cloud
2.8.1 Cloud Native in SOA
2.8.2 SOA Cloud Case 1 (Continental)
2.8.2 SOA Cloud Case 2 (Qualcomm)


3 Cloud Platform Infrastructure
3.1 Automotive Cloud Industry Chain
3.2 Data Centers
3.2.1 Distribution of Data Centers in China
3.2.2 Data Center Layout of Cloud Platform Companies
3.2.3 Supercomputing Centers
3.3 Cloud Servers
3.4 Server Chips
3.4.1 Server Chip Technology Route
3.4.2 Server Chip Vendors
3.5 Progress of Cloud Providers in Self-development of Chips
3.5.1 AWS’ Self-developed Chips
3.5.2 Google’s Self-developed Chips
3.5.3 Alibaba’s Self-developed Chips
3.5.4 Baidu’s Self-developed Chips: Architecture of Kunlunxin
3.5.4 Baidu’s Self-developed Chips: Cloud Scenario of Kunlunxin


4 Automotive Public Cloud Platforms
4.1 Amazon Cloud - AWS
4.1.1 Introduction
4.1.2 Regional Distribution
4.1.3 Automotive Industry Layout
4.1.4 AWS for Automotive
4.1.5 Software-Defined Vehicle Solutions
4.1.6 Telematics Data Lake
4.1.7 Autonomous Driving Data Lake
4.1.8 Automotive Customers
4.1.9 Supply Relationship (2024 Summary)
4.1.10 Cooperation Case: Audi
4.1.10 Cooperation Case: BMW
4.1.10 Cooperation Case: Continental
4.1.10 Cooperation Case: HERE
4.1.10 Cooperation Case: ABUP
4.1.10 Cooperation Case: ThunderSoft
4.1.10 Cooperation Case: 51World

4.2 Microsoft Cloud - Azure
4.2.1 Azure Automotive Solutions
4.2.2 Azure Telematics Cloud Platform
4.2.3 Microsoft Connected Vehicle Platform (MCVP) Service: Business Model and Main Customers
4.2.4 Microsoft Connected Vehicle Platform (MCVP) Service: Ecosystem Partners
4.2.5 Cooperated with Ericsson Connected Vehicle Cloud (CVC)
4.2.6 Ericsson CVC Solution
4.2.7 NVIDIA AI Cloud Server Azure Solution
4.2.8 Cooperative Auto Parts Suppliers
4.2.9 Cooperative OEMs

4.3 Google Cloud
4.3.1 Google Cloud Platform (GCP)
4.3.2 Latest Dynamics

4.4 Huawei Automotive Cloud
4.4.1 Introduction
4.4.2 Automotive Solutions
4.4.3 Telematics Solution
4.4.4 Autonomous Driving Development Solution
4.4.5 Autonomous Driving Cloud Service: Qiankun 3.0
4.4.5 Autonomous Driving Cloud Service: Xinghe AI Cloud
4.4.6 Foundation Model Solution
4.4.7 Mobility Solution
4.4.8 Automotive Simulation Solution
4.4.9 Digital Intelligent Platform Solution
4.4.10 Digital Marketing Solution
4.4.11 Overseas Business Solutions
4.4.12 Customers (1)
4.4.12 Customers (2)

4.5 Baidu Automotive Cloud
4.5.1 Introduction
4.5.2 3.0 Architecture
4.5.3 Autonomous Driving Solution: Model Training Acceleration
4.5.3 Autonomous Driving Solution: Simulation
4.5.3 Autonomous Driving Solution: Intelligent Driving Data Platform
4.5.4 Baidu Telematics Cloud
4.5.5 Baidu V2X Cloud
4.5.6 Data Closed-Loop Solution
4.5.7 Data Annotation Solution
4.5.8 Security System

