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市場調查報告書
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1846210

汽車人工智慧：市場佔有率分析、行業趨勢、統計數據和成長預測（2025-2030 年）

Automotive Artificial Intelligence - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 150 Pages | 商品交期: 2-3個工作天內

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簡介目錄

預計到 2025 年，汽車人工智慧市場規模將達到 49.8 億美元，到 2030 年將達到 150.8 億美元，在預測期（2025-2030 年）內，複合年成長率將達到 24.72%。

汽車人工智慧市場-IMG1

軟體定義車輛的快速普及、歐盟和美國強制實施的L2級ADAS法規，以及車用級人工智慧運算成本的下降，正將競爭優勢從機械工程轉向演算法性能。基於晶片組的系統晶片（SoC）也使得即使是中階車型也能實現高TOPS性能。特斯拉首創並被中國主要汽車製造商效仿的車隊學習框架，正以閉合迴路檢驗無法企及的速度提升感知精度。在此背景下，汽車製造商、一級供應商、超大規模資料超大規模資料中心業者和人工智慧新興企業之間的策略夥伴關係正在取代垂直整合，建構模組化創新生態系統，從而促進專業差異化。

全球汽車人工智慧市場趨勢與洞察

二級及以上ADAS安全功能的強制法規

歐盟《通用安全法規II》（General Safety Regulation II）於2024年7月生效，該法規強制要求所有在歐洲銷售的新車必須配備自動緊急煞車、緊急車道維持和智慧速度援助。美國和日本也正在推出類似的法規，這促使全球汽車製造商「一次設計，全球認證」。因此，合規性要求正在將曾經的高級添加物轉變為基本設計要素，從而推動一級供應商對感知系統堆疊的需求量激增。聯合國歐洲經濟委員會關於駕駛輔助系統的第171號法規通過詳細規定人工智慧功能的虛擬測試規則，進一步強化了這一轉變。因此，曾經依靠機械改進來脫穎而出的汽車製造商如今在軟體成熟度方面展開競爭，而隨著明確的規則手冊取代了零散的本地要求，新參與企業的市場准入門檻也隨之降低。

面向汽車SoC的AI計算與TOPS的快速衰退

英偉達的Thor處理器承諾達到2000 TOPS的運算能力，而特斯拉即將推出的AI5晶片則目標為2500 TOPS。成本下降主要得益於共用資料中心容量、先進的晶圓代工製程以及晶片組分區技術，後者以模組化晶片取代了光罩大小的整體式晶片。 imec的汽車晶片組計劃將博世、寶馬和其他行業先驅聯合起來，共同開發一種晶粒互通的晶片間通訊協定，從而縮短開發週期並實現跨車型平台的複用。隨著矽材料的日益稀缺，差異化優勢將轉向軟體，這將迫使傳統半導體供應商整合工具鏈、晶粒和參考堆疊，以支援汽車製造商的大規模部署。

功能安全法規在不同司法管轄區之間分散

ISO 26262、ISO/IEC 5469:2024 以及即將發布的 ISO/TS 5083:2025 分別針對自動駕駛技術堆疊的不同環節定義了安全流程，迫使汽車製造商 (OEM) 協調重疊且不一致的標準。歐洲的 GSR II 標準與美國聯邦指南和中國的 GB/T 標準之間存在差異，導致全球平台必須為每個地區單獨維護合規性證明。規模較小的供應商難以應對多軌檢驗帶來的額外負擔，這往往會導致產品發布延遲或地理範圍縮小。行業聯盟一直倡導“安全案例交換”，即允許認證機構之間共享審核結果，但目前尚未達成共識。在實現統一標準之前，這種拼湊式的模式會增加非重複性工程成本，並阻礙汽車人工智慧市場的成長。

細分市場分析

隨著汽車價值創造從鋼鐵轉向程式碼，到2024年，軟體將貢獻汽車人工智慧市場65.23%的收益。汽車製造商如今提供神經網路升級服務，在車輛售出數年後仍可增加其功能，使每輛連網汽車都成為一個收費的服務節點。硬體部分在預測期間將以14.23%的複合年成長率成長，但隨著晶片生態系統將TOPS（頂級處理器）商品化，其利潤率將會下降。因此，汽車人工智慧市場將青睞那些能夠提供程式碼、工具鏈和生命週期支援的公司，而不是那些僅僅銷售晶片的公司。

諸如 Cerence CaLLM Edge 之類的邊緣駐留語言模型展示了軟體如何在不產生網路費用的情況下提升感知智慧，並符合歐洲和中國的隱私準則。法規要求持續改進煞車和車道維持功能，這進一步鞏固了軟體收益。因此，軟體已穩固確立其在汽車人工智慧市場的主要護城河地位，一級廠商在 DevOps 人才和空中下載 (OTA) 網路安全方面投入數十億美元。

