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市场调查报告书
商品编码
1936477

汽车电脑视觉人工智慧市场机会、成长要素、产业趋势分析及2026年至2035年预测

Automotive Computer Vision AI Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 255 Pages | 商品交期: 2-3个工作天内

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简介目录

全球汽车电脑视觉人工智慧市场预计到 2025 年将达到 19 亿美元，到 2035 年将达到 89 亿美元，年复合成长率为 16.7%。

汽车电脑视觉人工智慧市场-IMG1

汽车製造商正在将基于视觉的人工智慧技术融入车辆，使车辆能够解读路况、侦测物体并即时做出反应，从而显着提升安全性和驾驶效率。汽车产业的数位转型持续加速人工智慧在乘用车和商用车领域的应用。大规模生产、半导体创新和演算法改进正在降低高级驾驶辅助技术的整体成本，使电脑视觉解决方案不再实用化高端市场。视觉人工智慧不再是可选项，而是下一代出行技术的核心。整个产业正稳步迈向数据驱动的学习架构，以提高车辆在动态环境中的感知精度。这些发展共同推动了人工智慧技术在全球汽车生态系统中的快速市场渗透、强劲的投资趋势和长期需求。

市场覆盖范围
开始年份 2025
预测年份 2026-2035
起始值 19亿美元
预测金额 89亿美元
复合年增长率 16.7%

高级驾驶辅助系统 (ADAS) 和基于视觉的安全功能正日益成为大众市场车辆和入门车型的标准配备。在过去五年中，ADAS 相关成本降低了 40%，推动了其价格的下降和普及。成本的降低得益于生产效率的提高、人工智慧模型的最佳化以及晶片性能的提升，使汽车製造商能够大规模部署电脑视觉人工智慧。因此，购车者现在期望智慧安全功能和感知能力作为标准配置，而不是额外的付费选配。汽车电脑视觉人工智慧领域正朝着整合式深度学习架构发展，该架构能够处理原始感测器资料并产生驾驶操作，而无需采用分段式的、基于规则的工作流程。

预计到2025年，硬体部分将占据44%的市场份额，并在2026年至2035年间以16.9%的复合年增长率成长。此部分包括摄影机、影像感测器、AI加速晶片、储存单元、电源控制组件和整合式感测器模组。车规级硬体需要具备高耐久性、符合功能安全标准以及长使用寿命，这增加了研发和製造成本。这些因素进一步凸显了硬体在实现车辆可靠的电脑视觉性能方面的核心作用。

预计到2025年，OEM厂商安装的解决方案将占据86%的市场份额，并在2035年之前以17%的复合年增长率成长。汽车製造商之所以青睐工厂出货时装载的系统，是因为这些系统符合监管要求、能够与车辆无缝整合、享有保固服务，并且具有规模化的成本效益。电脑视觉和人工智慧技术正在製造过程中被整合到多个车型类别中，从而推动了曾经仅在定价模式上才有的功能的快速标准化。

中国汽车电脑视觉人工智慧市场预计2025年将占据全球38%的市场份额，到2035年市场规模将达到14亿美元，年复合成长率达17.2%。中国受益于对智慧汽车的强大政策支持、电动车的广泛普及以及成本效益高的国内供应链。本土製造商正积极竞相将基于视觉的系统作为标准配置，巩固了中国在大规模应用领域的主导地位。