4.6 Alibaba Automotive Cloud
4.6.1 Introduction
4.6.2 Industry Capabilities
4.6.3 Technical Bases: Apsara Platform
4.6.3 Technical Bases: Apsara + CIPU
4.6.3 Technical Bases: Intelligent Computing Platform
4.6.3 Technical Bases: Intelligent Computing Center
4.6.4 Main Customers: Momenta
4.6.4 Main Customers: Xpeng Motors
4.6.5 Telematics Security Solution: Cloud-Network-Terminal Integrated Defense

4.7 Tencent Automotive Cloud
4.7.1 Introduction
4.7.2 Architecture: A New Generation of Data Closed Loop
4.7.3 Autonomous Driving Cloud
4.7.4 Intelligent Connected Cloud
4.7.5 Capabilities
4.7.6 Ecosystem
4.7.7 Security Mechanism
4.7.8 OEM Customers

4.8 ByteDance Automotive Cloud
4.8.1 Introduction
4.8.2 System Architecture
4.8.3 Ecosystem
4.8.4 ByteDance’s Cloud Computing Capabilities
4.8.5 Volcano Engine Multi-Cloud Disaster Tolerance Architecture: Traffic Scheduling Solution
4.8.5 Volcano Engine Multi-Cloud Disaster Tolerance Architecture: Traffic Scheduling Solutions for Access and Application Layers

4.9 NVIDIA Cloud Service Supporting
4.9.1 Omniverse Cloud
4.9.2 Cooperation Case


5 OEM Cloud Platform Layout

OEM Solution Comparison (1) - (3)

5.1 Geely
5.1.1 Cloud Platform Strategy
5.1.2 Digital Transformation Strategic Planning
5.1.3 Corporate Cloud Platform
5.1.4 Corporate Cloud Platform Solution and Planning
5.1.5 Xingrui Intelligent Computing Center
5.1.6 Intelligent Driving Cloud Data Factory
5.1.7 Cooperation Case with Tencent Cloud
5.1.8 Cooperation Case with Qiniu Cloud
5.1.9 Cooperation Case with Huawei Cloud
5.1.10 Cooperation Case between Zeekr and Alibaba Cloud

5.2 Xpeng Motors
5.2.1 Cloud Platform
5.2.2 Fuyao Intelligent Computing Center

5.3 Li Auto
5.3.1 Cloud Platform Layout
5.3.2 End-to-End Intelligent Driving Cloud World Model
5.3.3 Telematics Cloud
5.3.4 Data Storage Solution

5.4 NIO
5.4.1 Hybrid Cloud
5.4.2 Energy Cloud
5.4.3 Autonomous Driving Cloud

5.5 FAW
5.5.1 FAW Group’s Cloud Platform Layout
5.5.2 FAW Hongqi Intelligent Cloud
5.5.3 FAW Group Local Data Center
5.5.4 Cooperation Case between FAW and Huawei Cloud
5.5.5 Cooperation Case between FAW and Alibaba Cloud
5.5.6 Case Study of Cooperation between FAW and Baidu Cloud
5.5.7 FAW Work Cloud Platform - Beidou Cloud

5.6 Changan
5.6.1 Digitalization Path: Cloud Stage
5.6.1 Digitalization Path: Digital Management Stage
5.6.1 Digitalization Path: Enlightenment Stage
5.6.2 Cloud Platform Big Data
5.6.3 Intelligent Vehicle Cloud Big Data Processing Architecture
5.6.4 Telematics Cloud and R&D Cloud
5.6.5 Terminal-Cloud Integrated SDA Architecture
5.6.6 Terminal-Cloud Integrated Service Ecosystem
5.6.7 Intelligent Vehicle Cloud Platform
5.6.8 Cloud Platform Partners
5.6.9 Changan and Tencent Cloud: Telematics Hybrid Cloud and Supercomputing Center
5.6.9 Changan and Tencent Cloud: Cooperation History
5.6.10 Changan and Huawei Cloud: Industrial Internet Cloud

