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汽車雲服務平台市場：2023 年行業報告
市場調查報告書
商品編碼
1337773

汽車雲服務平台市場：2023 年行業報告

Automotive Cloud Service Platform Industry Report, 2023
出版日期: | 出版商: ResearchInChina | 英文 260 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

從企業的角度來看，數字化轉型的目標是實現汽車全生命週期的所有流程要素數字化，包括研發、生產、銷售、運營、售後服務等，將服務器、機房中的數據上傳到企業雲，打通各環節的數據通道，對全產業鏈數據進行一體化管理，逐步實現雲-管-端一體化實時互聯，為用戶打造跨越行業全生命週期的服務運營模式，加強行業上下游合作夥伴的聯繫，創造更大的價值。

在產品方面，車輛智能化和網聯化正在蓬勃發展。例如，從L2開始，自動駕駛能力每發展到一個更高的水平，雲基礎設施平台、應用程序和服務的消耗就會增加幾個數量級。隨著高度自動駕駛進入量產，車輛傳感器數量和數據量不斷增加，本地處理難以滿足要求。因此，遷移到雲端是最好的選擇。

汽車製造商每年花費數千萬元建設雲服務，這對市場產生了巨大的推動作用。2022年，中國汽車雲服務市場規模將超過150億元，預計未來五年將保持30-40%的增速。

2021年，ByteDance將推出 "ByteDance Auto Cloud" ，提供數字營銷、智能座艙、自動駕駛、車輛服務四個細分領域的雲服務。2022年，Tencent Intelligent Cloud Cloud、Baidu Auto Cloud、Alibaba Auto Cloud上線。Baidu, Alibaba, Tencent, Huawei, Douyin五巨頭（BATHD）紛紛入局，加劇了基於汽車專用雲的汽車雲服務競爭。

本報告調查了汽車雲服務平台市場，並提供了市場概述，以及雲解決方案、平台基礎設施趨勢、需求趨勢、商業模式和未來趨勢。

目錄

第一章 汽車雲服務概述

  • 汽車雲服務行業概況
  • 汽車雲服務主要類型
  • 汽車雲服務競爭格局
  • 中國汽車雲商業模式
  • 汽車雲發展機遇
  • 汽車雲應用場景

第二章 汽車雲解決方案

  • 自動駕駛雲
  • 遠程信息處理
  • V2X雲
  • 數字化轉型
  • 雲數據閉環
  • 雲信息安全

第三章 雲平台基礎設施

  • 汽車雲產業鏈
  • 數據中心
  • 雲服務器
  • 服務器芯片
  • 雲提供商在內部芯片開發方面取得進展

第四章 汽車公有雲平台

  • Amazon Cloud-AWS
  • Microsoft Cloud-Azure
  • Google Cloud
  • Huawei Auto Cloud
  • Baidu Auto Cloud
  • Alibaba Auto Cloud
  • Tencent Auto Cloud
  • ByteDance Auto Cloud

第5章 OEM雲平台佈局

  • Geely
  • Xpeng
  • Li Auto
  • NIO
  • FAW
  • Changan
  • Great Wall Motor
  • SAIC

第六章 概述與趨勢

  • 雲遷移對汽車製造商的重要性
  • 雲服務需求趨勢
  • 汽車雲應用和商業模式
  • 雲計算架構的趨勢
  • 數據湖和雲原生
  • 其他趨勢
簡介目錄
Product Code: YSJ119

Research on Automotive Cloud Services: As Dedicated Automotive Cloud Platforms Are Launched, the Market Enters A Phase of Differentiated Competition

1. The exponentially increasing amount of vehicle data makes cloud migration an inevitable choice.

From the perspective of companies, the goals of digital transformation are to digitize all elements of the whole process throughout the full life cycle of vehicles, including R&D, production, sale, operation, and after-sales service; upload the data in the local servers and computer rooms of automakers to the cloud; connect the data channels of each link to gradually realize the integrated management of data in the whole industry chain, and the cloud-pipe-terminal integrated real-time interconnection; and build service operation models that span the full life cycle of users to enhance the connections between upstream and downstream partners in the industry and create greater value.

In terms of products, vehicle intelligence and connectivity are booming. For example, starting from L2, every time the autonomous driving functions evolve to a higher level, the consumption of cloud infrastructure platforms, applications, and services will rise by an order of magnitude. As high-level autonomous driving comes into mass production, the number of vehicle sensors and the amount of data multiply, making it difficult for local processing to meet the requirements. Cloud migration thus will be the best choice.

