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市场调查报告书
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1822585

汽车市场中的生成式人工智慧机会、成长动力、产业趋势分析及 2025 - 2034 年预测

Generative AI in Automotive Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 230 Pages | 商品交期: 2-3个工作天内

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简介目录

2024 年全球汽车生成人工智慧市场价值为 5.066 亿美元，预计到 2034 年将以 23.8% 的复合年增长率增长至 45.8 亿美元。

汽车市场的生成式人工智慧 - IMG1

随着汽车製造商越来越多地整合生成式人工智慧来简化自动驾驶系统、优化设计工作流程并模拟关键驾驶场景，汽车产业正在经历快速转型。监管部门的鼓励和资金支持正在推动汽车製造商、零件供应商和出行技术创新者的研发。随着数位化进程的深入以及汽车智慧化和互联化程度的提高，生成式人工智慧正成为汽车开发的核心。它使汽车製造商能够复製罕见或复杂的交通事件，从而大幅缩短安全验证所需的时间和成本。这种先进的功能正在树立模拟精度的新标准，并有助于缩短开发週期。汽车製造商目前正在利用生成式人工智慧来增强使用者介面、预测维护需求并微调进阶驾驶辅助系统。随着汽车产业向软体定义汽车和连网平台转型，人工智慧不再只是一种增强功能，而是下一代出行生态系统的核心推动力。软体公司和硬体开发商之间的合作正在建立基础设施，以支援人工智慧在汽车环境中的无缝整合。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 5.066亿美元
预测值 45.8亿美元
复合年增长率 23.8%

2024年，乘用车市场占了68%的市场份额，这得益于智慧系统在车辆领域的广泛应用。人工智慧技术如今已深深融入高级资讯娱乐、驾驶支援功能和车载安全系统等功能中。这些工具透过对话介面改善互动体验，提供个人化洞察，并支援即时自适应回应，显着提升了驾驶体验。汽车製造商正专注于开发能够增强安全性和功能性的人工智慧工具，例如主动服务通知和情境感知驾驶建议等功能。随着感测器技术和远端软体更新的不断改进，生成式人工智慧在该领域的应用预计将稳步增长。

预计2025年至2034年，内燃机 (ICE) 汽车市场的复合年增长率将达到14.8%。虽然电动车平台通常处于技术应用的前沿，但内燃机汽车也在整合人工智慧驱动的系统以保持竞争力。汽车製造商正在升级现有的内燃机车型，为其配备智慧模组，以支援更强大的诊断功能、无缝互联和沈浸式数位体验。这项变革的驱动力来自于高阶内燃机汽车对智慧功能日益增长的需求，如今，透过无线更新和可扩展平台技术，这些汽车更容易实现基于人工智慧系统的改装。增强型车载软体使传统车型无需进行大规模硬体重新设计即可受益于先进的预测功能。

2024年，美国汽车生成式人工智慧市场产值达1.488亿美元。凭藉强大的创新格局、雄厚的研发实力以及与学术机构、技术供应商和政府机构的通力合作，美国将继续保持领先地位。生成式人工智慧在车辆系统及其支援的数位基础设施中的整合正在迅速推进。这些因素使美国成为生成式人工智慧解决方案开发和应用的主要枢纽，尤其是在增强即时驾驶智慧、简化车辆设计流程和促进智慧出行解决方案方面。

积极塑造全球汽车生成人工智慧市场的关键参与者包括 NVIDIA、亚马逊网路服务 (AWS)、博世、微软、高通、Aptiv、IBM、大陆集团、英特尔和Google。为了在汽车生成人工智慧市场保持竞争优势，主要参与者正专注于策略联盟、技术创新和平台开发。各公司正在与汽车製造商和一级供应商建立长期合作伙伴关係，以确保跨车辆系统的无缝人工智慧整合。对先进模拟工具、即时资料处理和边缘人工智慧运算的投资是其成长方式的核心。主要公司还透过 SDK 和 API 扩展其软体生态系统，使开发人员能够更快地建立人工智慧应用程式。

