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市場調查報告書
商品編碼
2061483

汽車產業生成式人工智慧的市場機會、成長促進因素、產業趨勢與預測（2026-2035 年）

Generative AI in Automotive Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 240 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

2025 年全球汽車產業生成式人工智慧市場價值 6.627 億美元，預計到 2035 年將達到 76 億美元，複合年成長率為 27.3%。

汽車市場中的生成式人工智慧-IMG1

隨著汽車產業日益向軟體定義車輛（SDV）架構轉型，市場正迅速擴張。在SDV架構中，數位系統在設計、製造、診斷和使用者體驗等各個環節都扮演著核心角色。生成式人工智慧（GI）透過自動化軟體程式碼產生、測試工作流程、檢驗流程和需求定義，顯著提升了開發效率，並透過持續的空中升級（OTA）加速了開發週期。隨著下一代汽車變得越來越複雜，人們越來越依賴人工智慧驅動的解決方案來有效管理軟體密集生態系統。此外，生成式人工智慧透過產生模擬罕見複雜駕駛場景的合成環境，顯著降低了對真實世界測試的依賴，從而改變了自動駕駛技術的發展。這提高了訓練效率，增強了模型的穩健性。同時，消費者對智慧車載體驗的期望不斷提高，推動了自然語言模型的應用，以實現對話式互動、智慧導航和高級資訊娛樂功能，所有這些都將汽車駕駛座轉變為數位體驗中心。

市場範圍
開始年份 2025
預測期 2026-2035
初始市場規模 6.627億美元
預測市場規模 76億美元
複合年成長率 27.3%

數位雙胞胎和模擬人工智慧領域預計在2025年將佔據28%的市場佔有率，並在2026年至2035年間以26.6%的複合年成長率成長。該領域專注於創建車輛、生產系統和駕駛環境的虛​​擬副本，以實現持續的模擬和測試。在生成式人工智慧驅動的汽車生態系統中，這些工具被廣泛用於檢驗自動駕駛系統、預測維護需求和最佳化製造流程。提高開發速度和創新效率，同時減少對實體測試的依賴，是推動其應用的主要動力。

預計到2025年，基於雲端的部署方案將佔據48.2%的市場佔有率，並在2035年之前以27.5%的複合年成長率成長。雲端基礎設施使汽車製造商能夠利用可擴展的運算資源來訓練和部署生成式人工智慧模型，包括大規模語言模型、合成資料引擎和數位雙胞胎系統。這種部署方式支援即時系統更新、工程團隊間的全球協作以及靈活的成本結構。它被廣泛應用於軟體定義汽車生態系統中的自動駕駛模擬和車載人工智慧應用。

美國汽車產業的生成式人工智慧市場預計到2025年將達到1.988億美元，並在2026年至2035年間以26.1%的複合年成長率成長。美國仍然是人工智慧主導出行領域創新的領先中心，這得益於其先進的自動駕駛研發專案以及汽車製造商和科技公司之間的緊密合作。高效能運算和人工智慧平台的融合正在加速下一代出行解決方案的模擬、訓練和部署。此外，自動駕駛系統的監管框架正在推動基於人工智慧的檢驗和測試技術的應用，進一步促進市場擴張。

目錄

第1章：調查方法

第2章執行摘要

第3章 行業洞察

  • 產業生態系分析
    • 供應商情況
    • 利潤率
    • 成本結構
    • 每個階段增加的價值
    • 影響價值鏈的因素
    • 中斷
  • 影響產業的因素
    • 促進因素
      • 軟體定義車輛的廣泛應用
      • 自動駕駛數據的需求
      • 原始設備製造商面臨最佳化成本的壓力
      • 車載人工智慧助理的普及
    • 產業潛在風險與挑戰
      • 對車輛資料隱私的擔憂
      • 人工智慧基礎設施成本高昂
    • 市場機遇
      • 生成式車輛設計簡介
      • 人工智慧在商用車領域的應用拓展
      • 新興市場採用潛力
      • 面向汽車產業的跨產業人工智慧解決方案
  • 成長潛力分析
  • 技術與創新展望
    • 最新科技趨勢
    • 新興技術
  • 成本細分分析
  • 監理情勢
    • 北美洲
      • 美國國家標準與技術研究院
      • 加拿大創新、科學與經濟發展部
    • 歐洲
      • 歐盟委員會
      • 歐洲電訊標準組織
    • 亞太地區
      • 工業資訊技術部
      • 經濟產業省
    • 拉丁美洲
      • 科學、技術和創新部
      • 國家統計局
    • 中東和非洲
      • 沙烏地阿拉伯資料與人工智慧局
      • 通訊及數位技術局
  • 波特的分析
  • PESTLE分析
  • 專利分析
  • 人工智慧和生成式人工智慧對市場的影響
    • 利用人工智慧改造現有經營模式
    • 按細分市場分類的生成式人工智慧用例和部署藍圖
    • 風險、限制和監管考量
  • 永續性和環境方面
    • 永續計劃
    • 減少廢棄物策略
    • 生產中的能源效率
    • 具有環保意識的舉措
    • 考慮碳足跡
  • 預測假設和情境分析
    • 基本案例：驅動複合年成長率的關鍵宏觀經濟與產業變量
    • 樂觀情境：宏觀經濟與產業的順風
    • 悲觀情景：宏觀經濟放緩或產業逆風