Automakers spend tens of millions of yuan every year building cloud services, which gives a big boost to the market. In 2022, China's automotive cloud service market was valued at over RMB15 billion, and it is expected to sustain the growth rate of 30-40% in the next five years.

2. As dedicated automotive cloud platforms are launched, differentiated competition becomes crucial.

In 2021, ByteDance announced the "ByteDance Auto Cloud", which will provide cloud services in four segments: Digital Marketing, Intelligent Cockpit, Autonomous Driving, and Vehicle Services. In 2022, Tencent Intelligent Cloud Cloud, Baidu Auto Cloud, and Alibaba Auto Cloud became available. All the five giants (BATHD), i.e., Baidu, Alibaba, Tencent, Huawei and Douyin have stepped in the market, and the competition in automotive cloud services built on exclusive automotive cloud has become fiercer.

The service scope of each automotive cloud is much of a muchness, generally covering R&D, manufacture, marketing, and supply chain. The support for R&D is concentrated in the fields of autonomous driving, intelligent cockpit, telematics, and "three electrics" (battery, motor and ECU). How to gain differentiated competitive edges in the competition therefore has become the key to success for companies.

3. The differentiated competitive edges in cloud services are mainly built from two aspects: basic resource layer services and upper-layer R&D tool chains.

In terms of basic resource layer, supercomputing centers are an important indicator for assessing service capabilities, and Alibaba and Baidu are the first to deploy.

In August 2022, Alibaba Cloud launched the two intelligent supercomputing centers located in Zhangbei County and Ulanqab, with total compute of 15 EFLOPS (15 exascale floating-point operations per second). At the same time, Alibaba Cloud also introduced the "Apsara Intelligent Computing Platform", an intelligent computing solution which opens up intelligent computing capabilities by way of "platform + intelligent computing center".

Following the five intelligent computing centers in Yangquan, Jinan, Fuzhou, Yancheng, and Tianjin, Baidu Cloud started construction of the Baidu AI Cloud-Shenyang Intelligent Computing Center in May 2023, a project with a land area of about 2.4 hectares, floor areas of 42,000 square meters, and the total planned computing power of 500P, 200P for Phase I. In the future, Baidu Shenyang Intelligent Computing Center will not only involve physical data center construction capabilities and intelligent computing infrastructure capabilities, but also comprehensive solutions for AI software and hardware ecosystem capabilities such as foundation models, supporting the computing tasks of companies in different business scenarios and meeting the industrial application requirements of foundation models in the era of intelligent computing.

With regard to R&D tool chains, cloud service providers are committed to creating "fully furnished" service experiences for users by offering "full-process" and "fully closed-loop" services.

  • In Tencent's autonomous driving cloud platform, virtual simulation has become a key link.
  • Huawei's autonomous driving cloud platform "Octopus" has built in a dataset with 20 million frame annotations, a library with 200,000 simulation scenes, a complete tool chain, and annotation algorithms, covering the full life cycle businesses such as autonomous driving data, models, training, simulation, and annotation, and helping automakers to build autonomous driving development capabilities on a "zero" basis.
  • Baidu makes a full-stack layout and enables a data closed loop by virtue of from chip (Kunlunxin), deep learning (PaddlePaddle) and training foundation model (ERNIE) to search (Baidu Search), cloud platform (Baidu AI Cloud), autonomous driving (Apollo) and intelligent connection (Xiaodu).

4. Under the multi-cloud strategy, the need of OEMs has changed from the pursuit of resources to efficiency.

With the in-depth migration to the cloud, the resource needs of OEMs for cloud migration have been overall met, and thus the underlying logic of the cloud strategy of companies has changed from the pursuit of resources to efficiency to finally improve their overall digitization capabilities in production and operation. In this process, OEMs are no longer tightly bound with some cloud platform, but implement a multi-cloud strategy where different business types are put on different cloud platforms.