到2024年，機器學習將佔據汽車人工智慧市場41.56%的佔有率，因為其透明的決策樹符合ISO 26262審核的要求。然而，深度學習16.25%的複合年成長率表明，製造商正在轉向多感測器融合，而傳統演算法無法分析此類融合數據。電腦視覺、自然語言處理和情境感知正在推動駕駛座使用者體驗的提升，並將汽車人工智慧市場拓展到安全以外的領域。

特斯拉計劃推出的AI5晶片表明，只有深度卷積模型才能在高速公路上處理4D雷達、雷射雷達和高清攝影機的融合數據。中國供應商正在將Transformer網路整合到泊車輔助模組中，使曾經遙不可及的人工智慧成為展示室中的差異化優勢。因此，供應鏈合作夥伴將競相提供標註資料、可擴展的訓練基礎設施和檢驗工具，以應對複雜多變的神經潛在空間。

區域分析

得益於特斯拉的數據優勢、德克薩斯州寬鬆的測試法規以及英偉達矽谷總部周邊的本土人工智慧運算叢集，北美在2024年佔據了汽車人工智慧市場36.25%的收入佔有率。同時，通用汽車、福特和Waymo正在將其無人駕駛業務從鳳凰城擴展到奧斯汀，這既檢驗了收益，也凸顯了車隊級遠端協助法規方面的不足。

亞太地區將以23.43%的複合年成長率成為全球成長最快的地區。以出口導向電動車領先地位和相對統一的監管環境，奇瑞計劃在30款車型中部署人工智慧技術，華為的目標是到2025年實現50萬輛自動駕駛汽車的交付量。日本的豐田、日產和本田已組成半導體聯盟，以應對該國人工智慧人才短缺的問題。相較之下，韓國現代汽車正在投資7兆韓元，建造連接其工業園區和港口的自動化物流走廊。本土電池和LiDAR供應商將降低區域汽車製造商的零件成本，並推動人工智慧技術在中階車市場的應用。

在嚴格遵守資料隱私規則的同時，歐洲正根據GSR II法規強制要求車輛配備人工智慧安全功能，為每個大眾市場平台建立合主導基準。 BMW計畫於2025年在中國市場整合DeepSeek人工智慧技術，彰顯其本土化策略。大眾汽車將透過OTA方式向數百萬輛歐洲車輛部署Cerence Chat Pro。 GDPR的限制推動了對邊緣推理的需求，促使供應商設計保護隱私的模型更新流程。儘管歐洲市場的絕對成長速度落後於亞洲，但其車輛的高配置使其成為專注於駕駛員監控和網路安全OTA技術堆疊的專業供應商的盈利的市場。

其他福利：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章 引言

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章 市場情勢

  • 市場概覽
  • 市場促進因素
    • 二級及以上ADAS安全功能的監理要求
    • AI計算和汽車SoC的TOPS快速下降
    • OTA軟體更新的普及性使得人工智慧能力的收益成為可能
    • 一種用於提高感知模型準確性的艦隊學習架構
    • 設備端多模態基礎架構模型，可降低對雲端的依賴
    • 基於晶片組的ECU的出現降低了大眾市場車輛的物料清單成本。
  • 市場限制
    • 功能安全法規在不同司法管轄區之間存在差異。
    • 針對極端情況檢驗人工智慧模型的成本很高
    • 一級汽車級人工智慧人才持續短缺
    • 接觸先進節點代工產能供應鏈
  • 價值/供應鏈分析
  • 監管狀況
  • 技術展望
  • 波特五力分析
    • 新進入者的威脅
    • 買方的議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭對手之間的競爭強度

第5章 市場規模與成長預測

  • 按產品
    • 硬體
    • 軟體
  • 依技術
    • 機器學習
    • 深度學習
    • 電腦視覺
    • 自然語言處理
    • 情境感知
  • 透過流程
    • 資料探勘
    • 影像識別
    • 訊號識別
  • 透過使用
    • 自動駕駛
    • ADAS（進階駕駛輔助系統）
    • 人機介面
    • 預測性維護和診斷
  • 按車輛類型
    • 搭乘用車
    • 輕型商用車
    • 大型商用車輛
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 北美其他地區
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 西班牙
      • 義大利
      • 俄羅斯
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 韓國
      • 印度
      • 印尼
      • 菲律賓
      • 越南
      • 澳洲
      • 其他亞太地區
    • 中東和非洲
      • 阿拉伯聯合大公國
      • 沙烏地阿拉伯
      • 土耳其
      • 南非
      • 奈及利亞
      • 埃及
      • 其他中東和非洲地區