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
      • 车辆设计和 ADAS 中的 AI 集成
      • 电动车和连网汽车的普及率不断提高
      • 云端和边缘 AI 部署
      • OEM科技公司合作
      • 多模式人工智慧的进步
    • 产业陷阱与挑战
      • 资料隐私和网路安全
      • 与遗留系统集成
    • 市场机会
      • 软体定义和自动驾驶汽车的扩展
      • 与学术和研究机构的合作
      • 亚太地区和拉丁美洲的新兴市场
      • 与行动服务集成
  • 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
  • 成本分解分析
  • 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
    • 碳足迹考虑
  • 用例和应用
    • 车辆设计与工程应用
    • 製造和生产应用
    • 自动驾驶和ADAS应用
    • 客户体验与服务应用
  • 最佳情况
  • 技术和创新格局
    • 当前的技术趋势
    • 新兴技术
  • 生成式人工智慧技术基础与演进
    • 生成式人工智慧技术架构与能力
    • 人工智慧模型开发和培训基础设施
    • 汽车专用AI模型开发
    • 技术演进与未来路线图
  • 未来技术路线图与创新时间表
    • 生成式人工智慧技术演进（2024-2034）
    • 汽车人工智慧应用发展时间表
    • 技术融合与整合场景
    • 颠覆性技术评估与市场影响
  • 汽车产业数位转型背景
    • 汽车产业技术颠覆格局
    • 数位孪生与仿真技术集成
    • 数据驱动的决策与分析
    • 汽车软体和平台生态系统
  • 监管环境和标准框架
    • 人工智慧治理与监管格局
    • 汽车安全标准和人工智慧集成
    • 国际标准和协调努力
    • 道德人工智慧和负责任的发展框架
  • 投资前景和资金分析
    • 全球人工智慧投资趋势与汽车焦点
    • 汽车业AI投资模式
    • 区域投资格局与政府支持
    • 新创企业生态系统与创新中心
  • 网路安全与风险管理框架
    • 人工智慧安全威胁与漏洞评估
    • 汽车网路安全与人工智慧集成
    • 透过设计和开发实践实现安全性
    • 合规性和监理安全要求

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
    • 多边环境协定
  • 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：依车型，2021 - 2034

  • 搭乘用车
    • 掀背车
    • 轿车
    • SUV
    • 多功能车
    • 电动乘用车
  • 商用车
    • 轻型商用车
    • 重型商用车

第六章：市场估计与预测：以推进方式，2021 - 2034 年

  • 主要趋势
  • 冰
  • 纯电动车
  • 插电式混合动力

第七章：市场估计与预测：依技术分类，2021 - 2034 年

  • 主要趋势
  • 大型语言模型（LLM）和 NLP
  • 电脑视觉和图像生成
  • 多模态人工智慧与跨领域集成
  • 生成式人工智慧平台和工具
  • 其他的

第 8 章：市场估计与预测：按应用，2021 - 2034 年

  • 主要趋势
  • 自动驾驶和ADAS应用
  • 车辆设计与工程
  • 製造和生产优化
  • 客户体验与个人化
  • 供应炼和物流优化
  • 其他的

第九章：市场估计与预测：依最终用途，2021 - 2034

  • 主要趋势
  • OEM
  • 一级汽车供应商
  • 汽车软体和技术公司
  • 出行服务提供者和车队营运商

第 10 章：市场估计与预测：按地区，2021 年至 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
  • 多边环境协定
    • 南非
    • 沙乌地阿拉伯
    • 阿联酋

第 11 章：公司简介

  • Global Technology Leaders
    • Amazon Web Services (AWS)
    • Google
    • IBM
    • Intel
    • Microsoft
    • NVIDIA
    • OpenAI
    • Qualcomm
  • Automotive Technology Specialists
    • Aptiv
    • Bosch
    • Continental
    • DENSO
    • Magna International
    • Mobileye
    • Valeo
    • Waymo
    • ZF Friedrichshafen
  • Emerging AI Specialists and Startups
    • Argo AI
    • Aurora Innovation
    • Cruise
    • DeepRoute.ai
    • Einride
    • Ghost Autonomy
    • Innoviz Technologies
    • Motional
    • Plus
    • Pony.ai
    • Scale AI
    • WeRide
    • Zoox
简介目录
Product Code: 14635

The Global Generative AI in Automotive Market was valued at USD 506.6 million in 2024 and is estimated to grow at a CAGR of 23.8% to reach USD 4.58 billion by 2034.

