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市场调查报告书
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汽车边缘运算市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

Automotive Edge Computing Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 170 Pages | 商品交期: 2-3个工作天内

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简介目录

2024年，全球汽车边缘运算市场规模达74亿美元，预计到2034年将以21.7%的复合年增长率成长，达到422亿美元。随着汽车产业的快速发展，车辆正日益成为能够即时处理大量资料的智慧平台。自动驾驶技术和互联出行解决方案的蓬勃发展正推动着这一转变。因此，传统的集中式运算模式正显着转向基于边缘的资料处理，这使得运算能力更接近源头——车辆内部。

汽车边缘运算市场 - IMG1

汽车边缘运算在支援这一转变中发挥关键作用，它提供管理复杂车载功能所需的低延迟和高频宽。它增强了对现代车辆安全高效运行至关重要的即时决策能力。互联功能的激增和先进车载感测器的日益普及导致资料爆炸性增长，迫切需要车载分析和即时响应系统。边缘运算无需将资料路由到遥远的云端中心，而是使车辆能够在源头分析资讯并采取行动，从而减少网路拥塞和回应时间，同时提高效能、安全性和可靠性。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 74亿美元
预测值 422亿美元
复合年增长率 21.7%

这对于驾驶辅助、预测性维护和智慧路线规划等应用至关重要。随着製造商向以软体为中心的车辆架构转型，整合先进的边缘平台对于建立面向下一代移动出行的可扩展、安全且高效的交通系统至关重要。

就组件而言，市场分为硬体、软体和服务。硬体在2024年成为领先细分市场，贡献了近54%的全球市场份额，预计在整个预测期内将以超过22%的复合年增长率成长。高效能运算单元、人工智慧最佳化处理器和汽车级模组的持续部署，凸显了边缘硬体在处理复杂资料流方面日益增长的重要性。这些组件经过精心设计，能够承受极端的车辆环境，同时确保即时监控和自主导航等应用的持续处理能力。

依车辆类型划分，市场分为乘用车和商用车。乘用车在2024年占据主导地位，约占市场总收入的69%。预计该细分市场在2025年至2034年期间的复合年增长率将超过23%。乘用车对整合数位功能、个人化驾驶体验和进阶驾驶辅助系统的需求日益增长，推动了边缘运算技术的普及。这些车辆需要强大的处理能力来管理来自各种嵌入式系统的资料输入，从而实现即时决策，从而提升使用者体验和车辆安全性。

根据部署模式，该产业可分为云端部署和本地部署解决方案。云端部署凭藉其灵活性、可扩展性以及支援各种连网汽车功能的能力，继续占据相当大的市场份额。这些平台支援无缝软体整合、远端更新和集中协调，这对于自动驾驶和车队管理等新兴用例至关重要。随着人们越来越依赖支援动态路线优化、资讯娱乐交付和预测性诊断等服务的车云基础设施，云端部署的广泛应用也得到了推动。

从区域来看，中国在2024年引领全球汽车边缘运算市场，创造了约19亿美元的收入，占据了亚太地区约63%的市场。中国在智慧旅行领域的快速扩张，加上其作为全球最大汽车生产国的地位，使其在边缘技术应用方面处于领先地位。政府的大力支持、电动车的快速发展以及车联网系统的大规模部署，将继续推动该地区市场的成长。

随着汽车製造商和技术供应商优先考虑更快、更智慧、更去中心化的处理系统，汽车边缘运算领域正在经历结构性转型。对即时资料解读的需求日益增长，尤其是在安全敏感的驾驶条件下，这促使人们从根本上重新思考车辆内部资讯的处理方式。如今，各大公司正专注于将人工智慧功能、轻量级资料处理框架和强大的安全协议直接整合到车载环境中。这些进步旨在将原始感测器输出转化为可即时采取行动的有意义的洞察，从而创造更安全、更具适应性、更有效率的交通系统。

目录

第一章：方法论

  • 市场范围和定义
  • 研究设计
    • 研究方法
    • 资料收集方法
  • 资料探勘来源
    • 全球的
    • 地区/国家
  • 基础估算与计算
    • 基准年计算
    • 市场评估的主要趋势
  • 初步研究和验证
    • 主要来源
  • 预测模型
  • 研究假设和局限性

