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全球汽車數據管理和雲端平台策略 (2019年)

Global Automotive Data Management and Cloud Platform Strategies, 2019

出版商 Frost & Sullivan 商品編碼 927990
出版日期 內容資訊 英文 65 Pages
商品交期: 最快1-2個工作天內
全球汽車數據管理和雲端平台策略 (2019年) Global Automotive Data Management and Cloud Platform Strategies, 2019
出版日期: 2020年02月28日內容資訊: 英文 65 Pages


本報告研究分析全球汽車產業數據管理平台與雲端平台之開發、普及趨勢、未來展望,彙整技術概要、基本功能、未來技術進步潛力和方向、整體市場規模趨勢展望、主要供應商市場機會和需因應課題,提供按領域的詳細趨勢,以及大型企業 (汽車製造商 (OEM)、技術提供商等) 的代表性案例等情報。



新車用 (OEM) 雲端平台市場趨勢

  • 汽車IoT平台的不同階層:概要
  • 全球汽車用IoT生態系統:參與企業
  • 汽車雲端主要應用
  • 雲端和數據管理的OEM企業 (汽車製造商) 行動
  • OEM分析:連接性和雲端夥伴
  • OEM雲端平台分析
  • 汽車雲端:主要供應商概要
  • 服務提供夥伴:主要供應商概要
  • 汽車雲端平台:未來市場機會


  • 全球連網車市場未來預測
  • 透過雲端的連網服務
  • 連網車服務的興起
  • OEM (汽車製造商) 數據管理策略:連網服務
  • 連網服務發展模式分析
  • 案例研討:BMW - Connected Services Architecture
  • 案例研討:VW - Automotive Cloud
  • 案例研討:Daimler - eXtollo Cloud Platform

自動駕駛車 (AV):測試與開發

  • 自動駕駛車 (L3 / 4) 市場規模 (2030年前)
  • 透過雲端的自動駕駛車 (AV) 開發
  • 未來的自動駕駛服務
  • 汽車製造商 (OEM) 數據管理策略:AV開發
  • 用於AV開發的汽車雲端平台
  • 案例研討:Toyota Research Institute (TRI) 和AWS IoT
  • 案例研討:Audi的AV開發

成長機會和企業行動 (C2A)

  • 汽車用雲端:成長機會
  • 成功與成長的策略要務:汽車用雲端平台


  • 結論:3大預測


關於Frost & Sullivan

Product Code: K399-18

Prioritising Critical Datasets from Non-critical Ones Will Determine Cloud-related Partnerships for OEMs by 2025

The aim of this research study is to give an overview of automotive cloud platforms and the key cloud applications adopted in the automotive market. The study focuses on the different cloud platform strategies adopted by original equipment manufacturers (OEMs), key business models used, key cloud vendors, and their core features. Frost & Sullivan believes that automotive cloud and data management platforms will form the backbone of digitization initiatives in the industry. However, OEMs are challenged with technical skills in-house and, hence, are dependent on technology partnerships for building automotive cloud platforms.

Automakers understand the importance of managing data and deriving value from them. Cloud platforms are critical to move, store, secure, and index massive volumes of data generated from connected vehicles. However, only purpose-built cloud platforms exist today. Connected services deployment has one and smart manufacturing service has another. Data from connected and autonomous vehicles are collected and processed separately. This situation gives rise to data scalability issues and accommodation of evolving technological changes. Therefore, automakers are seeking technological partners who will not just provide cloud solutions, but have the capabilities to build a unified data management platform with Artificial Intelligence (AI)/Machine Learning (ML) capabilities.

OEMs store connected data on private cloud, fearing security and privacy issues; this will be an expensive option in the long run. Hybrid cloud architecture will be the future for mass automakers. Less critical services will be on the public cloud and the sensitive use cases (such as OEM-specific vehicle testing and development) will be mostly on private cloud. The hybrid cloud approach will be ideal once OEMs build the capabilities that are required to segment the critical datasets from non-critical ones. Frost & Sullivan believes that 80-85% of the OEMs will transition to hybrid cloud architecture, owing to scale and cost benefits.

Automotive companies should lay down aggressive roadmaps for the development of futuristic data management strategies-including what level of data needs to be collected, how data labeling will happen, and what the level of scaling in future will be-and accordingly set up an ecosystem for storage, processing, and service delivery. Cognitive capabilities should not be used for any specific application, but should be embedded throughout the cloud platforms. Automotive firms can, thereby, leverage deeper insights from generated data and create compelling use cases for connected and autonomous vehicles.

Table of Contents

Executive Summary

  • Key Findings
  • Key Trends Transforming the Autonomous and Connected Landscape
  • Growth in Connected Vehicles and Data
  • Different Layers of Automotive IoT Platform-Overview
  • Data Management Strategies Adopted in the Market
  • OEM-Technology Partnerships for Cloud
  • OEMs With In-house Strategies
  • VW Future Strategy-In-house Software Development
  • Automotive Cloud Platform-Future Opportunities
  • Automotive Cloud-Key Vendor Highlights
  • Automotive Cloud and Data Management-Current Vs. Future Outlook

Research Scope, Objectives, and Background

  • Research Scope
  • Key Questions this Study will Answer
  • Research Background

OEM Cloud Platform-Market Trends

  • Different Layers of Automotive IoT Platform-Overview
  • Global Automotive IoT Ecosystem-Participants
  • Key Applications of Automotive Cloud
  • OEM Approach Towards Cloud and Data Management
  • OEM Analysis-Connectivity and Cloud Partnerships
  • OEM Cloud Platform Analysis
  • Automotive Cloud-Key Vendor Highlights
  • Service Delivery Partners-Key Vendor Highlights
  • Future Opportunities for Automotive Cloud Platform

Connected Services

  • Global Connected Cars Forecast
  • Connected Services Through Cloud
  • Rise in Connected Vehicle Services
  • OEM Data Management Strategies-Connected Services
  • Deployment Model Analysis for Connected Services
  • Case Study: BMW Connected Services Architecture
  • Case Study: VW Automotive Cloud
  • Case Study: Daimler's eXtollo Cloud Platform

Autonomous Vehicles-Testing and Development

  • Market Volume of Automated Vehicles (L3/4) by 2030
  • AV Development Through Cloud
  • Autonomous Driving Services of the Future
  • OEM Data Management Strategies-AV Development
  • Automotive Cloud Platforms used for AV Development
  • Case Study: Toyota Research Institute and AWS IoT
  • Case Study: Audi's AV Development

Growth Opportunities and Companies to Action

  • Automotive Cloud Growth Opportunities
  • Strategic Imperatives for Success and Growth-Automotive Cloud Platform

The Last Word

  • The Last Word-3 Big Predictions
  • Legal Disclaimer


  • List of Exhibits
  • List of Exhibits (continued)

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