2025-2029年全球汽車機器學習市場

全球汽車機器學習市場預計將以29.1%的複合年成長率成長，在2024年至2029年的預測期內達到119.351億美元。本報告對全球汽車機器學習市場進行了全面分析，包括市場規模和預測、趨勢、成長要素和挑戰，以及對約25家公司的供應商分析。

本報告對當前市場狀況、最新趨勢和促進因素以及整體市場環境進行了最新分析。市場成長的促進因素包括：對自動駕駛和高級駕駛輔助系統 (ADAS) 的強勁需求、消費者對提升車內體驗和預測性維護功能的日益成長的期望，以及汽車數據生成和高性能計算能力的協同成長。

本研究採用客觀的方法，結合一手和二手訊息，包括來自主要行業相關人員的意見。報告包含全面的市場規模數據、區域細分分析、供應商格局以及對主要企業的分析。報告提供歷史數據和預測數據。

報告指出，邊緣運算和混合運算架構在即時推理領域的廣泛應用是未來幾年推動全球汽車機器學習市場成長的關鍵因素之一。此外，軟體定義汽車的興起、數據貨幣化模式的演變以及機器學習應用場景在出行領域之外的多元化（例如車載系統和製造價值鏈），預計也將為市場帶來巨大的需求。

目錄

第1章執行摘要

第2章 Technavio 分析

• 價格、生命週期、顧客購買籃、採用率和購買標準分析
• 投入與差異化因素的重要性
• 混淆來源
• 促進因素和挑戰的影響

第3章 市場情勢

• 市場生態系統
• 市場特徵
• 價值鏈分析

第4章 市場規模

• 市場定義
• 市場區隔分析
• 2024年市場規模
• 2024-2029年市場展望

第5章 市場規模表現

• 2019-2023年全球汽車機器學習市場
• 2019-2023年車輛類型細分市場分析
• 2019-2023年技術細分市場分析
• 應用細分市場分析 2019-2023
• 2019-2023年區域細分市場分析
• 2019-2023年國家細分市場分析

第6章五力分析

• 五力分析概述
• 買方的議價能力
• 供應商的議價能力
• 新進入者的威脅
• 替代品的威脅
• 競爭威脅
• 市場狀況

7. 按車輛類型分類的市場細分

• 比較：按車輛類型
• 搭乘用車
• 輕型商用車
• 大型車輛
• 按車輛類型分類的市場機會

8. 按技術分類的市場細分

• 比較：按技術
• 機器學習和深度學習
• 電腦視覺
• 自然語言處理（NLP）
• 情境感知計算
• 按技術分類的市場機遇

第9章 按應用進行市場細分

• 比較：透過應用
• ADAS（進階駕駛輔助系統）
• 自動駕駛
• 車載資訊娛樂系統
• 預測性維護
• 按應用分類的市場機會

第10章 客戶情況

第11章 區域情勢

• 區域細分
• 區域比較
• 亞太地區
  • 中國
  • 日本
  • 印度
  • 韓國
  • 澳洲
  • 印尼
• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲
  • 德國
  • 英國
  • 法國
  • 義大利
  • 西班牙
  • 荷蘭
• 南美洲
  • 巴西
  • 阿根廷
  • 哥倫比亞
• 中東和非洲
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 南非
  • 以色列
  • 土耳其
• 各區域的市場機遇

第12章：促進因素、挑戰與機遇

• 市場促進因素
• 市場挑戰
• 促進因素和挑戰的影響
• 市場機遇

第13章 競爭格局

• 概述
• 競爭格局
• 令人困惑的局面
• 產業風險

第14章 競爭分析

• 公司簡介
• 企業排名指數
• 公司市場定位
• 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15章附錄

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.

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