第二章：执行摘要

第三章：行业洞察

  • 产业生态系统分析
    • 供应商格局
    • 利润率分析
    • 成本结构
    • 每个阶段的增值
    • 影响价值链的因素
    • 中断
  • 产业衝击力
    • 成长动力
      • 自动驾驶和连网汽车的需求不断增长
      • 车载感测器资料量不断增加
      • 增强资讯娱乐和车载体验
      • 政府对道路安全和资料在地化的监管
    • 产业陷阱与挑战
      • 初始基础设施成本高
      • 资料隐私与合规性的复杂性
    • 市场机会
      • 与人工智慧和机器学习整合以进行决策
      • 智慧城市与V2X生态系扩展
  • 成长潜力分析
  • 监管格局
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • 中东和非洲
  • 波特的分析
  • PESTEL分析
  • 技术和创新格局
    • 当前的技术趋势
    • 新兴技术
  • 成本細項分析
    • 软体开发和授权成本
    • 部署和整合成本
    • 维护和支援成本
    • 网路安全与合规成本
    • 培训和变更管理成本
  • 专利分析
  • 永续性和环境方面
    • 永续实践
    • 减少废弃物的策略
    • 生产中的能源效率
    • 环保倡议
    • 碳足迹考量
  • 用例
  • 最佳情况
  • 消费者行为与采用趋势
  • 使用者体验和介面趋势

第四章：竞争格局

  • 介绍
  • 公司市占率分析
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • MEA
  • 主要市场参与者的竞争分析
  • 竞争定位矩阵
  • 战略展望矩阵
  • 关键进展
    • 併购
    • 伙伴关係与合作
    • 新产品发布
    • 扩张计划和资金

第五章：市场估计与预测：按组件，2021 - 2034 年

  • 主要趋势
  • 硬体
    • 边缘节点
    • 闸道
    • 边缘伺服器
  • 软体
    • 边缘设备管理
    • 分析和处理软体
    • 安全软体
  • 服务
    • 专业的
      • 系统整合与部署
      • 咨询与策略
      • 培训与支援
    • 託管
      • 远端监控和管理
      • 维护和更新
      • 安全管理

第六章：市场估计与预测：依车型，2021 - 2034 年

  • 主要趋势
  • 搭乘用车
    • 轿车
    • 掀背车
    • SUV
  • 商用车
    • 轻型
    • 中型
    • 重负

第七章：市场估计与预测：依部署模式，2021 - 2034 年

  • 主要趋势
  • 基于云端
  • 本地

第八章：市场估计与预测：依企业规模，2021 - 2034 年

  • 主要趋势
  • 中小企业
  • 大型企业

第九章：市场估计与预测：按应用，2021 - 2034 年

  • 主要趋势
  • 自动驾驶和网联驾驶
  • 车载体验与资讯娱乐
  • 预测性维护和诊断
  • 车队和交通管理
  • 网路安全和资料保护

第十章：市场估计与预测：按地区，2021 - 2034 年

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 北欧人
    • 俄罗斯
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 南非
    • 沙乌地阿拉伯
    • 阿联酋

第 11 章：公司简介

  • Amazon
  • Arteris IP
  • Autotalks
  • Bosch Group
  • Cisco
  • ETAS GmbH
  • FogHorn Systems
  • GreenWave Systems
  • Hewlett Packard Enterprise (HPE)
  • Huawei
  • IBM
  • Infineon Technologies
  • Intel
  • Microsoft
  • Mobileye
  • NVIDIA
  • NXP Semiconductors
  • Qualcomm Technologies
  • Siemens
  • Teradata
简介目录
Product Code: 14139

The Global Automotive Edge Computing Market was valued at USD 7.4 billion in 2024 and is estimated to grow at a CAGR of 21.7% to reach USD 42.2 billion by 2034. As the automotive industry rapidly evolves, vehicles are increasingly becoming intelligent platforms capable of processing vast amounts of data in real time. This transformation is being driven by the surge in autonomous driving technologies and connected mobility solutions. As a result, there is a significant shift away from traditional centralized computing models to edge-based data processing, which places computational power closer to the source-inside the vehicle itself.

Automotive Edge Computing Market - IMG1

Automotive edge computing is playing a pivotal role in supporting this shift by delivering the low latency and high bandwidth required to manage complex in-vehicle functions. It enhances real-time decision-making capabilities critical to the safe and efficient operation of modern vehicles. The proliferation of connected features and the growing use of advanced in-vehicle sensors are contributing to an explosion of data, creating a pressing need for on-board analytics and instant response systems. Rather than routing data to distant cloud centers, edge computing empowers vehicles to analyze and act on information at the source, reducing network congestion and response time while enhancing performance, safety, and reliability.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$7.4 Billion
Forecast Value$42.2 Billion
CAGR21.7%

This is becoming essential for applications such as driver assistance, predictive maintenance, and intelligent route planning. As manufacturers transition toward software-centric vehicle architectures, the integration of advanced edge platforms becomes vital for creating scalable, secure, and efficient transportation systems built for the next generation of mobility.