Generative AI in Automotive Market - IMG1

The industry is witnessing rapid transformation as automotive manufacturers increasingly integrate generative artificial intelligence to streamline autonomous systems, optimize design workflows, and simulate critical driving scenarios. Regulatory encouragement and supportive funding are fueling development across automakers, component suppliers, and mobility tech innovators. As digitalization deepens and vehicles become more intelligent and interconnected, generative AI is becoming central to vehicle development. It enables automakers to replicate rare or complex traffic events, drastically cutting the time and costs associated with safety verification. This advanced capability is creating new standards in simulation accuracy and contributing to faster development timelines. Automakers are now leveraging generative AI to enhance user interfaces, predict maintenance requirements, and fine-tune advanced driving assistance systems. As the automotive industry transitions to software-defined vehicles and connected platforms, AI is no longer an enhancement but a core enabler of next-gen mobility ecosystems. Collaborations between software firms and hardware developers are creating foundational infrastructure that supports seamless integration of AI across automotive environments.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$506.6 Million
Forecast Value$4.58 Billion
CAGR23.8%

In 2024, the passenger vehicle segment generated a 68% share driven by the widespread implementation of intelligent systems across vehicles. AI-enabled technologies are now deeply embedded in functions such as advanced infotainment, driver support features, and in-car safety systems. These tools are significantly elevating the driving experience by improving interaction through conversational interfaces, delivering personalized insights, and powering adaptive responses in real time. Automakers are focusing on AI tools that enhance safety and functionality, with features like proactive service notifications and context-aware driving suggestions. With ongoing enhancements in sensor technology and remote software updates, the application of generative AI in this segment is expected to rise steadily.

The internal combustion engine (ICE) vehicle segment is expected to grow at a CAGR of 14.8% from 2025 to 2034. While electric vehicle platforms are often at the forefront of technological adoption, ICE-powered cars are also integrating AI-driven systems to stay competitive. Automakers are upgrading existing ICE models with intelligent modules that support improved diagnostics, seamless connectivity, and immersive digital experiences. This evolution is being driven by the rising demand for smart functionality in premium ICE vehicles, where retrofitting with AI-based systems is now more accessible through over-the-air updates and scalable platform technologies. Enhanced onboard software allows traditional vehicle categories to benefit from advanced predictive capabilities without requiring major hardware redesigns.

United States Generative AI in Automotive Market generated USD 148.8 million in 2024. The US continues to hold a leadership position due to its strong innovation landscape, vast R&D capabilities, and collaborative efforts spanning academic institutions, technology providers, and government agencies. The integration of generative AI is advancing rapidly across both vehicle systems and the digital infrastructure supporting them. These factors position the US as a primary hub for the development and adoption of generative AI solutions, particularly in enhancing real-time driving intelligence, streamlining vehicle design processes, and facilitating smart mobility solutions.

Key players actively shaping Global Generative AI in Automotive Market include NVIDIA, Amazon Web Services (AWS), Bosch, Microsoft, Qualcomm, Aptiv, IBM, Continental, Intel, and Google. To maintain a competitive edge in the generative AI in automotive market, major players are focusing on strategic alliances, technological innovation, and platform development. Companies are forming long-term partnerships with automakers and tier-one suppliers to ensure seamless AI integration across vehicle systems. Investment in advanced simulation tools, real-time data processing, and edge AI computing is central to their growth approach. Key firms are also expanding their software ecosystems through SDKs and APIs, allowing developers to build AI-powered applications faster.