Examples include:

  • Based on the "1+6+N" Geely Hybrid Cloud Platform co-built with Baidu, Geely works with Alibaba to build the Xingrui Intelligent Computing Center, and teams up with Tencent on telematics and security solutions.
  • FAW Group uses Huawei Cloud Stack to build hybrid cloud architecture, and also cooperates with Alibaba Cloud on intelligent manufacturing, digital marketing and other businesses.
  • Without a doubt, the multi-cloud strategy offers benefits. It can integrate the advantages of various cloud platforms, enable refined business deployment, and reduce costs for companies, and also helps automakers to gain the core initiative in building cloud platforms and avoid being puzzled by the "soul" dispute. Yet the challenges of the multi-cloud strategy are also unavoidable. How to allocate storage/computing power among multiple clouds, cross-cloud data synchronization's dependency on bandwidth, and whether costs and network delays will have an impact are all urgent problems to be solved. Hence how to formulate a multi-cloud strategy is a problem for OEMs.

Table of Contents

1 Overview of Automotive Cloud Service

  • 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
    • 1.1.5 Competitive Landscape of Automotive Cloud Platforms in China
  • 1.2 Main Types of Automotive Cloud Services
    • 1.2.1 China's Automotive Cloud Market Size by Type
    • 1.2.2 Competitive Landscape of Automotive Cloud Services by Type in China
  • 1.3 Competitive Landscape of Automotive Cloud Services
  • 1.4 Automotive Cloud Business Models in China
  • 1.5 Development Opportunities for Automotive Cloud
  • 1.6 Application Scenarios of Automotive Cloud

2 Automotive Cloud Solutions

  • 2.1 Autonomous Driving Cloud
    • 2.1.1 China's Autonomous Driving Market
    • 2.1.2 Requirements of Autonomous Driving for Cloud
    • 2.1.3 Examples of Autonomous Driving Cloud Service Providers
  • 2.2 Telematics Cloud
    • 2.2.1 China's Telematics Market
    • 2.2.2 Requirements of Telematics for Cloud
    • 2.2.3 Examples of Telematics Cloud Service Providers
  • 2.3 V2X Cloud
    • 2.3.1 Overview of V2X CLOUD
    • 2.3.2 Examples of V2X Cloud Service Providers
  • 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
    • 2.5.3 Cloud Platform Data Closed Loop Cases
  • 2.6 Cloud Information Security
    • 2.6.1 Telematics Security Challenges
    • 2.6.2 Cloud Information Threats
    • 2.6.3 Cloud Information Security Architecture
    • 2.6.4 Cloud Security Policy
    • 2.6.5 Typical Cases of Cloud Security

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

4 Automotive Public Cloud Platforms

  • 4.1 Amazon Cloud - AWS
    • 4.1.1 Introduction to Automotive Cloud Business
    • 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 AWS & Continental
    • 4.1.10 AWS & HERE
    • 4.1.11 AWS & ABUP
    • 4.1.12 AWS & ThunderSoft
    • 4.1.13 AWS & 51WORLD
  • 4.2 Microsoft Cloud - Azure
    • 4.2.1 Azure Telematics Cloud Platform
    • 4.2.2 Microsoft Connected Vehicle Platform (MCVP) Service
    • 4.2.3 MCVP Business Model and Major Clients
    • 4.2.4 MCVP Ecosystem Partners
    • 4.2.5 Cooperated with Ericsson Connected Vehicle Cloud (CVC)
    • 4.2.6 Ericsson CVC Solution
    • 4.2.7 Cooperative Automakers
    • 4.2.8 Cooperative Auto Parts Suppliers
  • 4.3 Google Cloud
    • 4.3.1 Google Cloud Platform (GCP)
    • 4.3.2 Cooperated with Kia and Ford
  • 4.4 Huawei Auto Cloud
    • 4.4.1 Introduction to Huawei Auto Cloud Business
    • 4.4.2 1+3+M+N Global Cloud Infrastructure Layout
    • 4.4.3 Automotive Solution
    • 4.4.4 Telematics Solution
    • 4.4.5 Autonomous Driving Development Solution
    • 4.4.6 Huawei's Autonomous Driving Cloud Ecosystem Partners
    • 4.4.7 Mobility Solutions
    • 4.4.8 Automotive Simulation Solution
    • 4.4.9 Digital Intelligent Platform Solution
    • 4.4.10 Digital Marketing Solution
    • 4.4.11 Overseas Business Solution
    • 4.4.12 Cooperative Customers
  • 4.5 Baidu Auto Cloud
    • 4.5.1 Introduction
    • 4.5.2 3.0 Architecture
    • 4.5.3 Autonomous Driving Solutions
    • 4.5.4 Baidu Telematics Cloud
    • 4.5.5 Baidu V2X Cloud
    • 4.5.6 Data Closed Loop Solution
    • 4.5.7 Data Annotation Scheme
    • 4.5.8 Security System
  • 4.6 Alibaba Auto Cloud
    • 4.6.1 Introduction
    • 4.6.2 Industry Capabilities
    • 4.6.3 Technical Bases
    • 4.6.4 Major Customers
    • 4.6.5 Telematics Security Solution
  • 4.7 Tencent Auto Cloud
    • 4.7.1 Introduction
    • 4.7.2 Architecture
    • 4.7.3 Tencent Autonomous Driving Cloud
    • 4.7.4 Tencent Intelligent Connection Cloud
    • 4.7.5 Capabilities
    • 4.7.6 Ecosystem
    • 4.7.7 Security Mechanism
    • 4.7.8 Automaker Customers
  • 4.8 ByteDance Auto Cloud
    • 4.8.1 Introduction
    • 4.8.2 System Architecture
    • 4.8.3 Ecosystem
    • 4.8.4 ByteDance's Cloud Computing Capabilities

