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市場調查報告書
商品編碼
2045670

以汽車市場為導向的AI模型：市場機會、成長要素、產業趨勢分析及2026-2035年預測

AI Foundation Model for 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 260 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2025 年全球汽車 AI 平台市場價值為 9 億美元，預計到 2035 年將達到 236 億美元，複合年成長率為 38.5%。

汽車市場人工智慧基礎模型-IMG1

隨著汽車製造商不斷推動人工智慧技術的應用，從試點階段邁向大規模商業化，市場正迅速擴張。 ADAS（高階駕駛輔助系統）在大眾市場的日益普及，加速了對能夠支援感知、規劃和自主決策功能的AI模型的需求。對AI訓練基礎設施、車載運算平台和大規模資料管理營運的大量投資，進一步推動了市場成長。對交通安全、運作可靠性和車輛自動化的日益重視，推動了車輛生命週期內軟體和模型的持續升級。監管趨勢也在產業成長中發揮重要作用，監管機構不斷實施更嚴格的智慧駕駛技術和自動安全系統標準。此外，低功耗汽車運算硬體和合成資料產生技術的進步，使製造商能夠提高檢驗效率、降低部署成本，並加速AI驅動的汽車平台在乘用車、商用車和車隊車輛領域的商業化進程。

市場範圍
開始年份 2025
預測期 2026-2035
上市時的市場規模 9億美元
預計金額 236億美元
複合年成長率 38.5%

汽車人工智慧加速器的進步顯著提升了現代自動駕駛系統的性能。高性能處理平台能夠在相對較低的功耗下提供數百至數千 TOPS 的處理能力，從而實現即時感知和車輛規劃，而無需承擔過高的硬體成本。同時，合成資料開發平臺正在幫助汽車製造商降低複雜駕駛場景的測試和檢驗成本，這些場景難以在實際環境中重現。這些技術進步縮短了人工智慧基礎模型從開發階段到認證部署所需的時間。這在商業應用中尤其有效，因為可衡量的安全檢驗至關重要。

到2025年，基於視覺的模型市佔率將達到22.5%。基於大規模變壓器模型，並利用海量駕駛資料集進行訓練，正被擴大用於支援車輛感知、環境解讀和駕駛決策能力。這些系統透過減少對高度設計化的介面的依賴並簡化開發流程，幫助製造商縮短檢驗週期，並在受控部署環境中提高營運效率。基礎模型同時處理多個自動駕駛任務的能力不斷增強，進一步推動了它們在下一代汽車系統中的應用。

到2025年，自主研發的商業化模型將佔據62.1%的市場佔有率，市場規模將達到5.751億美元。汽車製造商持續青睞自主研發的人工智慧平台，因為這些平台性能可靠、支援長期穩定且責任明確。監管機構在評估自動駕駛技術時，越來越要求提供詳細的文件、效能檢驗和基於情境的安全證據，這使得能夠提供由先進工具、合規框架和有保障的服務模式支援的全面整合解決方案的公司更具優勢。這種對商業化支援平台的偏好預計將繼續推動整個汽車產業對人工智慧模式的投資。

美國汽車人工智慧平台模型市場預計到2025年將達到4.906億美元，並在2026年至2035年間以38.8%的複合年成長率成長。在快速的技術創新和人工智慧驅動的出行解決方案的早期應用推動下，美國仍然是先進自動駕駛技術商業化的領先地區之一。現代汽車平臺日益普及的自動駕駛功能正在支撐著全國強勁的市場成長。隨著自動駕駛汽車、先進軟體生態系統的發展以及對智慧交通技術的持續投資，美國正在確立其在全球汽車人工智慧平台模型市場中的領先創新中心地位。預計在預測期內，積極的研發和商業化措施將進一步鞏固美國在先進車輛自動化技術領域的領先地位。