第4章 競爭情勢

  • 介紹
  • 企業市佔率分析
    • 北美洲
    • 歐洲
    • 亞太地區
    • LATAM
    • 中東和非洲
  • 主要市場公司的競爭分析
  • 競爭定位矩陣
  • 主要進展
    • 併購
    • 夥伴關係和聯盟
    • 新產品發布
    • 業務拓展計劃及資金籌措
  • 4.6 按公司規模分類的基準
    • 排名分類標準與遴選標準
    • 按銷售額、地區和創新能力分類的層級定位矩陣。

第5章 市場估計與預測：依技術分類，2022-2035年

  • 大規模語言模型（LLM）和自然語言處理（NLP）
  • 衍生設計與電腦視覺
  • 合成數據的生成
  • 數位雙胞胎與模擬人工智慧
  • 人工智慧代理和副駕駛

第6章 市場估計與預測：依應用領域分類，2022-2035年

  • 車輛設計與工程
  • 自動駕駛和高級駕駛輔助系統（ADAS）的發展
  • 生產/品管
  • 軟體開發與測試
  • 車內體驗與客戶互動
  • 供應鏈與採購
  • 預測性維護和診斷

第7章 市場估價與預測：依車輛類型分類，2022-2035年

  • 搭乘用車
    • 轎車
    • SUV
    • 掀背車
  • 商用車輛
    • LCV
    • MCV
    • HCV

第8章 市場估算與預測：依部署模式分類，2022-2035年

  • 基於雲端的
  • 現場
  • 混合

第9章 市場估計與預測：依最終用途分類，2022-2035年

  • 汽車原廠設備製造商
  • 一級和二級供應商
  • 汽車軟體和技術供應商
  • 車隊營運商和售後服務服務供應商

第10章 市場估價與預測：依地區分類，2022-2035年

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 荷蘭
    • 挪威
    • 瑞典
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲
    • 泰國
    • 印尼
    • 新加坡
    • 馬來西亞
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中東和非洲
    • 南非
    • 沙烏地阿拉伯
    • UAE

第11章：公司簡介

  • 世界公司
    • Autodesk
    • Bosch
    • Google
    • Microsoft
    • Mobileye
    • NVIDIA
    • PTC
    • Qualcomm
    • Siemens
    • Tesla
  • 當地公司
    • Baidu
    • BYD
    • Huawei
    • KPIT Technologies
    • Pony.ai
    • Xpeng
  • 新興企業
    • Aurora Innovation
    • Waabi
    • Wayve
簡介目錄
Product Code: 14635

The Global Generative AI In Automotive Market was valued at USD 662.7 million in 2025 and is estimated to grow at a CAGR of 27.3% to reach USD 7.6 billion in 2035.

Generative AI in Automotive Market - IMG1

The market is experiencing rapid expansion as the automotive industry increasingly shifts toward software-defined vehicle architectures, where digital systems play a central role in design, manufacturing, diagnostics, and user experience. Generative AI enables major advancements by automating software code generation, testing workflows, validation processes, and requirements engineering while also accelerating development cycles through continuous over-the-air update capabilities. The growing complexity of next-generation vehicles is increasing reliance on AI-driven solutions to manage software-intensive ecosystems efficiently. In addition, generative AI is transforming autonomous mobility development by producing synthetic environments that replicate rare and complex driving scenarios, significantly reducing dependency on physical testing. This improves training efficiency and enhances model robustness. At the same time, rising consumer expectations for intelligent in-vehicle experiences are driving adoption of natural language models that enable conversational interaction, personalized recommendations, smart navigation, and advanced infotainment features, collectively reshaping the automotive cockpit into a digital experience hub.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$662.7 Million
Forecast Value$7.6 Billion
CAGR27.3%

The digital twins & simulation AI segment held a 28% share in 2025 and is projected to grow at a CAGR of 26.6% from 2026 to 2035. This segment focuses on creating virtual replicas of vehicles, production systems, and driving environments to enable continuous simulation and testing. In the generative AI automotive ecosystem, these tools are widely used for validating autonomous driving systems, forecasting maintenance requirements, and optimizing manufacturing workflows. Their ability to reduce reliance on physical testing while enhancing development speed and innovation efficiency is driving strong adoption.