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 Vehicle
    • 2.2.3 Propulsion
    • 2.2.4 Technology
    • 2.2.5 Application
    • 2.2.6 End Use
  • 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 AI integration in vehicle design and ADAS
      • 3.2.1.2 Increasing adoption of electric and connected vehicles
      • 3.2.1.3 Cloud and edge AI deployment
      • 3.2.1.4 OEM-tech company collaborations
      • 3.2.1.5 Advancements in multimodal AI
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Data privacy and cybersecurity
      • 3.2.2.2 Integration with legacy systems
    • 3.2.3 Market opportunities
      • 3.2.3.1 Expansion of software-defined and autonomous vehicles
      • 3.2.3.2 Collaborations with academic and research institutes
      • 3.2.3.3 Emerging markets in Asia-Pacific and Latin America
      • 3.2.3.4 Integration with mobility services
  • 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 Cost breakdown analysis
  • 3.8 Patent analysis
  • 3.9 Sustainability and environmental aspects
    • 3.9.1 Sustainable practices
    • 3.9.2 Waste reduction strategies
    • 3.9.3 Energy efficiency in production
    • 3.9.4 Eco-friendly Initiatives
    • 3.9.5 Carbon footprint considerations
  • 3.10 Use cases and Applications
    • 3.10.1 Vehicle design and engineering applications
    • 3.10.2 Manufacturing and production applications
    • 3.10.3 Autonomous driving and ADAS applications
    • 3.10.4 Customer experience and service applications
  • 3.11 Best-case scenario
  • 3.12 Technology and Innovation landscape
    • 3.12.1 Current technological trends
    • 3.12.2 Emerging technologies
  • 3.13 Generative AI technology foundation and evolution
    • 3.13.1 Generative AI technology architecture and capabilities
    • 3.13.2 Ai model development and training infrastructure
    • 3.13.3 Automotive-specific AI model development
    • 3.13.4 Technology evolution and future roadmap
  • 3.14 Future technology roadmap and innovation timeline
    • 3.14.1 Generative AI technology evolution (2024-2034)
    • 3.14.2 Automotive AI application development timeline
    • 3.14.3 Technology convergence and integration scenarios
    • 3.14.4 Disruptive technology assessment and market impact
  • 3.15 Automotive industry digital transformation context
    • 3.15.1 Automotive industry technology disruption landscape
    • 3.15.2 Digital twin and simulation technology integration
    • 3.15.3 Data-driven decision making and analytics
    • 3.15.4 Automotive software and platform ecosystem
  • 3.16 Regulatory environment and standards framework
    • 3.16.1 AI governance and regulatory landscape
    • 3.16.2 Automotive safety standards and AI integration
    • 3.16.3 International standards and harmonization efforts
    • 3.16.4 Ethical AI and responsible development framework
  • 3.17 Investment landscape and funding analysis
    • 3.17.1 Global AI investment trends and automotive focus
    • 3.17.2 Automotive industry AI investment patterns
    • 3.17.3 Regional investment landscape and government support
    • 3.17.4 Startup ecosystem and innovation hubs
  • 3.18 Cybersecurity and risk management framework
    • 3.18.1 AI security threats and vulnerability assessment
    • 3.18.2 Automotive cybersecurity and AI integration
    • 3.18.3 Security by design and development practices
    • 3.18.4 Compliance and regulatory security requirements

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 Vehicle, 2021 - 2034 ($Mn)

  • 5.1 Passenger vehicles
    • 5.1.1 Hatchback
    • 5.1.2 Sedan
    • 5.1.3 SUV
    • 5.1.4 MPV
    • 5.1.5 Electric passenger cars
  • 5.2 Commercial vehicles
    • 5.2.1 Light commercial vehicles
    • 5.2.2 Heavy commercial vehicles

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

  • 6.1 Key trends
  • 6.2 ICE
  • 6.3 BEV
  • 6.4 PHEV

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

  • 7.1 Key trends
  • 7.2 Large language models (LLMs) and NLP
  • 7.3 Computer vision and image generation
  • 7.4 Multimodal AI and cross-domain integration
  • 7.5 Generative AI platforms and tools
  • 7.6 Others

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

  • 8.1 Key trends
  • 8.2 Autonomous Driving and ADAS Applications
  • 8.3 Vehicle Design and Engineering
  • 8.4 Manufacturing and production optimization
  • 8.5 Customer experience and personalization
  • 8.6 Supply chain and logistics optimization
  • 8.7 Others

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

  • 9.1 Key trends
  • 9.2 OEM
  • 9.3 Tier 1 automotive suppliers
  • 9.4 Automotive software and technology companies
  • 9.5 Mobility service providers and fleet operators

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($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 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 Global Technology Leaders
    • 11.1.1 Amazon Web Services (AWS)
    • 11.1.2 Google
    • 11.1.3 IBM
    • 11.1.4 Intel
    • 11.1.5 Microsoft
    • 11.1.6 NVIDIA
    • 11.1.7 OpenAI
    • 11.1.8 Qualcomm
  • 11.2 Automotive Technology Specialists
    • 11.2.1 Aptiv
    • 11.2.2 Bosch
    • 11.2.3 Continental
    • 11.2.4 DENSO
    • 11.2.5 Magna International
    • 11.2.6 Mobileye
    • 11.2.7 Valeo
    • 11.2.8 Waymo
    • 11.2.9 ZF Friedrichshafen
  • 11.3 Emerging AI Specialists and Startups
    • 11.3.1 Argo AI
    • 11.3.2 Aurora Innovation
    • 11.3.3 Cruise
    • 11.3.4 DeepRoute.ai
    • 11.3.5 Einride
    • 11.3.6 Ghost Autonomy
    • 11.3.7 Innoviz Technologies
    • 11.3.8 Motional
    • 11.3.9 Plus
    • 11.3.10 Pony.ai
    • 11.3.11 Scale AI
    • 11.3.12 WeRide
    • 11.3.13 Zoox