In terms of components, the market is categorized into hardware, software, and services. Hardware emerged as the leading segment in 2024, contributing nearly 54% of the global market share, and is anticipated to grow at a CAGR exceeding 22% throughout the forecast period. The rising deployment of high-performance computing units, AI-optimized processors, and automotive-grade modules underscores the growing importance of edge hardware in handling complex data streams. These components are engineered to endure extreme vehicle environments while ensuring continuous processing power for applications like real-time monitoring and autonomous navigation.

By vehicle type, the market is divided into passenger cars and commercial vehicles. Passenger cars held a dominant position in 2024, accounting for approximately 69% of the total market revenue. This segment is set to expand at a CAGR of over 23% between 2025 and 2034. The increasing demand for integrated digital features, personalized driver experiences, and advanced driver-assist systems in passenger vehicles is driving the uptake of edge computing technologies. These vehicles require robust processing capabilities to manage data inputs from various embedded systems, enabling real-time decisions that improve both user experience and vehicle safety.

Based on deployment mode, the industry is segmented into cloud-based and on-premises solutions. Cloud-based deployment continues to command a significant share of the market due to its flexibility, scalability, and ability to support a wide range of connected vehicle functions. These platforms allow seamless software integration, remote updates, and centralized coordination, which are critical for emerging use cases in autonomous driving and fleet management. Their widespread adoption is being propelled by the increasing reliance on vehicle-to-cloud infrastructure that supports services like dynamic route optimization, infotainment delivery, and predictive diagnostics.

Regionally, China led the global automotive edge computing market in 2024, generating around USD 1.9 billion in revenue and capturing roughly 63% of the Asia Pacific market. The country's rapid expansion in smart mobility initiatives, coupled with its position as the world's largest automotive producer, has positioned it at the forefront of edge technology adoption. Strong governmental support, fast-paced development in electric vehicles, and massive deployment of connected vehicle systems continue to bolster market growth in the region.

The automotive edge computing landscape is undergoing a structural transformation as automakers and technology providers prioritize faster, more intelligent, and decentralized processing systems. Growing requirements for instantaneous data interpretation, especially in safety-sensitive driving conditions, are prompting a fundamental rethinking of how information is handled within vehicles. Companies are now focused on integrating AI capabilities, lightweight data processing frameworks, and robust security protocols directly into in-vehicle environments. These advancements are designed to transform raw sensor outputs into meaningful insights that can be acted upon in real time, thus enabling safer, more adaptive, and more efficient transportation systems.

Table of Contents

Chapter 1 Methodology

  • 1.1 Market scope and definition
  • 1.2 Research design
    • 1.2.1 Research approach
    • 1.2.2 Data collection methods
  • 1.3 Data mining sources
    • 1.3.1 Global
    • 1.3.2 Regional/Country
  • 1.4 Base estimates and calculations
    • 1.4.1 Base year calculation
    • 1.4.2 Key trends for market estimation
  • 1.5 Primary research and validation
    • 1.5.1 Primary sources
  • 1.6 Forecast model
  • 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