The cloud-based deployment segment held a 48.2% share in 2025 and is expected to grow at a CAGR of 27.5% through 2035. Cloud infrastructure enables automotive companies to access scalable computing resources for training and deploying generative AI models, including large language models, synthetic data engines, and digital twin systems. This deployment approach supports real-time system updates, global collaboration across engineering teams, and flexible cost structures. It is widely used for autonomous driving simulations and in-vehicle AI applications within software-defined automotive ecosystems.

United States Generative AI In Automotive Market reached USD 198.8 million in 2025 and is projected to grow at a CAGR of 26.1% from 2026 to 2035. The country remains a key hub for innovation in AI-driven mobility, supported by advanced autonomous driving development programs and strong collaboration between automotive and technology companies. The integration of high-performance computing and AI platforms is accelerating simulation, training, and deployment of next-generation mobility solutions. Regulatory frameworks governing autonomous driving systems are also encouraging the use of AI-based validation and testing technologies, further supporting market expansion.

Major companies operating in the Global Generative AI In Automotive Industry include Autodesk, Amazon Web Services, Baidu, Bosch, Google, Microsoft, NVIDIA, PTC, Qualcomm, and Siemens. Companies operating in the generative AI in automotive market are focusing on strengthening their position through heavy investment in AI model development tailored for automotive-grade applications such as autonomous driving, predictive maintenance, and in-vehicle experience systems. They are expanding cloud-native AI platforms to provide scalable computing infrastructure for training and deploying large-scale generative models. Strategic collaborations with automakers, semiconductor firms, and mobility service providers are being prioritized to accelerate ecosystem integration. Firms are also investing in digital twin technologies and simulation environments to improve testing efficiency and reduce development cycles. Another key strategy includes integrating generative AI with edge computing systems to enable real-time vehicle intelligence and decision-making. Companies are further focusing on enhancing data security, model accuracy, and regulatory compliance to support safe deployment in autonomous systems.

Table of Contents

Chapter 1 Methodology

  • 1.1 Research approach
  • 1.2 Quality Commitments
    • 1.2.1 GMI AI policy & data integrity commitment
      • 1.2.1.1 Source consistency protocol
  • 1.3 Research Trail & Confidence Scoring
    • 1.3.1 Research Trail Components
    • 1.3.2 Scoring Components
  • 1.4 Data Collection
    • 1.4.1 Partial list of primary sources
  • 1.5 Data mining sources
    • 1.5.1 Paid sources
      • 1.5.1.1 Sources, by region
  • 1.6 Base estimates and calculations
    • 1.6.1 Base year calculation
  • 1.7 Forecast model
    • 1.7.1 Quantified market impact analysis
      • 1.7.1.1 Mathematical impact of growth parameters on forecast
  • 1.8 Research transparency addendum
    • 1.8.1 Source attribution framework
    • 1.8.2 Quality assurance metrics
    • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Technology
    • 2.2.3 Application
    • 2.2.4 Vehicle
    • 2.2.5 Deployment mode
    • 2.2.6 End use
  • 2.3 TAM analysis, 2026-2035
  • 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin
    • 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 Software-defined vehicle adoption growth
      • 3.2.1.2 Autonomous driving data demand
      • 3.2.1.3 OEM cost optimization pressure
      • 3.2.1.4 In-Vehicle AI assistant expansion
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Vehicle data privacy concerns
      • 3.2.2.2 High AI infrastructure costs
    • 3.2.3 Market opportunities
      • 3.2.3.1 Generative vehicle design adoption
      • 3.2.3.2 Commercial fleet AI expansion
      • 3.2.3.3 Emerging market deployment potential
      • 3.2.3.4 Cross-industry automotive AI solutions
  • 3.3 Growth potential analysis
  • 3.4 Technology and innovation landscape
    • 3.4.1 Current technological trends
    • 3.4.2 Emerging technologies
  • 3.5 Cost breakdown analysis
  • 3.6 Regulatory landscape
    • 3.6.1 North America
      • 3.6.1.1 National Institute of Standards and Technology
      • 3.6.1.2 Innovation, Science and Economic Development Canada
    • 3.6.2 Europe
      • 3.6.2.1 European Commission
      • 3.6.2.2 European Telecommunications Standards Institute
    • 3.6.3 Asia Pacific
      • 3.6.3.1 Ministry of Industry and Information Technology
      • 3.6.3.2 Ministry of Economy, Trade and Industry
    • 3.6.4 Latin America
      • 3.6.4.1 Ministry of Science, Technology and Innovation
      • 3.6.4.2 National Institute of Statistics and Geography
    • 3.6.5 Middle East & Africa
      • 3.6.5.1 Saudi Data and Artificial Intelligence Authority
      • 3.6.5.2 Department of Communications and Digital Technologies
  • 3.7 Porter’s analysis
  • 3.8 PESTEL analysis
  • 3.9 Patent analysis (Driven by Primary Research)
  • 3.10 Impact of AI & Generative AI on the Market
    • 3.10.1 AI-driven disruption of existing business models
    • 3.10.2 Gen AI use cases & adoption roadmap by segment
    • 3.10.3 Risks, limitations & regulatory considerations
  • 3.11 Sustainability and environmental aspects
    • 3.11.1 Sustainable practices
    • 3.11.2 Waste reduction strategies
    • 3.11.3 Energy efficiency in production
    • 3.11.4 Eco-friendly initiatives
    • 3.11.5 Carbon footprint considerations
  • 3.12 Forecast assumptions & scenario analysis (Driven by primary research)
    • 3.12.1 Base Case - key macro & industry variables driving CAGR
    • 3.12.2 Optimistic Scenarios - Favorable macro and industry tailwinds
    • 3.12.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

