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市场调查报告书
商品编码
1915690
2025-2029年全球汽车机器学习市场Global Machine Learning In Automobile Market 2025-2029 |
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全球汽车机器学习市场预计将以29.1%的复合年增长率成长，在2024年至2029年的预测期内达到119.351亿美元。本报告对全球汽车机器学习市场进行了全面分析，包括市场规模和预测、趋势、成长要素和挑战，以及对约25家公司的供应商分析。
本报告对当前市场状况、最新趋势和驱动因素以及整体市场环境进行了最新分析。市场成长的驱动因素包括：对自动驾驶和高级驾驶辅助系统 (ADAS) 的强劲需求、消费者对提升车内体验和预测性维护功能的日益增长的期望，以及汽车数据生成和高性能计算能力的协同增长。
本研究采用客观的方法，结合一手和二手讯息，包括来自主要行业相关人员的意见。报告包含全面的市场规模数据、区域細項分析、供应商格局以及对主要企业的分析。报告提供历史数据和预测数据。
| 市场覆盖范围 | |
|---|---|
| 基准年 | 2025 |
| 目标期 | 2029 |
| 预测期 | 2025-2029 |
| 成长势头 | 加速度 |
| 同比 | 27.5% |
| 复合年增长率 | 29.1% |
| 增量 | 119.351亿美元 |
报告指出，边缘运算和混合运算架构在即时推理领域的广泛应用是未来几年推动全球汽车机器学习市场成长的关键因素之一。此外，软体定义汽车的兴起、数据货币化模式的演变以及机器学习应用场景在出行领域之外的多元化（例如车载系统和製造价值链），预计也将为市场带来巨大的需求。
目录
第一章执行摘要
第二章 Technavio 分析
- 价格、生命週期、顾客购买篮、采用率和购买标准分析
- 投入与差异化因素的重要性
- 混淆来源
- 驱动因素和挑战的影响
第三章 市场情势
- 市场生态系统
- 市场特征
- 价值链分析
第四章 市场规模
- 市场定义
- 市场区隔分析
- 2024年市场规模
- 2024-2029年市场展望
第五章 市场规模表现
- 2019-2023年全球汽车机器学习市场
- 2019-2023年车辆类型细分市场分析
- 2019-2023年技术细分市场分析
- 应用细分市场分析 2019-2023
- 2019-2023年区域细分市场分析
- 2019-2023年国家细分市场分析
第六章五力分析
- 五力分析概述
- 买方的议价能力
- 供应商的议价能力
- 新进入者的威胁
- 替代品的威胁
- 竞争威胁
- 市场状况
7. 按车辆类型分類的市场细分
- 比较：按车辆类型
- 搭乘用车
- 轻型商用车
- 大型车辆
- 按车辆类型分類的市场机会
8. 按技术分類的市场细分
- 比较：按技术
- 机器学习和深度学习
- 电脑视觉
- 自然语言处理（NLP）
- 情境感知计算
- 按技术分類的市场机会
第九章 按应用进行市场细分
- 比较：透过应用
- ADAS（进阶驾驶辅助系统）
- 自动驾驶
- 车载资讯娱乐系统
- 预测性维护
- 按应用分類的市场机会
第十章 客户情况
第十一章 区域情势
- 区域细分
- 区域比较
- 亚太地区
- 中国
- 日本
- 印度
- 韩国
- 澳洲
- 印尼
- 北美洲
- 美国
- 加拿大
- 墨西哥
- 欧洲
- 德国
- 英国
- 法国
- 义大利
- 西班牙
- 荷兰
- 南美洲
- 巴西
- 阿根廷
- 哥伦比亚
- 中东和非洲
- 沙乌地阿拉伯
- 阿拉伯聯合大公国
- 南非
- 以色列
- 土耳其
- 各区域的市场机会
第十二章：驱动因素、挑战与机会
- 市场驱动因素
- 市场挑战
- 驱动因素和挑战的影响
- 市场机会
第十三章 竞争格局
- 概述
- 竞争格局
- 令人困惑的局面
- 产业风险
第十四章 竞争分析
- 公司简介
- 企业排名指数
- 公司市场定位
- Aptiv Plc
- BMW AG
- Continental AG
- Ford Motor Co.