第6章 競爭情勢

  • 市場集中度
  • 策略舉措
  • 市佔率分析
  • 公司簡介
    • Tesla Inc.
    • Waymo LLC（Alphabet）
    • NVIDIA Corporation
    • Intel Corporation/Mobileye
    • Horizon Robotics Inc.
    • Aptiv PLC
    • Continental AG
    • Robert Bosch GmbH
    • Qualcomm Incorporated
    • Huawei Technologies Co.
    • Microsoft Corporation
    • Amazon Web Services Inc.
    • Mercedes-Benz Group AG
    • ZF Friedrichshafen AG
    • BMW AG
    • Toyota Motor Corporation
    • Uber Technologies Inc.
    • Hyundai Motor Company
    • Hyundai Mobis Co. Ltd.
    • Magna International Inc.

第7章 市場機會與未來展望

簡介目錄
Product Code: 65185

The Automotive AI market is valued at USD 4.98 billion in 2025 and is forecast to reach USD 15.08 billion by 2030, advancing at a 24.72% CAGR during the forecast period (2025-2030).

Automotive Artificial Intelligence - Market - IMG1

Rapid software-defined vehicle adoption, mandatory Level-2 ADAS regulations in the EU and the United States, and falling costs of automotive-grade AI compute are shifting competitive advantage from mechanical engineering to algorithm performance. Automakers are scaling over-the-air (OTA) update platforms that turn every delivered vehicle into a revenue-generating edge node, while chiplet-based system-on-chips (SoCs) make high TOPS performance affordable for mid-range models. Fleet-learning frameworks pioneered by Tesla and replicated by leading Chinese OEMs raise perception accuracy at a pace no closed-loop validation can match. Against this backdrop, strategic partnerships between carmakers, Tier-1s, hyperscalers, and AI start-ups are replacing vertical integration, creating a modular innovation ecosystem that encourages specialist differentiation.

Global Automotive Artificial Intelligence Market Trends and Insights

Regulatory Mandates For Level-2+ ADAS Safety Features

The EU General Safety Regulation II, which came into force in July 2024, obliges every new car sold in Europe to include automatic emergency braking, emergency lane-keeping, and intelligent speed assistance. Comparable requirements are gaining traction in the United States and Japan, nudging global automakers to design once and certify everywhere. Compliance needs have therefore transformed what used to be premium add-ons into baseline design elements, stimulating larger order volumes for perception stacks from Tier-1 suppliers. The United Nations ECE Regulation 171 on Driver Control Assistance Systems reinforces this shift by detailing virtual-testing rules for AI functions. As a result, OEMs that once differentiated through mechanical refinement now compete on software maturity timelines, and market entry barriers for newcomers fall when a clear rulebook replaces fragmented local requirements.

Rapid Decline In AI Compute And TOPS For Automotive SoCs

NVIDIA's Thor processor promises 2,000 TOPS, and Tesla's forthcoming AI5 chip targets 2,500 TOPS-ten times today's in-car performance while cutting cost per TOPS by roughly 40% every year since 2022. Cost deflation comes from shared data-center volumes, advanced foundry nodes, and chiplet partitioning that substitutes reticle-size monoliths with modular tiles. Imec's Automotive Chiplet Programme unites Bosch, BMW, and other pioneers around interoperable die-to-die protocols that compress development cycles and enable platform reuse across vehicle lines. As silicon ceases to be scarce, differentiation migrates to software, forcing traditional semiconductor suppliers to embed toolchains, middleware, and reference stacks that help automakers deploy at scale.

Fragmented Functional-Safety Regulations Across Jurisdictions

ISO 26262, ISO/IEC 5469:2024, and forthcoming ISO/TS 5083:2025 each define safety processes for different slices of the autonomy stack, leaving OEMs to reconcile overlaps and contradictions. Europe's GSR II departs from emerging US federal guidelines and China's GB/T standards, forcing global platforms to maintain separate compliance evidence for each region. Smaller suppliers struggle with the overhead of multi-track validation, often delaying launches or narrowing geographic scope. Industry consortia advocate a "safety case exchange" where audit artefacts could be ported between homologation authorities, but consensus remains distant. Until unification arrives, the patchwork saps the Automotive AI market growth by raising non-recurring engineering costs.