目录

第一章调查方法

第二章执行摘要

第三章业界考察

  • 生态系分析
    • 供应商情况
    • 利润率分析
    • 成本结构
    • 每个阶段的附加价值
    • 影响价值链的因素
    • 中断
  • 产业影响因素
      • 司机
      • 车辆中高阶驾驶辅助系统（ADAS）的普及应用日益广泛
      • 自动驾驶和半自动驾驶汽车的需求不断增长
      • 严格的安全和排放气体法规推动了基于人工智慧的视觉系统的应用。
      • 人工智慧、机器学习和感测器融合领域的技术进步
      • 汽车製造商（OEM）和一级供应商正在加大对智慧汽车技术的投资。
    • 产业潜在风险与挑战
      • 高昂的开发和整合成本
      • 感测器融合和即时数据处理的复杂性
    • 市场机会
      • 自动驾驶和半自动驾驶汽车的发展
      • 先进的人工智慧演算法提高了识别能力
      • 与联网汽车技术的集成
      • 对车载监控和安全功能的需求日益增长
      • 汽车製造商与技术提供者之间的合作
      • 新兴汽车市场的扩张
  • 成长潜力分析
  • 监管环境
    • 北美洲
      • 美国 - FMVSS 和 NHTSA 指南
      • 加拿大 - 机动车辆安全法规 (MVSR)
    • 欧洲
      • 德国-欧盟通用安全法规（GSR）
      • 英国- 道路车辆（许可）条例
      • 法国-欧盟自动驾驶车辆与道路安全框架
      • 义大利 - 国家道路安全计画 (PNSS)
    • 亚太地区
      • 中国 - GB/T 标准和 GB 标准
      • 印度－机动车辆（修正）法案与AIS标准
      • 日本－道路交通法及国土交通省自动驾驶指南
      • 澳洲 - 澳洲外观设计规则 (ADR)
    • LATAM
      • 墨西哥-NOM车辆安全标准
      • 阿根廷 - 国家交通法 24.449
    • 中东和非洲
      • 南非共和国 - 国家道路交通法（1996 年）
      • 沙乌地阿拉伯—交通法规与2030愿景交通倡议
  • 波特五力分析
  • PESTEL 分析
  • 科技与创新趋势
    • 当前技术趋势
    • 新兴技术
  • 专利分析
  • 用例和成功案例
  • 永续性和环境方面
    • 永续实践
    • 减少废弃物策略
    • 生产中的能源效率
    • 环保倡议
    • 碳足迹考量
  • 未来前景与机会

第四章 竞争情势

  • 介绍
  • 公司市占率分析
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • 中东和非洲
  • 主要市场公司的竞争分析
  • 竞争定位矩阵
  • 战略展望矩阵
  • 重大进展
    • 併购
    • 伙伴关係与合作
    • 新产品发布
    • 企业扩张计画和资金筹措

第五章 按组件分類的市场估算与预测，2022-2035年

  • 硬体
    • 摄影机（单声道、立体声、环绕声、红外线）
    • 感测器（光达、雷达、超音波）
    • 处理器和边缘人工智慧晶片
  • 软体
    • 人工智慧（AI）和机器学习演算法
    • 电脑视觉平台
    • 影像处理与目标侦测软体
  • 服务
    • 系统整合
    • 咨询和定制
    • 安装和设定
    • 维护和支援

第六章 依车辆类型分類的市场估计与预测，2022-2035年

  • 搭乘用车
    • 掀背车
    • SUV
    • 轿车
  • 商用车辆
    • 轻型商用车（LCV）
    • 中型商用车（MCV）
    • 重型商用车（HCV）
  • 电动车（EV）
  • 自动驾驶汽车

第七章 按技术分類的市场估计与预测，2022-2035年

  • 基于机器视觉的系统
  • 基于深度学习的系统
  • 基于感测器融合技术的系统

第八章 依实施类型分類的市场估算与预测，2022-2035年

  • OEM
  • 售后市场

第九章 按应用领域分類的市场估算与预测，2022-2035年

  • ADAS（进阶驾驶辅助系统）
    • 前向碰撞警报（FCW）
    • 自动紧急煞车（AEB）
    • 车道偏离预警（LDW）
    • 车道维持辅助系统（LKA）
    • 主动式车距维持定速系统（ACC）
    • 交通标誌识别（TSR）
    • 盲点侦测（BSD）
    • 停车辅助和环景显示监控
  • 自动驾驶
    • 物体和行人侦测
    • 道路边缘和车道边界侦测
    • 自由空间探测
    • 环境测绘
    • 路线规划协助
  • 车上监控系统
    • 驾驶员监控系统（DMS）
    • 人员监控系统（OMS）
    • 手势姿态辨识
    • 安全带和儿童安全座椅使用检测系统
  • 其他的

第十章 2022-2035年各地区市场估计与预测

  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 俄罗斯
    • 荷兰
    • 瑞典
    • 丹麦
    • 波兰
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 新加坡
    • 泰国
    • 印尼
    • 越南
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
    • 哥伦比亚
  • 中东和非洲
    • 南非
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 以色列