- Huawei Technologies Co. Ltd.
- Intel Corp.
- Magna International Inc.
- Mobileye Technologies Ltd.
- NVIDIA Corp.
- Qualcomm Inc.
- Robert Bosch GmbH
- Tesla Inc.
- Toyota Motor Corp.
- Volkswagen AG
- Waymo LLC
第十五章附录
The global machine learning in automobile market is forecasted to grow by USD 11935.1 mn during 2024-2029, accelerating at a CAGR of 29.1% during the forecast period. The report on the global machine learning in automobile market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.
The report offers an up-to-date analysis regarding the current market scenario, the latest trends and drivers, and the overall market environment. The market is driven by imperative for autonomous driving and advanced driver assistance systems (adas), rising consumer expectations for enhanced in-vehicle experience and predictive maintenance capabilities, synergistic growth of automotive data generation and high performance computing capabilities.
The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market size data, segment with regional analysis and vendor landscape in addition to an analysis of the key companies. Reports have historic and forecast data.
| Market Scope | |
|---|---|
| Base Year | 2025 |
| End Year | 2029 |
| Series Year | 2025-2029 |
| Growth Momentum | Accelerate |
| YOY 2025 | 27.5% |
| CAGR | 29.1% |
| Incremental Value | $11935.1 mn |
Technavio's global machine learning in automobile market is segmented as below:
By Vehicle Type
- Passenger cars
- Light commercial vehicles
- Heavy vehicles
By Technology
- ML and deep learning
- Computer vision
- NLP
- Context-aware computing
By Application
- Advanced driver assistance systems
- Autonomous driving
- In-vehicle infotainment
- Predictive maintenance
Geography
- APAC
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- North America
- US
- Canada
- Mexico
- Europe
- Germany
- UK
- France
- Italy
- Spain
- The Netherlands
- South America
- Brazil
- Argentina
- Colombia
- Middle East and Africa
- UAE
- South Africa
- Turkey
- Rest of World (ROW)
This study identifies the proliferation of edge and hybrid computing architectures for real time inference as one of the prime reasons driving the global machine learning in automobile market growth during the next few years. Also, emergence of software defined vehicle and data monetization models and diversification of machine learning use cases beyond mobility into in-cabin systems and manufacturing value chain will lead to sizable demand in the market.
The report on the global machine learning in automobile market covers the following areas:
- Global machine learning in automobile market sizing
- Global machine learning in automobile market forecast
- Global machine learning in automobile market industry analysis
The robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading global machine learning in automobile market vendors that include Aptiv Plc, BMW AG, Cerence Inc., Continental AG, Cruise.com Inc., DeepSeek, Ford Motor Co., Huawei Technologies Co. Ltd., Intel Corp., Magna International Inc., Mobileye Technologies Ltd., MORAI Inc., NVIDIA Corp., Qualcomm Inc., Robert Bosch GmbH, Tesla Inc., Toyota Motor Corp., Volkswagen AG, Waymo LLC. Also, the global machine learning in automobile market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage all forthcoming growth opportunities.
The publisher presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive primary and secondary research. The market research reports provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast accurate market growth.