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
      • 對汽車安全和減少事故的需求日益成長
      • ADAS（進階駕駛輔助系統）的法規要求
      • 自動駕駛和ADAS平台模型的介紹
      • 生成式人工智慧與聯網汽車的融合正在穩步推進。
    • 產業潛在風險與挑戰
      • 即時推理對運算能力要求很高
      • 資料隱私問題和跨境資料傳輸限制
    • 市場機遇
      • 產生合成資料以涵蓋長尾情景
      • 基礎模型壓縮和邊緣最佳化技術
      • 部署到智慧駕駛座和車載人工智慧應用
  • 技術與創新展望
    • 最新科技趨勢
      • 基於變壓器的感知模型
      • 多模態感測器融合系統
      • 邊緣人工智慧推理平台
    • 新興技術
      • 用於自主導航的生成式世界模型
      • 合成數據生成引擎
  • 成長潛力分析
  • 監理情勢
    • 北美洲
      • 美國 - 美國國家公路交通安全管理局
      • 加拿大 - 加拿大運輸部
    • 歐洲
      • 歐盟交通運輸總司（DG MOVE）
      • 德國聯邦汽車運輸管理局 (KBA)
    • 亞太地區
      • 中國 - 工業及資訊化部（工信部）
      • 日本國土交通省
    • LATAM
      • 巴西 - 國家交通管理局 (SENATRAN)
      • 智利 - 交通部
    • 中東和非洲
      • 南非 - SASO
      • 阿拉伯聯合大公國道路和交通管理局（RTA）
  • 波特的分析
  • PESTLE分析
  • 成本細分分析
  • 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
  • 用於駕駛員輔助的多模態平台模型
    • 自主系統中視覺模型和語言模型的整合
    • 感測器融合平台模型（攝影機、LiDAR、雷達）
    • 用於車輛控制的自然語言介面
  • OEM廠商、一級供應商與AI平台：權力格局的轉變
    • 一級供應商的重新定位策略
    • 科技巨頭進入生態系統並佔據主導地位的動態
    • 開放原始碼與專有平台之爭
  • 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
  • 按公司規模進行基準測試
    • 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 市場估價與預測：依車型功能分類，2022-2035年

  • 多模態大規模語言模型（MLLM）
  • World Foundation Models
  • 願景基礎模型
  • 合成資料的生成模型
  • 端對端自動駕駛模型
  • 3D場景重建模型
  • 其他

第6章 市場估算與預測：依授權類型分類，2022-2035年

  • 開放原始碼模式
  • 專有/商業模型
  • 混合

第7章 市場估計與預測：依發展階段分類，2022-2035年

  • 基於雲端的模型
  • Edge/車用模型
  • 混合模式

第8章 市場估計與預測：依應用領域分類，2022-2035年

  • 自動駕駛車輛的規劃和運營
    • 機器人計程車服務
    • 自動化配送/貨運
  • 智慧駕駛座和車載人工智慧
  • 消費者進階駕駛輔助系統
  • 其他

第9章 市場估計與預測：依最終用途分類，2022-2035年

  • OEMs
  • 自動駕駛汽車營運商
  • 一級汽車零件供應商
  • 其他

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
    • 荷蘭
    • 瑞典
    • 瑞士
  • 亞太地區
    • 中國
    • 日本
    • 韓國
    • 印度
    • 新加坡
    • 澳洲
    • 泰國
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
    • 智利
  • 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
    • NVIDIA
    • Tesla
    • Waymo
    • Liquid AI
    • Baidu
    • General Motors
    • Mobileye
    • Scale AI
    • Zoox
    • Toyota Motor
    • Volkswagen
    • Bosch
    • Qualcomm Technologies
    • Aurora Innovation
  • 當地公司
    • Xpeng Motors
    • Momenta
    • Li Auto
  • 新興企業
    • Nuro
    • PlusAI
    • Waabi
簡介目錄
Product Code: 15828

The Global AI Foundation Model for Automotive Market was valued at USD 900 million in 2025 and is estimated to grow at a CAGR of 38.5% to reach USD 23.6 billion by 2035.

AI Foundation Model for Automotive Market - IMG1

The market is advancing rapidly as automotive manufacturers continue transitioning artificial intelligence technologies from pilot deployments to large-scale commercial integration. Increasing adoption of advanced driver assistance systems across mass-market vehicle categories is accelerating demand for AI foundation models capable of supporting perception, planning, and autonomous decision-making functions. Significant investments in AI training infrastructure, vehicle computing platforms, and large-scale data management operations are further strengthening market expansion. Growing emphasis on road safety, operational reliability, and vehicle automation is encouraging continuous software and model upgrades throughout the vehicle lifecycle. Regulatory developments are also playing a major role in industry growth, as authorities continue introducing stricter standards related to intelligent driving technologies and automated safety systems. In addition, advancements in low-power automotive computing hardware and synthetic data generation technologies are helping manufacturers improve validation efficiency, reduce deployment costs, and accelerate the commercialization of AI-powered automotive platforms across passenger, commercial, and fleet vehicle segments.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$900 Million
Forecast Value$23.6 Billion
CAGR38.5%

Advancements in automotive-grade AI accelerators are significantly improving the performance capabilities of modern autonomous systems. High-performance processing platforms are now capable of delivering hundreds to thousands of TOPS while operating under relatively low power consumption levels, enabling real-time perception and vehicle planning functions without creating excessive hardware costs. At the same time, synthetic data development pipelines are helping automotive companies lower testing and validation expenses associated with complex driving scenarios that are difficult to reproduce in physical environments. These technological improvements are reducing the time required to move AI foundation models from development stages to certified deployment, particularly in operational environments where measurable safety validation is critical for commercial implementation.

