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汽车云服务平台市场：2023 年行业报告
市场调查报告书
商品编码
1337773

汽车云服务平台市场：2023 年行业报告

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个工作天内

价格
简介目录

从企业的角度来看，数字化转型的目标是实现汽车全生命週期的所有流程要素数字化，包括研发、生产、销售、运营、售后服务等，将服务器、机房中的数据上传到企业云，打通各环节的数据通道，对全产业链数据进行一体化管理，逐步实现云-管-端一体化实时互联，为用户打造跨越行业全生命週期的服务运营模式，加强行业上下游合作伙伴的联繫，创造更大的价值。

在产品方面，车辆智能化和网联化正在蓬勃发展。例如，从L2开始，自动驾驶能力每发展到一个更高的水平，云基础设施平台、应用程序和服务的消耗就会增加几个数量级。随着高度自动驾驶进入量产，车辆传感器数量和数据量不断增加，本地处理难以满足要求。因此，迁移到云端是最好的选择。

汽车製造商每年花费数千万元建设云服务，这对市场产生了巨大的推动作用。2022年，中国汽车云服务市场规模将超过150亿元，预计未来五年将保持30-40%的增速。

2021年，ByteDance将推出 "ByteDance Auto Cloud" ，提供数字营销、智能座舱、自动驾驶、车辆服务四个细分领域的云服务。2022年，Tencent Intelligent Cloud Cloud、Baidu Auto Cloud、Alibaba Auto Cloud上线。Baidu, Alibaba, Tencent, Huawei, Douyin五巨头（BATHD）纷纷入局，加剧了基于汽车专用云的汽车云服务竞争。

本报告调查了汽车云服务平台市场，并提供了市场概述，以及云解决方案、平台基础设施趋势、需求趋势、商业模式和未来趋势。

目录

第一章 汽车云服务概述

  • 汽车云服务行业概况
  • 汽车云服务主要类型
  • 汽车云服务竞争格局
  • 中国汽车云商业模式
  • 汽车云发展机遇
  • 汽车云应用场景

第二章 汽车云解决方案

  • 自动驾驶云
  • 远程信息处理
  • V2X云
  • 数字化转型
  • 云数据闭环
  • 云信息安全

第三章 云平台基础设施

  • 汽车云产业链
  • 数据中心
  • 云服务器
  • 服务器芯片
  • 云提供商在内部芯片开发方面取得进展

第四章 汽车公有云平台

  • 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云平台布局

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

第六章 概述与趋势

  • 云迁移对汽车製造商的重要性
  • 云服务需求趋势
  • 汽车云应用和商业模式
  • 云计算架构的趋势
  • 数据湖和云原生
  • 其他趋势
简介目录
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