Table of Contents
1 Executive Summary
- 1.1 Market overview
- Executive Summary - Chart on Market Overview
- Executive Summary - Data Table on Market Overview
- Executive Summary - Chart on Global Market Characteristics
- Executive Summary - Chart on Market by Geography
- Executive Summary - Chart on Market Segmentation by Vehicle Type
- Executive Summary - Chart on Market Segmentation by Technology
- Executive Summary - Chart on Market Segmentation by Application
- Executive Summary - Chart on Incremental Growth
- Executive Summary - Data Table on Incremental Growth
- Executive Summary - Chart on Company Market Positioning
2 Technavio Analysis
- 2.1 Analysis of price sensitivity, lifecycle, customer purchase basket, adoption rates, and purchase criteria
- Analysis of price sensitivity, lifecycle, customer purchase basket, adoption rates, and purchase criteria
- 2.2 Criticality of inputs and Factors of differentiation
- 2.3 Factors of disruption
- 2.4 Impact of drivers and challenges
3 Market Landscape
- 3.1 Market ecosystem
- 3.2 Market characteristics
- 3.3 Value chain analysis
4 Market Sizing
- 4.1 Market definition
- 4.2 Market segment analysis
- Market segments
- 4.3 Market size 2024
- 4.4 Market outlook: Forecast for 2024-2029
5 Historic Market Size
- 5.1 Global Machine Learning In Automobile Market 2019 - 2023
- Historic Market Size - Data Table on Global Machine Learning In Automobile Market 2019 - 2023 ($ million)
- 5.2 Vehicle Type segment analysis 2019 - 2023
- Historic Market Size - Vehicle Type Segment 2019 - 2023 ($ million)
- 5.3 Technology segment analysis 2019 - 2023
- Historic Market Size - Technology Segment 2019 - 2023 ($ million)
- 5.4 Application segment analysis 2019 - 2023
- Historic Market Size - Application Segment 2019 - 2023 ($ million)
- 5.5 Geography segment analysis 2019 - 2023
- Historic Market Size - Geography Segment 2019 - 2023 ($ million)
- 5.6 Country segment analysis 2019 - 2023
- Historic Market Size - Country Segment 2019 - 2023 ($ million)
6 Five Forces Analysis
- 6.1 Five forces summary
- Five forces analysis - Comparison between 2024 and 2029
- 6.2 Bargaining power of buyers
- Bargaining power of buyers - Impact of key factors 2024 and 2029
- 6.3 Bargaining power of suppliers
- Bargaining power of suppliers - Impact of key factors in 2024 and 2029
- 6.4 Threat of new entrants
- Threat of new entrants - Impact of key factors in 2024 and 2029
- 6.5 Threat of substitutes
- Threat of substitutes - Impact of key factors in 2024 and 2029
- 6.6 Threat of rivalry
- Threat of rivalry - Impact of key factors in 2024 and 2029
- 6.7 Market condition
7 Market Segmentation by Vehicle Type
- 7.1 Market segments
- 7.2 Comparison by Vehicle Type
- 7.3 Passenger cars - Market size and forecast 2024-2029
- 7.4 Light commercial vehicles - Market size and forecast 2024-2029
- 7.5 Heavy vehicles - Market size and forecast 2024-2029
- 7.6 Market opportunity by Vehicle Type
- Market opportunity by Vehicle Type ($ million)
8 Market Segmentation by Technology
- 8.1 Market segments
- 8.2 Comparison by Technology
- 8.3 ML and deep learning - Market size and forecast 2024-2029
- 8.4 Computer vision - Market size and forecast 2024-2029
- 8.5 NLP - Market size and forecast 2024-2029
- 8.6 Context-aware computing - Market size and forecast 2024-2029
- 8.7 Market opportunity by Technology
- Market opportunity by Technology ($ million)
9 Market Segmentation by Application
- 9.1 Market segments
- 9.2 Comparison by Application
- 9.3 Advanced driver assistance systems - Market size and forecast 2024-2029
- 9.4 Autonomous driving - Market size and forecast 2024-2029
- 9.5 In-vehicle infotainment - Market size and forecast 2024-2029
- 9.6 Predictive maintenance - Market size and forecast 2024-2029
- 9.7 Market opportunity by Application
- Market opportunity by Application ($ million)
10 Customer Landscape
