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중국의 자동차 클라우드 서비스 플랫폼 산업(2024년)
Automotive Cloud Service Platform Industry Report, 2024
상품코드 : 1613804
리서치사 : ResearchInChina
발행일 : 2024년 10월
페이지 정보 : 영문 360 Pages
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한글목차

1. AI 기반 모델과 자동차용 NOA가 자동차 클라우드 서비스의 수요를 확대

2024년, 가격 경쟁을 겪은 후 OEM의 비용 절감에 초점을 맞추면서 비즈니스의 클라우드화 속도가 느려지고 클라우드 수요가 감소했으나, NOA의 대량 생산과 클라우드 기반 AI 기반 모델의 자동차 탑재로 인해 자동차 클라우드 서비스 수요는 감소하지 않고 증가했습니다. 증가하고 있습니다.

자동차에 탑재되는 AI 기반 모델과 NOA는 클라우드 서비스에 대해 다음과 같은 요구사항이 있습니다.

1. 하드웨어 인프라 하드웨어 인프라: AI 기반 모델/NOA의 운영에는 많은 컴퓨팅 파워가 필요합니다. 클라우드 서버의 성능과 대수에 대한 요구가 높아지는 것은 물론, 서버 장비의 네트워크 성능도 시험대에 오릅니다.

2. 소프트웨어 솔루션

AI 기반 모델: 2024년 9월 현재 국내 양산 모델에 탑재된 기반 모델은 주로 클라우드 기반 배포를 채택하고 있습니다. 일부 신흥 OEM(예 : NIO/Xpeng Motors/Li Auto 등)은 터미널과 클라우드의 통합 배포를 채택하여 복잡한 기능 모드를 완료하려면 클라우드 기반 모델을 호출해야합니다.

기반 모델이 차량에 탑재된 후, 사용자와의 상호 작용 빈도가 증가하여 하루에 몇 번의 상호 작용 요청에서 수십, 수백 번의 상호 작용 요청이 발생하여 클라우드 서비스에 대한 수요가 증가했습니다.

NOA: NOA의 탑재량이 급증하고 사용 빈도가 증가함에 따라 처리 및 저장되는 데이터의 양이 증가하고, 클라우드 서비스에 대한 수요가 증가했으며, 이를 지원하는 툴체인도 증가했습니다.

2024년, 알리바바 클라우드는 모멘타에 안정적이고 유연한 클라우드 네이티브 컴퓨팅 리소스를 제공하여 자동화된 폐쇄 루프 데이터를 구축할 수 있도록 지원할 예정입니다. 이 솔루션은 엔드 투 엔드 기술 프레임워크를 지원하고, 시각 인식 AI 기능을 커버하며, 지능형 운전 솔루션의 대규모 배포 및 활용을 촉진할 수 있습니다. HPA 메커니즘의 탄력성을 바탕으로 높은 비용 효율성을 실현하고 있습니다.

같은 해 AWS는 메르세데스-벤츠, BMW 등 국제 브랜드와 협력하여 클라우드 플랫폼 툴체인을 통해 AI 어시스턴트를 구축하여 업무 효율을 향상시키고 있습니다.

2. 클라우드 플랫폼 툴체인 심층 통합

2024년, 자동차 클라우드 서비스 솔루션은 설계 및 개발, 생산 관리, 공급망 최적화, 마케팅 촉진, 애프터서비스에 이르기까지 전체 체인 데이터 통합과 지능형 의사결정 지원을 실현하고 심층 통합의 방향으로 더욱 발전할 것입니다. 발전할 것으로 보입니다. 클라우드 벤더의 새로운 솔루션은 깊은 기능 통합과 완벽한 툴체인의 특징을 반영하고 있습니다.

2024년 9월, 바이두는 엔드 투 엔드 지능형 주행 개발에 초점을 맞춘 Intelligent Cloud 3.0을 발표했습니다. 특징은 다음과 같습니다.

가상 시뮬레이션 데이터 구축을 포함하여 차량에서 클라우드까지 전 과정의 지능형 주행 훈련에 사용할 수 있습니다.

클라우드에서 훈련 효율을 높이고 차량과 도로 사이의 데이터 장벽을 제거하여 실시간 차량-도로 클라우드 플랫폼을 구축합니다. 이 플랫폼은 정체 구간 조기 회피, 가시거리를 벗어난 위험 경고, 신호등 알림, 원격 라이브 조감도 등의 서비스를 제공합니다.