Other drivers and restraints analyzed in the detailed report include:

  1. Explosion of Over-the-Air SW Updates Enabling AI Feature Monetisation
  2. Fleet-Learning Architectures Accelerating Perception Model Accuracy
  3. High Validation Cost Of AI Models For Edge-Case Scenarios

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software generated 65.23% of the automotive artificial intelligence market revenue in 2024 as vehicle value creation migrated from iron and steel to lines of code. Automakers now ship neural-network upgrades that add features years after purchase, turning every connected car into a living, billed service node. Hardware segment grows at a CAGR of 14.23% during the forecast period, yet its margin compresses when chiplet ecosystems commoditise TOPS. The Automotive AI market, therefore, rewards companies able to bundle code, toolchains, and life-cycle support rather than those selling silicon alone.

Edge-resident language models like Cerence CaLLM Edge illustrate how software can boost perceived intelligence without network fees, meeting privacy guidelines in Europe and China. Regulatory mandates that require continuous improvement of braking or lane-keeping further lock in software revenues, because compliance updates must reach every in-use unit, not just fresh builds. As a result, the Automotive AI market sees Tier-1s investing billions in DevOps talent and OTA cybersecurity, cementing software as the primary moat.

Machine learning owns 41.56% of the automotive artificial intelligence market share in 2024 because its transparent decision trees satisfy ISO 26262 audit needs. Still, deep learning's 16.25% CAGR indicates manufacturers' migration toward multi-sensor fusion that classic algorithms cannot parse. Computer vision, natural language processing, and context awareness tie into cockpit user experience, widening the Automotive AI market beyond safety alone.

Tesla's planned AI5 chip demonstrates that only deep convolutional models can manage 4D radar, LiDAR, and HD-camera fusion at freeway speed. Chinese suppliers follow by embedding transformer networks inside parking-assist modules, making once-exotic AI a showroom differentiator. Consequently, supply-chain partners race to supply annotated data, scalable training infrastructure, and verification tools that handle opaque neural latent spaces.

The Automotive Artificial Intelligence Market is Segmented by Offering (Hardware and Software), Technology (Machine Learning, Deep Learning, and More), Process (Data Mining, Image Recognition, and More), Application (Autonomous Driving, and More), Vehicle Type (Passenger Cars, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America generated 36.25% of the automotive artificial intelligence market in 2024 revenue, anchored by Tesla's data advantage, Texas's permissive testing statutes, and a domestic AI-compute cluster around NVIDIA's Silicon Valley headquarters. In the meantime, General Motors, Ford, and Waymo are scaling driverless operations from Phoenix to Austin, validating monetisation and spotlighting gaps in fleet-wide remote assistance regulation.

Asia-Pacific records a 23.43% CAGR, the fastest worldwide. China combines export-oriented EV leadership with a comparatively unified regulatory sandbox, letting Chery pledge AI rollout across 30 models and Huawei target 500,000 autonomous-capable vehicles by 2025. Japan's Toyota, Nissan, and Honda have formed a semiconductor consortium to address domestic AI shortages. In contrast, South Korea's Hyundai invests KRW 7 trillion in self-driving logistics corridors linking factory zones with ports. Local battery and lidar suppliers reduce the bill of materials for regional OEMs, boosting the Automotive AI market adoption in mid-segment vehicles.

Europe maintains strict data-privacy rules yet mandates AI safety functions under GSR II, creating a compliance-driven baseline for every volume platform. BMW's 2025 integration of DeepSeek AI in China underscores its localisation strategy, while Volkswagen rolls out Cerence Chat Pro OTA to millions of European vehicles. GDPR constraints amplify demand for edge inference, spurring suppliers to design privacy-preserving model-update pipelines. Although the market trails Asia in absolute growth, high per-vehicle content keeps Europe profitable for specialist vendors focusing on driver-monitoring and cyber-secure OTA stacks.

  1. Tesla Inc.
  2. Waymo LLC (Alphabet)
  3. NVIDIA Corporation
  4. Intel Corporation / Mobileye
  5. Horizon Robotics Inc.
  6. Aptiv PLC
  7. Continental AG
  8. Robert Bosch GmbH
  9. Qualcomm Incorporated
  10. Huawei Technologies Co.
  11. Microsoft Corporation
  12. Amazon Web Services Inc.
  13. Mercedes-Benz Group AG
  14. ZF Friedrichshafen AG
  15. BMW AG
  16. Toyota Motor Corporation
  17. Uber Technologies Inc.
  18. Hyundai Motor Company
  19. Hyundai Mobis Co. Ltd.
  20. Magna International Inc.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