The vision foundation models segment accounted for 22.5% share in 2025. Large-scale transformer-based models trained on extensive driving datasets are increasingly being used to support vehicle perception, environmental interpretation, and driving decision functions. These systems reduce dependency on heavily engineered interfaces and simplify development processes, helping manufacturers shorten validation timelines and improve operational efficiency within controlled deployment environments. The growing ability of foundation models to manage multiple autonomous driving tasks simultaneously continues to strengthen their adoption across next-generation automotive systems.

The proprietary and commercial models segment held 62.1% share in 2025 and generated USD 575.1 million. Automotive manufacturers continue to favor proprietary AI platforms due to their validated performance, long-term support capabilities, and clearly defined accountability structures. Regulatory authorities evaluating automated driving technologies increasingly require extensive documentation, performance verification, and scenario-based safety evidence, which benefits companies capable of delivering fully integrated solutions supported by advanced tooling, compliance frameworks, and warranty-backed service models. This preference for commercially supported platforms is expected to continue driving investment across the AI foundation model for the automotive industry.

U.S. AI Foundation Model for Automotive Market reached USD 490.6 million in 2025 and is projected to grow at a CAGR of 38.8% from 2026 to 2035. The United States remains one of the leading regions for the commercialization of advanced autonomous driving technologies due to rapid technological innovation and early adoption of AI-powered mobility solutions. Increasing deployment of higher-level autonomous driving capabilities across modern vehicle platforms is supporting strong market growth throughout the country. Continued investments in autonomous vehicle development, advanced software ecosystems, and intelligent transportation technologies are positioning the United States as a key innovation hub within the global AI foundation model for automotive market. Strong research activity and commercialization initiatives are expected to further strengthen the country's leadership position in advanced vehicle automation technologies during the forecast period.

Major companies operating in the Global AI Foundation Model for Automotive Market include Aurora Innovation, Baidu, Bosch, Mobileye, Momenta, NVIDIA, Scale AI, Tesla, Waymo, and Xpeng Motors. Companies operating in the AI foundation model for the automotive market are adopting multiple strategic initiatives to strengthen their market position and expand commercial adoption. Leading players are investing heavily in artificial intelligence research, large-scale training infrastructure, and high-performance automotive computing platforms to improve autonomous driving capabilities and model accuracy. Strategic partnerships with automotive manufacturers, semiconductor providers, and mobility technology companies are helping accelerate product integration and commercialization efforts. Companies are also focusing on proprietary software ecosystems, synthetic data generation technologies, and advanced simulation platforms to improve validation efficiency and reduce deployment timelines.

Table of Contents

Chapter 1 Methodology

  • 1.1 Research approach
  • 1.2 Quality Commitments
    • 1.2.1 GMI AI policy & data integrity commitment
  • 1.3 Research Trail & Confidence Scoring
    • 1.3.1 Research Trail Components
    • 1.3.2 Scoring Components
  • 1.4 Data Collection
  • 1.5 Data mining sources
    • 1.5.1 Paid sources
  • 1.6 Base estimates and calculations
    • 1.6.1 Base year calculation
  • 1.7 Forecast
    • 1.7.1 Quantified market impact analysis
  • 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 Model Capability
    • 2.2.3 Licensing
    • 2.2.4 Deployment
    • 2.2.5 Application
    • 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 Rising Demand for Vehicle Safety and Accident Reduction
      • 3.2.1.2 Regulatory Mandates for Advanced Driver Assistance Systems
      • 3.2.1.3 Adoption of autonomous driving & ADAS foundation models
      • 3.2.1.4 Increasing integration of generative AI in connected vehicles
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 High Computational Requirements for Real-Time Inference
      • 3.2.2.2 Data Privacy Concerns and Cross-Border Data Transfer Restrictions
    • 3.2.3 Market opportunities
      • 3.2.3.1 Synthetic Data Generation for Long-Tail Scenario Coverage
      • 3.2.3.2 Foundation Model Compression and Edge Optimization Techniques
      • 3.2.3.3 Expansion into Intelligent Cockpit and In-Vehicle AI Applications
  • 3.3 Technology and innovation landscape
    • 3.3.1 Current technological trends
      • 3.3.1.1 Transformer-Based Perception Models
      • 3.3.1.2 Multimodal Sensor Fusion Systems
      • 3.3.1.3 Edge AI Inference Platforms
    • 3.3.2 Emerging technologies
      • 3.3.2.1 Generative World Models for Autonomous Navigation
      • 3.3.2.2 Synthetic Data Generation Engines
  • 3.4 Growth potential analysis
  • 3.5 Regulatory landscape
    • 3.5.1 North America
      • 3.5.1.1 US - National Highway Traffic Safety Administration
      • 3.5.1.2 Canada - Transport Canada
    • 3.5.2 Europe
      • 3.5.2.1 EU - The Directorate-General for Mobility and Transport (DG MOVE)
      • 3.5.2.2 Germany - Federal Motor Transport Authority - KBA
    • 3.5.3 Asia Pacific
      • 3.5.3.1 China - Ministry of Industry and Information Technology (MIIT)
      • 3.5.3.2 Japan - Ministry of Land, Infrastructure, Transport and Tourism (MLIT)
    • 3.5.4 LATAM
      • 3.5.4.1 Brazil - National Traffic Secretariat (SENATRAN)
      • 3.5.4.2 Chile - Ministry of Transport and Telecommunications
    • 3.5.5 MEA
      • 3.5.5.1 South Africa - SASO
      • 3.5.5.2 UAE - Roads and Transport Authority - RTA
  • 3.6 Porter’s analysis
  • 3.7 PESTEL analysis
  • 3.8 Cost breakdown 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 GenAI use cases & adoption roadmap by segment
    • 3.10.3 Risks, limitations & regulatory considerations
  • 3.11 Multimodal foundation models for driving intelligence
    • 3.11.1 Vision-Language Model Integration in Autonomous Systems
    • 3.11.2 Sensor Fusion Foundation Models (Camera-LiDAR-Radar)
    • 3.11.3 Natural Language Interface for Vehicle Control
  • 3.12 OEM vs Tier-1 vs AI platform power shift
    • 3.12.1 Tier-1 Supplier Repositioning Strategies
    • 3.12.2 Tech Giants’ Entry and Ecosystem Control Dynamics
    • 3.12.3 Open-Source vs Proprietary Platform Competition
  • 3.13 Forecast assumptions & scenario analysis (Driven by Primary Research)
    • 3.13.1 Base Case- Key Macro & Industry Variables Driving CAGR
    • 3.13.2 Optimistic Scenarios- Favorable macro and industry tailwinds
    • 3.13.3 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 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 Model Capability, 2022 - 2035 ($ Mn)