- 10.1 Customer landscape overview
- Analysis of price sensitivity, lifecycle, customer purchase basket, adoption rates, and purchase criteria
11 Geographic Landscape
- 11.1 Geographic segmentation
- 11.2 Geographic comparison
- 11.3 APAC - Market size and forecast 2024-2029
- 11.3.1 China - Market size and forecast 2024-2029
- 11.3.2 Japan - Market size and forecast 2024-2029
- 11.3.3 India - Market size and forecast 2024-2029
- 11.3.4 South Korea - Market size and forecast 2024-2029
- 11.3.5 Australia - Market size and forecast 2024-2029
- 11.3.6 Indonesia - Market size and forecast 2024-2029
- 11.4 North America - Market size and forecast 2024-2029
- 11.4.1 US - Market size and forecast 2024-2029
- 11.4.2 Canada - Market size and forecast 2024-2029
- 11.4.3 Mexico - Market size and forecast 2024-2029
- 11.5 Europe - Market size and forecast 2024-2029
- 11.5.1 Germany - Market size and forecast 2024-2029
- 11.5.2 UK - Market size and forecast 2024-2029
- 11.5.3 France - Market size and forecast 2024-2029
- 11.5.4 Italy - Market size and forecast 2024-2029
- 11.5.5 Spain - Market size and forecast 2024-2029
- 11.5.6 The Netherlands - Market size and forecast 2024-2029
- 11.6 South America - Market size and forecast 2024-2029
- 11.6.1 Brazil - Market size and forecast 2024-2029
- 11.6.2 Argentina - Market size and forecast 2024-2029
- 11.6.3 Colombia - Market size and forecast 2024-2029
- 11.7 Middle East and Africa - Market size and forecast 2024-2029
- 11.7.1 Saudi Arabia - Market size and forecast 2024-2029
- 11.7.2 UAE - Market size and forecast 2024-2029
- 11.7.3 South Africa - Market size and forecast 2024-2029
- 11.7.4 Israel - Market size and forecast 2024-2029
- 11.7.5 Turkey - Market size and forecast 2024-2029
- 11.8 Market opportunity by geography
- Market opportunity by geography ($ million)
- Data Tables on Market opportunity by geography ($ million)
12 Drivers, Challenges, and Opportunity
- 12.1 Market drivers
- Imperative for autonomous driving and advanced driver assistance systems (ADAS)
- Rising consumer expectations for enhanced in-vehicle experience and predictive maintenance capabilities
- Synergistic growth of automotive data generation and high performance computing capabilities
- 12.2 Market challenges
- Addressing immense data requirements and verification and validation conundrum
- Navigating nascent and fragmented regulatory, legal, and ethical landscape
- Overcoming technical integration complexity, cybersecurity threats, and prohibitive costs
- 12.3 Impact of drivers and challenges
- Impact of drivers and challenges in 2024 and 2029
- 12.4 Market opportunities
- Proliferation of edge and hybrid computing architectures for real time inference
- Emergence of software defined vehicle and data monetization models
- Diversification of machine learning use cases beyond mobility into in-cabin systems and manufacturing value chain
13 Competitive Landscape
- 13.1 Overview
- 13.2 Competitive Landscape
- Overview on criticality of inputs and factors of differentiation
- 13.3 Landscape disruption
- Overview on factors of disruption
- 13.4 Industry risks
- Impact of key risks on business
14 Competitive Analysis
- 14.1 Companies profiled
- Companies covered
- 14.2 Company ranking index
- Company ranking index
- 14.3 Market positioning of companies
- Matrix on companies position and classification
- 14.4 Aptiv Plc
- Aptiv Plc - Overview
- Aptiv Plc - Business segments
- Aptiv Plc - Key news
- Aptiv Plc - Key offerings
- Aptiv Plc - Segment focus
- SWOT
- 14.5 BMW AG
- BMW AG - Overview
- BMW AG - Business segments
- BMW AG - Key offerings
- BMW AG - Segment focus
- SWOT
- 14.6 Continental AG
- Continental AG - Overview
- Continental AG - Business segments
- Continental AG - Key news
- Continental AG - Key offerings
- Continental AG - Segment focus
- SWOT
- 14.7 Ford Motor Co.