第十一章：公司简介

  • 世界玩家
    • Aptiv PLC
    • Continental
    • Denso
    • Intel
    • Magna International
    • Mobileye
    • NVIDIA
    • Qualcomm Technologies
    • Robert Bosch
    • Valeo
  • 区域玩家
    • Aisin Seiki
    • Hitachi Astemo
    • Hyundai Mobis
    • Panasonic Automotive
    • Renesas Electronics
    • Samsung Electronics
    • ZF Friedrichshafen
  • 新兴科技创新者
    • Ambarella
    • Arbe Robotics
    • DeepRoute.ai
    • Ficosa International
    • Horizon Robotics
    • Innoviz Technologies
    • StradVision
    • Veoneer
简介目录
Product Code: 15480

The Global Automotive Computer Vision AI Market was valued at USD 1.9 billion in 2025 and is estimated to grow at a CAGR of 16.7% to reach USD 8.9 billion by 2035.

Automotive Computer Vision AI Market - IMG1

Automotive manufacturers are embedding vision-based AI to enable vehicles to interpret road conditions, detect objects, and react in real time, significantly improving safety and driving efficiency. The ongoing digital transformation of the automotive sector continues to accelerate adoption across passenger and commercial vehicles. Cost reductions across advanced driver assistance technologies, driven by scale manufacturing, semiconductor innovation, and improved algorithms, are making computer vision solutions viable beyond premium segments. Vision AI is now positioned as a core enabler of next-generation mobility rather than an optional enhancement. The industry is steadily shifting toward data-driven learning architectures that improve perception accuracy in dynamic environments. These developments collectively support rapid market penetration, strong investment momentum, and long-term demand across global automotive ecosystems.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$1.9 Billion
Forecast Value$8.9 Billion
CAGR16.7%

Advanced driver assistance and vision-based safety features are increasingly offered across mass-market and entry-level vehicles. A 40% reduction in ADAS-related costs over the past five years has improved affordability and adoption. This decline reflects production efficiencies, optimized AI models, and improved chip performance, enabling automakers to deploy computer vision AI at scale. As a result, vehicle buyers now expect intelligent safety and perception capabilities as standard offerings rather than premium add-ons. The automotive computer vision AI landscape is evolving toward unified deep learning architectures that process raw sensor data and generate driving actions without segmented rule-based workflows.

The hardware segment held 44% share in 2025, growing at a CAGR of 16.9% from 2026 to 2035. This segment includes cameras, image sensors, AI acceleration chips, memory units, power control components, and integrated sensor modules. Automotive-grade hardware requires high durability, functional safety compliance, and long operational life, which increases development and production costs. These factors reinforce the central role of hardware in enabling reliable computer vision performance in vehicles.

The OEM-installed solutions segment held an 86% share in 2025 and is projected to grow at a CAGR of 17% through 2035. Automakers prefer factory-installed systems due to regulatory alignment, seamless vehicle integration, warranty coverage, and cost efficiencies achieved through large-scale deployment. Computer vision AI is being embedded during manufacturing across multiple vehicle categories, supporting rapid standardization of features that were once limited to higher-priced models.

China Automotive Computer Vision AI Market held 38% share in 2025 and is forecast to reach USD 1.4 billion by 2035, growing at a CAGR of 17.2%. The country benefits from strong policy support for intelligent vehicles, widespread adoption of electric mobility, and cost-efficient domestic supply chains. Local manufacturers actively compete by integrating vision-based systems as standard features, reinforcing China's leadership in large-scale deployment.

Key companies operating in the Global Automotive Computer Vision AI Market include NVIDIA, Robert Bosch, Mobileye, Continental, Qualcomm Technologies, Magna, Denso, Intel, Valeo, and Aptiv. Companies in the automotive computer vision AI market focus on vertical integration, long-term OEM partnerships, and continuous investment in AI model optimization to strengthen their market position. Many players prioritize scalable hardware-software platforms that can be deployed across multiple vehicle models and regions. Strategic collaborations with semiconductor manufacturers help ensure access to high-performance, automotive-grade chips. Firms also invest heavily in data acquisition and simulation to improve model accuracy and reliability. Expanding manufacturing footprints and localizing supply chains allow companies to reduce costs and meet regional regulatory requirements.