  • 1.1 Study Assumptions & Market Definition
  • 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Regulatory Mandates for Level-2+ ADAS Safety Features
    • 4.2.2 Rapid Decline in AI-compute and TOPS for Automotive SoCs
    • 4.2.3 Explosion of Over-the-air (OTA) SW Updates Enabling AI Feature Monetization
    • 4.2.4 Fleet-learning Architectures Accelerating Perception Model Accuracy
    • 4.2.5 On-device Multimodal Foundation Models Reducing Cloud Dependency
    • 4.2.6 Emerging Chiplet-Based ECUs Lowering BOM for Mass-market Vehicles
  • 4.3 Market Restraints
    • 4.3.1 Fragmented Functional-Safety Regulations Across Jurisdictions
    • 4.3.2 High Validation Cost of AI Models for Edge-case Scenarios
    • 4.3.3 Persistent Scarcity of Automotive-grade AI Talent in Tier-1s
    • 4.3.4 Supply-chain Exposure to Advanced-node Foundry Capacity
  • 4.4 Value / Supply-Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Threat of New Entrants
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Bargaining Power of Suppliers
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Intensity of Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

  • 5.1 By Offering
    • 5.1.1 Hardware
    • 5.1.2 Software
  • 5.2 By Technology
    • 5.2.1 Machine Learning
    • 5.2.2 Deep Learning
    • 5.2.3 Computer Vision
    • 5.2.4 Natural Language Processing
    • 5.2.5 Context Awareness
  • 5.3 By Process
    • 5.3.1 Data Mining
    • 5.3.2 Image Recognition
    • 5.3.3 Signal Recognition
  • 5.4 By Application
    • 5.4.1 Autonomous Driving
    • 5.4.2 Advanced Driver-Assistance Systems (ADAS)
    • 5.4.3 Human-Machine Interface
    • 5.4.4 Predictive Maintenance & Diagnostics
  • 5.5 By Vehicle Type
    • 5.5.1 Passenger Cars
    • 5.5.2 Light Commercial Vehicles
    • 5.5.3 Heavy Commercial Vehicles
  • 5.6 By Geography
    • 5.6.1 North America
      • 5.6.1.1 United States
      • 5.6.1.2 Canada
      • 5.6.1.3 Rest of North America
    • 5.6.2 South America
      • 5.6.2.1 Brazil
      • 5.6.2.2 Argentina
      • 5.6.2.3 Rest of South America
    • 5.6.3 Europe
      • 5.6.3.1 Germany
      • 5.6.3.2 United Kingdom
      • 5.6.3.3 France
      • 5.6.3.4 Spain
      • 5.6.3.5 Italy
      • 5.6.3.6 Russia
      • 5.6.3.7 Rest of Europe
    • 5.6.4 Asia-Pacific
      • 5.6.4.1 China
      • 5.6.4.2 Japan
      • 5.6.4.3 South Korea
      • 5.6.4.4 India
      • 5.6.4.5 Indonesia
      • 5.6.4.6 Philippines
      • 5.6.4.7 Vietnam
      • 5.6.4.8 Australia
      • 5.6.4.9 Rest of Asia-Pacific
    • 5.6.5 Middle East and Africa
      • 5.6.5.1 United Arab Emirates
      • 5.6.5.2 Saudi Arabia
      • 5.6.5.3 Turkey
      • 5.6.5.4 South Africa
      • 5.6.5.5 Nigeria
      • 5.6.5.6 Egypt
      • 5.6.5.7 Rest of Middle East and Africa

6 Competitive Landscape

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as Available, Strategic Information, Market Rank/Share for Key Companies, Products and Services, SWOT Analysis, and Recent Developments)
    • 6.4.1 Tesla Inc.
    • 6.4.2 Waymo LLC (Alphabet)
    • 6.4.3 NVIDIA Corporation
    • 6.4.4 Intel Corporation / Mobileye
    • 6.4.5 Horizon Robotics Inc.
    • 6.4.6 Aptiv PLC
    • 6.4.7 Continental AG
    • 6.4.8 Robert Bosch GmbH
    • 6.4.9 Qualcomm Incorporated
    • 6.4.10 Huawei Technologies Co.
    • 6.4.11 Microsoft Corporation
    • 6.4.12 Amazon Web Services Inc.
    • 6.4.13 Mercedes-Benz Group AG
    • 6.4.14 ZF Friedrichshafen AG
    • 6.4.15 BMW AG
    • 6.4.16 Toyota Motor Corporation
    • 6.4.17 Uber Technologies Inc.
    • 6.4.18 Hyundai Motor Company
    • 6.4.19 Hyundai Mobis Co. Ltd.
    • 6.4.20 Magna International Inc.

7 Market Opportunities & Future Outlook