  • 5.1 Key trends
  • 5.2 Multimodal Large Language Models (MLLMs)
  • 5.3 World Foundation Models
  • 5.4 Vision Foundation Models
  • 5.5 Generative Models for Synthetic Data
  • 5.6 End-to-End Autonomous Driving Models
  • 5.7 3D Scene Reconstruction Models
  • 5.8 Others

Chapter 6 Market Estimates and Forecast, By Licensing, 2022 - 2035 ($ Mn)

  • 6.1 Key trends
  • 6.2 Open-Source Models
  • 6.3 Proprietary/Commercial Models
  • 6.4 Hybrid

Chapter 7 Market Estimates and Forecast, By Deployment, 2022 - 2035 ($ Mn)

  • 7.1 Key trends
  • 7.2 Cloud-Based Models
  • 7.3 Edge/On-Vehicle Models
  • 7.4 Hybrid Models

Chapter 8 Market Estimates and Forecast, By Application, 2022 - 2035 ($ Mn)

  • 8.1 Key trends
  • 8.2 Autonomous Vehicle Planning & Operations
    • 8.2.1 Robotaxi Services
    • 8.2.2 Autonomous Delivery & Freight
  • 8.3 Intelligent Cockpit & In-Vehicle AI
  • 8.4 Consumer ADAS
  • 8.5 Others

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

  • 9.1 Key trends
  • 9.2 OEMs
  • 9.3 Autonomous Vehicle Operators
  • 9.4 Tier-1 Automotive Suppliers
  • 9.5 Others

Chapter 10 Market Estimates & Forecast, By Region, 2022 - 2035 ($Mn)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 United Kingdom
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Spain
    • 10.3.6 Netherlands
    • 10.3.7 Sweden
    • 10.3.8 Switzerland
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 Japan
    • 10.4.3 South Korea
    • 10.4.4 India
    • 10.4.5 Singapore
    • 10.4.6 Australia
    • 10.4.7 Thailand
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
    • 10.5.4 Chile
  • 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 NVIDIA
    • 11.1.2 Tesla
    • 11.1.3 Waymo
    • 11.1.4 Liquid AI
    • 11.1.5 Baidu
    • 11.1.6 General Motors
    • 11.1.7 Mobileye
    • 11.1.8 Scale AI
    • 11.1.9 Zoox
    • 11.1.10 Toyota Motor
    • 11.1.11 Volkswagen
    • 11.1.12 Bosch
    • 11.1.13 Qualcomm Technologies
    • 11.1.14 Aurora Innovation
  • 11.2 Regional players
    • 11.2.1 Xpeng Motors
    • 11.2.2 Momenta
    • 11.2.3 Li Auto
  • 11.3 Emerging players
    • 11.3.1 Nuro
    • 11.3.2 PlusAI
    • 11.3.3 Waabi