Table of Contents

Chapter 1 Methodology

  • 1.1 Research approach
  • 1.2 Quality commitments
  • 1.3 Research trail and 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
  • 1.6 Best estimates and calculations
    • 1.6.1 Base year calculation for any one approach
  • 1.7 Forecast model
  • 1.8 Research transparency addendum

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2022 - 2035
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Components
    • 2.2.3 Vehicles
    • 2.2.4 Technology
    • 2.2.5 Deployment Mode
    • 2.2.6 Application
  • 2.3 TAM Analysis, 2026-2035
  • 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
      • 3.2.1.1 Growth drivers
      • 3.2.1.2 Increasing adoption of advanced driver assistance systems (ADAS) in vehicles
      • 3.2.1.3 Rising demand for autonomous and semi-autonomous vehicles
      • 3.2.1.4 Stringent safety and emission regulations encouraging AI-based vision systems
      • 3.2.1.5 Technological advancements in AI, machine learning, and sensor fusion
      • 3.2.1.6 Growing investment by OEMs and Tier-1 suppliers in smart vehicle technologies
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 High development and integration costs
      • 3.2.2.2 Complexity in sensor fusion and real-time data processing
    • 3.2.3 Market opportunities
      • 3.2.3.1 Growth of autonomous and semi-autonomous vehicles
      • 3.2.3.2 Advanced AI algorithms for better perception
      • 3.2.3.3 Integration with connected vehicle technologies
      • 3.2.3.4 Rising demand for in-cabin monitoring and safety features
      • 3.2.3.5 Collaborations between OEMs and tech providers
      • 3.2.3.6 Expansion in emerging automotive markets
  • 3.3 Growth potential analysis
  • 3.4 Regulatory landscape
    • 3.4.1 North America
      • 3.4.1.1 US- FMVSS and NHTSA guidelines
      • 3.4.1.2 Canada - Motor vehicle safety regulations (MVSR)
    • 3.4.2 Europe
      • 3.4.2.1 Germany- EU General Safety Regulation (GSR)
      • 3.4.2.2 UK- Road Vehicles (Approval) Regulations
      • 3.4.2.3 France- EU AV and road safety frameworks
      • 3.4.2.4 Italy- National Road Safety Plan (PNSS)
    • 3.4.3 Asia Pacific
      • 3.4.3.1 China- GB/T and GB standards
      • 3.4.3.2 India- Motor Vehicles (Amendment) Act and AIS standards
      • 3.4.3.3 Japan- Road Traffic Act and MLIT autonomous driving guidelines
      • 3.4.3.4 Australia- Australian Design Rules (ADR)
    • 3.4.4 LATAM
      • 3.4.4.1 Mexico- NOM vehicle safety standards
      • 3.4.4.2 Argentina- National traffic law 24.449
    • 3.4.5 MEA
      • 3.4.5.1 South Africa- National road traffic act (1996)
      • 3.4.5.2 Saudi Arabia- Traffic law & vision 2030 transport initiatives
  • 3.5 Porter's analysis
  • 3.6 PESTEL analysis
  • 3.7 Technology and innovation landscape
    • 3.7.1 Current technological trends
    • 3.7.2 Emerging technologies
  • 3.8 Patent analysis
  • 3.9 Use cases & success stories
  • 3.10 Sustainability and environmental aspects
    • 3.10.1 Sustainable practices
    • 3.10.2 Waste reduction strategies
    • 3.10.3 Energy efficiency in production
    • 3.10.4 Eco-friendly Initiatives
    • 3.10.5 Carbon footprint considerations
  • 3.11 Future outlook and opportunities

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 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 Component, 2022 - 2035 ($Bn)

  • 5.1 Key trends
  • 5.2 Hardware
    • 5.2.1 Cameras (mono, stereo, surround, infrared)
    • 5.2.2 Sensors (LiDAR, radar, ultrasonic)
    • 5.2.3 Processors & Edge AI chips
  • 5.3 Software
    • 5.3.1 AI & machine learning algorithms
    • 5.3.2 Computer vision platforms
    • 5.3.3 Image processing & object detection software
  • 5.4 Services
    • 5.4.1 System integration
    • 5.4.2 Consulting & customization
    • 5.4.3 Deployment & installation
    • 5.4.4 Maintenance & support