- Ford Motor Co. - Overview
- Ford Motor Co. - Business segments
- Ford Motor Co. - Key offerings
- Ford Motor Co. - Segment focus
- SWOT
- 14.8 Huawei Technologies Co. Ltd.
- Huawei Technologies Co. Ltd. - Overview
- Huawei Technologies Co. Ltd. - Product / Service
- Huawei Technologies Co. Ltd. - Key news
- Huawei Technologies Co. Ltd. - Key offerings
- SWOT
- 14.9 Intel Corp.
- Intel Corp. - Overview
- Intel Corp. - Business segments
- Intel Corp. - Key news
- Intel Corp. - Key offerings
- Intel Corp. - Segment focus
- SWOT
- 14.10 Magna International Inc.
- Magna International Inc. - Overview
- Magna International Inc. - Business segments
- Magna International Inc. - Key news
- Magna International Inc. - Key offerings
- Magna International Inc. - Segment focus
- SWOT
- 14.11 Mobileye Technologies Ltd.
- Mobileye Technologies Ltd. - Overview
- Mobileye Technologies Ltd. - Product / Service
- Mobileye Technologies Ltd. - Key offerings
- SWOT
- 14.12 NVIDIA Corp.
- NVIDIA Corp. - Overview
- NVIDIA Corp. - Business segments
- NVIDIA Corp. - Key news
- NVIDIA Corp. - Key offerings
- NVIDIA Corp. - Segment focus
- SWOT
- 14.13 Qualcomm Inc.
- Qualcomm Inc. - Overview
- Qualcomm Inc. - Business segments
- Qualcomm Inc. - Key news
- Qualcomm Inc. - Key offerings
- Qualcomm Inc. - Segment focus
- SWOT
- 14.14 Robert Bosch GmbH
- Robert Bosch GmbH - Overview
- Robert Bosch GmbH - Product / Service
- Robert Bosch GmbH - Key news
- Robert Bosch GmbH - Key offerings
- SWOT
- 14.15 Tesla Inc.
- Tesla Inc. - Overview
- Tesla Inc. - Business segments
- Tesla Inc. - Key news
- Tesla Inc. - Key offerings
- Tesla Inc. - Segment focus
- SWOT
- 14.16 Toyota Motor Corp.
- Toyota Motor Corp. - Overview
- Toyota Motor Corp. - Business segments
- Toyota Motor Corp. - Key news
- Toyota Motor Corp. - Key offerings
- Toyota Motor Corp. - Segment focus
- SWOT
- 14.17 Volkswagen AG
- Volkswagen AG - Overview
- Volkswagen AG - Business segments
- Volkswagen AG - Key news
- Volkswagen AG - Key offerings
- Volkswagen AG - Segment focus
- SWOT
- 14.18 Waymo LLC
- Waymo LLC - Overview
- Waymo LLC - Product / Service
- Waymo LLC - Key offerings
- SWOT
15 Appendix
- 15.1 Scope of the report
- Market definition
- Objectives
- Notes and caveats
- 15.2 Inclusions and exclusions checklist
- Inclusions checklist
- Exclusions checklist
- 15.3 Currency conversion rates for US$
- Currency conversion rates for US$
- 15.4 Research methodology
- Research methodology
- 15.5 Data procurement
- Information sources
- 15.6 Data validation
- Data validation
- 15.7 Validation techniques employed for market sizing
- Validation techniques employed for market sizing
- 15.8 Data synthesis
- Data synthesis
- 15.9 360 degree market analysis
- 360 degree market analysis
- 15.10 List of abbreviations
- List of abbreviations