5 Cloud Platform Layout of OEMs

  • 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 Geely & Tencent Cloud
    • 5.1.8 Geely & Qiniu Cloud
    • 5.1.9 Geely & Huawei Cloud
  • 5.2 Xpeng
    • 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 Big Data Platform
    • 5.3.3 Telematics Cloud
    • 5.3.4 Data Storage Scheme
  • 5.4 NIO
    • 5.4.1 Autonomous Driving Cloud
    • 5.4.2 Energy Cloud
  • 5.5 FAW
    • 5.5.1 Cloud Platform Layout of FAW Group
    • 5.5.2 FAW Hongqi Intelligent Cloud
    • 5.5.3 FAW Group's Local Data Center
    • 5.5.4 FAW & Huawei Cloud
    • 5.5.5 FAW & Alibaba Cloud
    • 5.5.6 FAW Group & e Cloud
  • 5.6 Changan
    • 5.6.1 Automotive Digitalization Path
    • 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
    • 5.6.6 Terminal-Cloud Service Ecosystem
    • 5.6.7 Automotive Cloud Platform Partners
    • 5.6.8 Changan & Tencent Cloud
    • 5.6.9 History of Cooperation between Changan and Tencent
  • 5.7 Great Wall Motor
    • 5.7.1 Intelligent Cloud
    • 5.7.2 Great Wall Motor & Huawei Cloud
    • 5.7.3 Great Wall Motor & Tencent Cloud
  • 5.8 SAIC
    • 5.8.1 Cloud Business Layout
    • 5.8.2 Cloud Products and Services
    • 5.8.3 Overall Architecture of Cloud Platform
    • 5.8.4 Service Capabilities of Cloud Platform
    • 5.8.5 Autonomous Driving Cloud
    • 5.8.6 Vehicle-Cloud Integrated Operating System Architecture
    • 5.8.7 Cloud Product Technology Route and Security Route

6 Summary and Trends

  • 6.1 Significance of Automakers' Migration to Cloud
    • 6.1.1 Cloud Platform Is the Foundation of Digitization of Automakers
    • 6.1.2 Significance of Automakers' Migration to Cloud
  • 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
    • 6.2.3 What Are the Cloud Capabilities Required by OEMs?
  • 6.3 Automotive Cloud Application and Business Model
    • 6.3.1 Cloud Application of OEMs
    • 6.3.2 Automotive Cloud Business Model
  • 6.4 Cloud Computing Architecture Trends
    • 6.4.1 Cloud Computing Architecture Evolves to the Software and Hardware Integration
    • 6.4.2 E/E Architecture of Vehicle Cloud Computing
  • 6.5 Data Lake and Cloud Native
    • 6.5.1 Data Lake Has Become A Hotspot for Cloud Platform Companies to Explore
    • 6.5.2 Data Lake + Cloud Native Builds a New Storage and Computing System
    • 6.5.3 Data Lake Cloud Native Architecture
    • 6.5.4 Application of AWS Autonomous Driving Data Lake in China
    • 6.5.5 Xpeng Motors' Autonomous Driving Data Lake Based on Alibaba Cloud
    • 6.5.6 Cloud Native Security Evolution
  • 6.6 Other Trends
    • 6.6.1 Develop from Single Cloud to Multi-Cloud
    • 6.6.2 Expansion of Distributed Edge Cloud Applications
    • 6.6.3 Cloud-Intelligence Integration
    • 6.6.4 Telematics Cloud Control Basic Platform Will Play A Bigger Role