  • 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 Key developments
    • 4.5.1 Mergers & acquisitions
    • 4.5.2 Partnerships & collaborations
    • 4.5.3 New product launches
    • 4.5.4 Expansion plans and funding
  • 4.6 4.6 Company tier benchmarking
    • 4.6.1 Tier classification criteria & qualifying thresholds
    • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates and Forecast, By Technology, 2022 - 2035 ($ Million)

  • 5.1 Key trends
  • 5.2 Large Language Models (LLMs) & NLP
  • 5.3 Generative Design & Computer Vision
  • 5.4 Synthetic Data Generation
  • 5.5 Digital Twins & Simulation AI
  • 5.6 AI Agents & Copilots

Chapter 6 Market Estimates and Forecast, By Application, 2022 - 2035 ($ Million)

  • 6.1 Key trends
  • 6.2 Vehicle Design & Engineering
  • 6.3 Autonomous Driving & ADAS Development
  • 6.4 Manufacturing & Quality Control
  • 6.5 Software Development & Testing
  • 6.6 In-Vehicle Experience & Customer Interaction
  • 6.7 Supply Chain & Procurement
  • 6.8 Predictive Maintenance & Diagnostics

Chapter 7 Market Estimates and Forecast, By Vehicle, 2022 - 2035 ($ Million)

  • 7.1 Key trends
  • 7.2 Passenger cars
    • 7.2.1 Sedan
    • 7.2.2 SUV
    • 7.2.3 Hatchback
  • 7.3 Commercial Vehicles
    • 7.3.1 LCV
    • 7.3.2 MCV
    • 7.3.3 HCV

Chapter 8 Market Estimates and Forecast, By Deployment Mode, 2022 - 2035 ($ Million)

  • 8.1 Key trends
  • 8.2 Cloud-Based
  • 8.3 On-Premises
  • 8.4 Hybrid

Chapter 9 Market Estimates and Forecast, By End Use, 2022 - 2035 ($ Million)

  • 9.1 Key trends
  • 9.2 Automotive OEMs
  • 9.3 Tier-1 & Tier-2 Suppliers
  • 9.4 Automotive Software & Technology Providers
  • 9.5 Fleet Operators & Aftermarket Service Providers

Chapter 10 Market Estimates & Forecast, By Region, 2022 - 2035 ($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 Russia
    • 10.3.7 Netherlands
    • 10.3.8 Norway
    • 10.3.9 Sweden
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 South Korea
    • 10.4.5 Australia
    • 10.4.6 Thailand
    • 10.4.7 Indonesia
    • 10.4.8 Singapore
    • 10.4.9 Malaysia
  • 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 Global players
    • 11.1.1 Autodesk
    • 11.1.2 Bosch
    • 11.1.3 Google
    • 11.1.4 Microsoft
    • 11.1.5 Mobileye
    • 11.1.6 NVIDIA
    • 11.1.7 PTC
    • 11.1.8 Qualcomm
    • 11.1.9 Siemens
    • 11.1.10 Tesla
  • 11.2 Regional players
    • 11.2.1 Baidu
    • 11.2.2 BYD
    • 11.2.3 Huawei
    • 11.2.4 KPIT Technologies
    • 11.2.5 Pony.ai
    • 11.2.6 Xpeng
  • 11.3 Emerging players
    • 11.3.1 Aurora Innovation
    • 11.3.2 Waabi
    • 11.3.3 Wayve