Chapter 6 Market Estimates & Forecast, By Vehicle, 2022 - 2035 ($Bn)

  • 6.1 Key trends
  • 6.2 Passenger cars
    • 6.2.1 Hatchback
    • 6.2.2 SUV
    • 6.2.3 Sedan
  • 6.3 Commercial vehicles
    • 6.3.1 Light commercial vehicles (LCV)
    • 6.3.2 Medium commercial vehicles (MCV)
    • 6.3.3 Heavy commercial vehicles (HCV)
  • 6.4 Electric vehicles (EVs)
  • 6.5 Autonomous vehicles

Chapter 7 Market Estimates & Forecast, By Technology, 2022 - 2035 ($Bn)

  • 7.1 Key trends
  • 7.2 Machine vision-based system
  • 7.3 Deep learning-based system
  • 7.4 Sensor fusion-based system

Chapter 8 Market Estimates & Forecast, By Deployment Mode, 2022 - 2035 ($Bn)

  • 8.1 Key trends
  • 8.2 OEM
  • 8.3 Aftermarket

Chapter 9 Market Estimates & Forecast, By Application, 2022 - 2035 ($Bn)

  • 9.1 Key trends
  • 9.2 Advanced driver assistance systems (ADAS)
    • 9.2.1 Forward collision warning (FCW)
    • 9.2.2 Automatic emergency braking (AEB)
    • 9.2.3 Lane departure warning (LDW)
    • 9.2.4 Lane keeping assist (LKA)
    • 9.2.5 Adaptive cruise control (ACC)
    • 9.2.6 Traffic sign recognition (TSR)
    • 9.2.7 Blind spot detection (BSD)
    • 9.2.8 Parking assist and surround view monitoring
  • 9.3 Autonomous driving
    • 9.3.1 Object and pedestrian detection
    • 9.3.2 Road edge and lane boundary detection
    • 9.3.3 Free space detection
    • 9.3.4 Environmental mapping
    • 9.3.5 Path planning support
  • 9.4 In-cabin monitoring
    • 9.4.1 Driver monitoring system (DMS)
    • 9.4.2 Occupant monitoring system (OMS)
    • 9.4.3 Gesture recognition
    • 9.4.4 Seatbelt and child presence detection
  • 9.5 Others

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

  • 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 Russia
    • 10.3.7 Netherlands
    • 10.3.8 Sweden
    • 10.3.9 Denmark
    • 10.3.10 Poland
  • 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 Singapore
    • 10.4.7 Thailand
    • 10.4.8 Indonesia
    • 10.4.9 Vietnam
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
    • 10.5.4 Colombia
  • 10.6 MEA
    • 10.6.1 South Africa
    • 10.6.2 Saudi Arabia
    • 10.6.3 UAE
    • 10.6.4 Israel

Chapter 11 Company Profiles

  • 11.1 Global Players
    • 11.1.1 Aptiv PLC
    • 11.1.2 Continental
    • 11.1.3 Denso
    • 11.1.4 Intel
    • 11.1.5 Magna International
    • 11.1.6 Mobileye
    • 11.1.7 NVIDIA
    • 11.1.8 Qualcomm Technologies
    • 11.1.9 Robert Bosch
    • 11.1.10 Valeo
  • 11.2 Regional Players
    • 11.2.1 Aisin Seiki
    • 11.2.2 Hitachi Astemo
    • 11.2.3 Hyundai Mobis
    • 11.2.4 Panasonic Automotive
    • 11.2.5 Renesas Electronics
    • 11.2.6 Samsung Electronics
    • 11.2.7 ZF Friedrichshafen
  • 11.3 Emerging Technology Innovators
    • 11.3.1 Ambarella
    • 11.3.2 Arbe Robotics
    • 11.3.3 DeepRoute.ai
    • 11.3.4 Ficosa International
    • 11.3.5 Horizon Robotics
    • 11.3.6 Innoviz Technologies
    • 11.3.7 StradVision
    • 11.3.8 Veoneer