  • 2.1 Industry 3600 synopsis, 2021 – 2034
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Component
    • 2.2.3 Vehicle
    • 2.2.4 Deployment mode
    • 2.2.5 Enterprise size
    • 2.2.6 Application
  • 2.3 TAM Analysis, 2025-2034
  • 2.4 CXO perspectives: strategic imperatives
    • 2.4.1 Executive decision points
    • 2.4.2 Critical success factors
  • 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Growing demand for autonomous and connected vehicles
      • 3.2.1.2 Increasing data volume from in-vehicle sensors
      • 3.2.1.3 Enhanced infotainment and in-vehicle experience
      • 3.2.1.4 Government regulations for road safety and data localization
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 High initial infrastructure cost
      • 3.2.2.2 Data privacy & compliance complexities
    • 3.2.3 Market opportunities
      • 3.2.3.1 Integration with AI and ML for decision-making
      • 3.2.3.2. Smart city & V2 X ecosystem expansion
  • 3.3 Growth potential analysis
  • 3.4 Regulatory landscape
    • 3.4.1 North America
    • 3.4.2 Europe
    • 3.4.3 Asia Pacific
    • 3.4.4 Latin America
    • 3.4.5 Middle East & Africa
  • 3.5 Porter's analysis
  • 3.6 PESTEL analysis
  • 3.7 Technology and innovation landscape
    • 3.7.1 Current technological trends
    • 3.7.2 Emerging technologies
  • 3.8 Cost breakdown analysis
    • 3.8.1 Software development & licensing cost
    • 3.8.2 Deployment & integration cost
    • 3.8.3 Maintenance & support cost
    • 3.8.4 Cybersecurity & compliance cost
    • 3.8.5 Training & change management cost
  • 3.9 Patent analysis
  • 3.10 Sustainability and environmental aspects
    • 3.10.1 Sustainable practices
    • 3.10.2 Waste reduction strategies
    • 3.10.3 Energy efficiency in production
    • 3.10.4 Eco-friendly Initiatives
    • 3.10.5 Carbon footprint considerations
  • 3.11 Use cases
  • 3.12 Best-case scenario
  • 3.13 Consumer behaviour & adoption trends
  • 3.14 User experience & interface trends

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 LATAM
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Strategic outlook matrix
  • 4.6 Key developments
    • 4.6.1 Mergers & acquisitions
    • 4.6.2 Partnerships & collaborations
    • 4.6.3 New product launches
    • 4.6.4 Expansion plans and funding

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Mn)

  • 5.1 Key trends
  • 5.2 Hardware
    • 5.2.1 Edge nodes
    • 5.2.2 Gateways
    • 5.2.3 Edge servers
  • 5.3 Software
    • 5.3.1 Edge device management
    • 5.3.2 Analytics & processing software
    • 5.3.3 Security software
  • 5.4 Services
    • 5.4.1 Professional
      • 5.4.1.1 System integration & deployment
      • 5.4.1.2 Consulting & strategy
      • 5.4.1.3 Training & support
    • 5.4.2 Managed
      • 5.4.2.1 Remote monitoring & management
      • 5.4.2.2 Maintenance & updates
      • 5.4.2.3 Security management

Chapter 6 Market Estimates & Forecast, By Vehicle, 2021 - 2034 ($Mn)

  • 6.1 Key trends
  • 6.2 Passenger cars
    • 6.2.1 Sedans
    • 6.2.2 Hatchbacks
    • 6.2.3 SUVs
  • 6.3 Commercial vehicles
    • 6.3.1 Light duty
    • 6.3.2 Medium duty
    • 6.3.3 Heavy duty

Chapter 7 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Mn)

  • 7.1 Key trends
  • 7.2 Cloud-based
  • 7.3 On-premises

Chapter 8 Market Estimates & Forecast, By Enterprise Size, 2021 - 2034 ($Mn)

  • 8.1 Key trends
  • 8.2 SME
  • 8.3 Large enterprises

Chapter 9 Market Estimates & Forecast, By Application, 2021 - 2034 ($Mn)

  • 9.1 Key trends
  • 9.2 Autonomous and connected driving
  • 9.3 In-vehicle experience & infotainment
  • 9.4 Predictive maintenance & diagnostics
  • 9.5 Fleet & traffic management
  • 9.6 Cybersecurity & data protection

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($Mn)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 U.S.
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Spain
    • 10.3.6 Nordics
    • 10.3.7 Russia
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 Australia
    • 10.4.5 South Korea
    • 10.4.6 Southeast Asia
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
  • 10.6 MEA
    • 10.6.1 South Africa
    • 10.6.2 Saudi Arabia
    • 10.6.3 UAE

Chapter 11 Company Profiles

  • 11.1 Amazon
  • 11.2 Arteris IP
  • 11.3 Autotalks
  • 11.4 Bosch Group
  • 11.5 Cisco
  • 11.6 ETAS GmbH
  • 11.7 FogHorn Systems
  • 11.8 GreenWave Systems
  • 11.9 Hewlett Packard Enterprise (HPE)
  • 11.10 Huawei
  • 11.11 IBM
  • 11.12 Infineon Technologies
  • 11.13 Intel
  • 11.14 Microsoft
  • 11.15 Mobileye
  • 11.16 NVIDIA
  • 11.17 NXP Semiconductors
  • 11.18 Qualcomm Technologies
  • 11.19 Siemens
  • 11.20 Teradata