Global and China Automotive Cloud Service Platform Industry Report 2023 Featuring 8 Automotive Public Cloud Platforms and Cloud Platform Layout of 8 OEMs – ResearchAndMarkets.com

DUBLIN–(BUSINESS WIRE)–The “Global and China Automotive Cloud Service Platform Industry Report, 2023” report has been added to ResearchAndMarkets.com’s offering.

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

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.

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, 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.

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.

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).

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.

Key Topics Covered:

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

4.2 Microsoft Cloud – Azure

4.3 Google Cloud

4.4 Huawei Auto Cloud

4.5 Baidu Auto Cloud

4.6 Alibaba Auto Cloud

4.7 Tencent Auto Cloud

4.8 ByteDance Auto Cloud

5 Cloud Platform Layout of OEMs

5.1 Geely

5.2 Xpeng

5.3 Li Auto

5.4 NIO

5.5 FAW

5.6 Changan

5.7 Great Wall Motor

5.8 SAIC

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

For more information about this report visit https://www.researchandmarkets.com/r/23tlfh

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