의미 이해 스케줄링, 컨텐츠 생성, 벡터 검색, 크로스 모달리티 등 R&D 시나리오를 위한 클라우드 기반 콕핏 기반 모델을 제공합니다.

2024년 9월, 화웨이는 L4 자율주행 네트워크 솔루션인 Xinghe AI 자율주행 네트워크(Xinghe AI Autonomous Driving Network)를 발표했습니다. 이 솔루션은 네트워크 데이터 분석, 다중 시나리오 시뮬레이션, 에이전트 호출을 클라우드를 통해 실현합니다.

중국의 자동차 클라우드 서비스 플랫폼 산업에 대해 조사분석했으며, 국내외 클라우드 서비스 산업이나 각사 플랫폼에 관한 정보를 제공하고 있습니다.

목차

제1장 자동차 클라우드 서비스의 개요

제2장 자동차 클라우드 솔루션

제3장 클라우드 플랫폼 인프라

제4장 자동차 퍼블릭 클라우드 플랫폼

제5장 OEM 클라우드 플랫폼 레이아웃

제6장 요약과 동향

KSA
영문 목차

영문목차

Automotive cloud services: AI foundation model and NOA expand cloud demand, deep integration of cloud platform tool chain

In 2024, as the penetration rate of intelligent connected vehicles continues to increase, the development of automotive cloud services will show the following trends:

AI foundation model and NOA installed in vehicles expand the demand for automotive cloud services

Deep integration of cloud platform tool chain

Cloud native further changes the way automotive software is developed

Terminal-cloud integration

Cloud infrastructure resources are further in short supply, and cloud vendors are increasing their investment costs

......

1. AI foundation model and NOA installed in vehicles expand the demand for automotive cloud services

In 2024, after experiencing a price war, OEMs' cost reduction has become a focus, the pace of business cloudification has slowed down, and the demand for cloud has declined; but with the mass production of NOA and the installation of cloud-based AI foundation models in vehicles, the demand for automotive cloud services has increased instead of decreased.

AI foundation model and NOA installed in vehicles have the following requirements for cloud services:

1.Hardware infrastructure: The operation of AI foundation model/NOA requires a lot of computing power. In addition to placing higher demands on the performance and number of cloud servers, it also tests the network performance of server facilities.

2.Software solution:

AI foundation model: As of September 2024, the foundation model installed in domestic mass-produced models mainly adopts cloud-based deployment. Some emerging OEMs (such as NIO/ Xpeng Motors/Li Auto) adopt terminal-cloud integration deployment, in which complex functional modes still need to call cloud-based models to complete.

After the foundation model was installed in vehicles, the frequency of user interactions increased, from a few interaction requests per day to dozens or even hundreds of interaction requests per day, which led to an increase in demand for cloud services.

NOA: NOA's installation volume has surged significantly, and the frequency of use has also increased, which has led to an increase in the amount of data processed and stored, an increase in the demand for cloud services, and an increase in the supporting tool chain.

In 2024, Alibaba Cloud provides Momenta with stable and flexible cloud-native computing resources to build automated closed-loop data. The solution supports an end-to-end technical framework, covers visual perception AI capabilities, and can promote large-scale deployment and application of intelligent driving solutions. Momenta uses Spot cases to achieve high cost- effectiveness based on the elasticity of Alibaba Cloud ESS&HPA mechanisms.

In the same year, AWS cooperates with international brands such as Mercedes-Benz and BMW to build AI assistants through cloud platform tool chain to improve operational efficiency.

2. Deep Integration of cloud platform tool chain

In 2024, automotive cloud service solutions will further develop in the direction of deep integration, realizing data integration and intelligent decision support for the entire chain from design and development to production management, supply chain optimization, marketing promotion and even after-sales service. New solutions of cloud vendors reflect characteristics of deep functional integration and perfect tool chain:

In September 2024, Baidu launched Intelligent Cloud 3.0, focusing on end-to-end intelligent driving development. Features include:

It can be used for full-process intelligent driving training from vehicle to cloud, including building virtual simulation data;

By improving training efficiency in the cloud and breaking down data barriers between the vehicle and the road, a real-time vehicle-road cloud platform is built; the platform provides services including early avoidance of congested sections, beyond-visual-range risk warnings, traffic light reminders, and remote live bird's-eye view.