5.7 GWM
5.7.1 Intelligent Cloud
5.7.2 GWM & Huawei Cloud

5.8 SAIC
5.8.1 Cloud Business Layout
5.8.2 Cloud Products and Services
5.8.3 Cloud Platform: Overall Architecture
5.8.4 Cloud Platform: Features and Advantages
5.8.5 SAIC Autonomous Driving Cloud
5.8.6 Data Flow of SAIL-Cloud Combined with Cloud Foundation Model
5.8.7 Intelligent Connected Cloud of SAIL-Cloud
5.8.8 Cooperation Case of SAIL-Cloud
5.8.9 Cloud Product Technology and Security Route
5.8.10 Overseas Cooperation with AWS

5.9 GAC
5.9.1 Cooperate with Tencent on Telematics Cloud
5.9.2 Cooperate with Tencent on Intelligent Driving Cloud
5.9.3 Cooperate with ByteDance on Digital Cloud


6 Summary and Trends
6.1 Significance of OEMs’ Migration to Cloud
6.1.1 Cloud Platform Is the Foundation of Digitization of OEMs
6.1.2 Significance of OEMs’ Migration to Cloud (1)
6.1.3 Significance of OEMs’ Migration to Cloud (2)
6.1.4 Significance of OEMs’ Migration to Cloud (3)
6.1.5 Significance of OEMs’ Migration to Cloud (4)
6.2 Cloud Service Demand Trends
6.2.1 Development Path of Cloud Services in China
6.2.2 Changes in Demand for Cloud Services: Characteristics
6.2.2 Changes in Demand for Cloud Services: AI Foundation Model
6.2.2 Changes in Demand for Cloud Services: Multi-Cloud Environment
6.2.3 Summary of Cloud Capabilities Demanded by OEMs (1):
6.2.3 Summary of Cloud Capabilities Demanded by OEMs (2):
6.2.3 Summary of Cloud Capabilities Demanded by OEMs (3): Deep Integration of Cloud Platform Tool Chain
6.3 OEM and Supplier Cooperation Trends
6.3.1 Cloud Application of OEMs
6.3.2 Automotive Cloud Business Model
6.3.3 OEMs’ Strategy for Selecting Cloud Service Providers
6.4 Cloud Computing Architecture Trends
6.4.1 Cloud Computing Architecture Moves Towards Software and Hardware Integration
6.4.2 E/E Architecture of Automotive Cloud Computing
6.5 Cloud Native Changes Software Development Methods
6.5.1 Cloud Native Changes Software Development Methods: Vehicle-Cloud Collaboration
6.5.1 Cloud Native Changes Software Development Methods: Main Technologies and Advantages
6.5.1 Cloud Native Changes Software Development Methods: Application Scenarios
6.5.2 Data Lake + Cloud Native to Build a New Storage and Computing System
6.5.3 Cloud Native Security Evolution
6.5.4 Supplier’s Cloud Native Application Case: Alibaba Cloud
6.5.4 Supplier’s Cloud Native Application Case: Tencent Cloud
6.5.4 Supplier’s Cloud Native Application Case: Huawei Cloud
6.5.5 OEM’s Cloud Native Application Case: NIO (1)-(4)
6.5.5 OEM’s Cloud Native Application Case: GWM (1)-(8)
6.5.5 OEM’s Cloud Native Application Case: FAW
6.5.5 OEM’s Cloud Native Application Case: Xpeng Motors (1)-(2)
6.5.5 OEM’s Cloud Native Application Case: Li Auto
6.5.6 OEM’s Cloud Native Application Case: Summary
6.6 Terminal-Cloud Integration
6.6.1 Terminal-Cloud Integration (1)
6.6.2 Terminal-Cloud Integration (2)
6.7 Cloud Service Hardware Infrastructure Trends
6.7.1 Cloud Service Hardware Infrastructure Trends (1)
6.7.2 Cloud Service Hardware Infrastructure Trends (2)