Provides a cloud-based cockpit foundation model for R&D scenarios such as semantic understanding scheduling, content generation, vector search, and cross-modality.

In September 2024, Huawei launched the L4 autonomous driving network solution - Xinghe AI autonomous driving network, which implements network data analysis, multi - scenario simulation and Agent calling in the cloud:

3. Cloud native further changes the way automotive software is developed

As some OEMs/Tier1s begin to build a vehicle-cloud collaborative infrastructure and try to upload vehicle-side data to the cloud for analysis and processing, and send cloud-side commands to the vehicle to achieve simple vehicle-cloud interaction. Cloud native technology has begun to be applied to the cloud-vehicle collaborative development process in automotive industry.

Cloud native is a software approach to building and running scalable applications in new dynamic environments such as public clouds, private clouds, and hybrid clouds. The concept of "cloud native" was proposed before 2020, but its application in automobiles has been in the exploratory stage and has not been widely used. It is mainly concentrated in telematics and some intelligent cockpit functions. In 2024, with the application of multi-cloud environments and AI technologies, the application scenarios of cloud native will increase greatly, and begin to affect construction logic of PaaS/SaaS cloud service solutions in the automotive industry from underlying layer:

On the vehicle side, in addition to in-vehicle infotainment system, cloud native has been used for the development and optimization of related technologies for autonomous driving and intelligent cockpit. For example, through large-scale computing and training functions of cloud-based autonomous driving platform, more accurate models and more comprehensive algorithms are provided for autonomous driving system, improving the safety and reliability of autonomous driving.

In the cloud, OEMs use a more complete cloud platform to store, mine, analyze and process vehicle data, provide support for intelligent vehicle operations, and gradually apply cloud-native software development models to OEM supply chain management, production and manufacturing and other fields, achieving collaborative optimization of entire industry chain. For example, container orchestration technology (Kubernetes, etc.) has gradually become the core technology for OEMs to build cloud-vehicle software collaborative development platforms.

Taking the cooperation between AWS and Continental to develop cloud-based ECU as an example, Continental used AWS Graviton to simulate the hardware environment, then selected operating system and middleware to run on AWS EC2, and completed the creation of virtualized development environment through AWS EC2.

In 2024, cloud native applications will focus on:

1.Computing power management: The cloud-native platform can provide efficient computing power management technologies, including GPU computing power scheduling and distributed training, to meet the needs of AI algorithm training and real-time reasoning.

2.Optimization of service mesh and API gateway: Besides 5G communication, performance optimization of service mesh, collaboration with Kubernetes scheduler, flexible configuration and security protection of API gateway also have a great impact on cloud communication.

3.Data management and governance: Data management capabilities of Automotive Cloud Native Platform include data storage, backup, cleaning, labeling, and data security & compliance management. As OEMs increasingly value the circulation of data assets, efficient data circulation and sharing have become one of factors affecting the cloud native platform.

4.Cloud resource utilization: Some OEMs are beginning to pay attention to factors such as energy efficiency and resource utilization of cloud native technologies. Some energy-saving technologies and strategies reduce the energy consumption of cloud native platforms, while improving resource utilization efficiency and reducing operating costs.

NIO uses cloud-native technology to build a vehicle-cloud collaborative development platform

In order to solve problems such as scarce computing power, chaotic edge node management, and unstable cloud communications, NIO uses KubeEdge as the core of platform and builds a complete vehicle-cloud collaborative development platform with Kubernetes + KubeEdge as the technical base.

NIO's vehicle-cloud collaboration platform uses KubeEdge's cloud-edge communication mechanism to solve the tidal effect problem of node connections.

Typical application scenarios of this technology include:

1. New energy vehicle battery health and safety data analysis: In the algorithm development stage, use containerization to develop edge algorithms; in the engineering vehicle verification stage, deploy edge computing container applications in small batches; after verification, replace the corresponding mass production base image.

2. Build a vehicle-side software test management platform: After introducing cloud native capabilities, Virtual car, test benches, and real vehicles can be connected to K8s for unified monitoring and management, which can arrange test tasks more reasonably and improve the utilization of test resources.

Table of Contents

1 Overview of Automotive Cloud Services

2 Automotive Cloud Solutions

3 Cloud Platform Infrastructure

4 Automotive Public Cloud Platforms

5 OEM Cloud Platform Layout

6 Summary and Trends

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