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市場調查報告書
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2081179

汽車人工智慧 (AI) 市場預測至 2034 年——按組件、車輛類型、動力系統、部署模式、應用、最終用戶和地區分類的全球分析

Automotive Artificial Intelligence (AI) Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Vehicle Type, Propulsion Type, Deployment Mode, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球汽車人工智慧 (AI) 市場規模將達到 150 億美元，並在預測期內以 17.2% 的複合年成長率成長，到 2034 年將達到 534 億美元。

汽車人工智慧是一種利用機器學習、電腦視覺和自然語言處理技術的運算系統，它使車輛能夠感知周圍環境、解讀複雜情況、做出決策並從經驗中學習。這些系統處理來自攝影機、雷達、LiDAR和超音波設備的大量感測器數據，建立全面的環境模型，從而支援導航、防碰撞和乘員互動。

自動駕駛技術的發展

隨著汽車製造商競相開發自動駕駛功能，汽車人工智慧正獲得前所未有的投資。這些功能有望在道路安全和運輸效率方面帶來突破性的提升。經過各種駕駛場景訓練的機器學習演算法將使車輛能夠應對基於規則的程式設計方法難以處理的情況，例如複雜的城市環境、建築工地和惡劣天氣條件。為實現更高水準的自動化而產生的競爭壓力，催生了對日益複雜的人工智慧模型、大規模的訓練資料集和更高效能推理硬體的需求。消費者對高階駕駛輔助功能的興趣日益濃厚，這些功能可以減輕通勤和長途旅行中的駕駛負擔，這也推動了市場成長。

檢驗的複雜性

汽車人工智慧市場面臨著許多挑戰，其中之一就是機器學習系統的檢驗和核准，這些系統缺乏確定性行為和透明的決策流程。傳統的汽車開發依賴於針對規範的全面測試，但神經網路的運作如同“黑箱”，因此無法完全預測或解釋其對新輸入的反應。監管和問責框架尚未建立明確的人工智慧系統核准標準，以平衡促進創新和確保安全的需求。極端情況和特殊情況對事故的發生率影響尤其顯著，而這些情況本身就需要稀少且難以收集的訓練資料。

車內個性化

將人工智慧整合到汽車系統中，為提​​供能夠適應駕駛員個人偏好、生理狀態和情境需求的個人化體驗創造了巨大機會。自然語言處理技術能夠實現對話式介面，從而在不分散駕駛員視覺和手動操作注意力的情況下，控制車輛功能、檢索資訊和管理通訊。電腦視覺系統可以監測駕駛員的注意力、偵測疲勞程度並識別需要介入的醫療緊急情況。隨著車輛自動駕駛能力的提升，人工智慧驅動的車載感知技術將能夠根據透過持續互動學習到的乘員特徵，最佳化座椅位置、空調控制和娛樂內容。

演算法偏差帶來的風險

汽車人工智慧市場面臨新的威脅：演算法偏差會損害系統在不同人群和駕駛條件下的性能。使用低估特定族群、地理區域或天氣模式的訓練資料集，會導致模型效能參差不齊，進而可能造成安全差異和歧視性後果。公眾對人工智慧限制的認知正在不斷提高，媒體報告的自動駕駛汽車事故等重大事件正在影響消費者信心和監管機構的立場。人工智慧研發集中在少數幾家科技公司手中，引發了人們對競爭平衡和供應鏈韌性的擔憂。

新型冠狀病毒（COVID-19）的影響：

新冠疫情初期，由於實驗室關閉和需要現場資料收集活動受限，汽車產業的AI發展一度受阻。然而，這場危機加速了人們對最大限度減少人際接觸的自動駕駛配送和運輸解決方案的興趣，促使投資重新分配到物流和出行服務領域的AI應用。疫情期間實施的遠距辦公模式改進了分散式AI開發團隊的工具，促進了模型的持續訓練和基於模擬的檢驗。疫情後半導體短缺凸顯了高效能AI演算法的重要性，這些演算法即使在效能較低的硬體上也能提供可接受的效能。

在預測期內，軟體產業預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率，因為它在演算法、中介軟體和應用層的實現中發揮核心作用，而這些正是定義車輛人工智慧功能的基礎。機器學習架構、電腦視覺管線和感測器融合演算法等軟體元件是區分不同人工智慧平台的關鍵價值創造機制。隨著硬體商品化削弱晶片級差異化，軟體最佳化和生態系統整合正成為日益重要的競爭因素。

預計在預測期內，電池式電動車（BEV）細分市場將呈現最高的複合年成長率。

在預測期內，電池式電動車（BEV）細分市場預計將呈現最高的成長率，這主要得益於電氣化和智慧化的融合，二者是下一代汽車平臺相輔相成的發展趨勢。純電動車的電氣架構非常適合人工智慧運算，其配備的大容量電池能夠為電力消耗的推理處理器提供充足電量，而不會顯著影響續航里程。領先的電動車製造商正將人工智慧能力定位為核心品牌屬性。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於該地區集中了眾多領先的人工智慧技術公司，以及對自動駕駛技術研發的大量創業投資投資。美國在機器學習研究領域保持主導地位，其許多知名科技公司和研究機構正在取得一系列基礎性進展，這些進展將應用於汽車領域。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於該地區大規模的汽車生產、政府對智慧汽車發展的支持以及消費者對先進技術的快速接受。中國已將人工智慧列為戰略重點，並投入大量國家資金和政策支持，以增強國內在整個技術鏈上的能力。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需進行可行性評估）。
  • 競爭性標竿分析
    • 根據產品系列、企業發展和策略聯盟對重點公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章 全球汽車人工智慧（AI）市場：按組件分類

  • 硬體
    • 人工智慧處理器
    • 感應器
    • 相機
    • 雷達
    • LiDAR
    • 邊緣運算設備
  • 軟體
    • MAC平台
    • 電腦視覺軟體
    • 自然語言處理（NLP）軟體
    • 預測分析軟體
    • 自動駕駛軟體
  • 服務

第6章 全球汽車人工智慧（AI）市場：依車輛類型分類

  • 搭乘用車
  • 商用車輛
    • 輕型商用車（LCV）
    • 中型商用車（MCV）
    • 重型商用車（HCV）

第7章 全球汽車人工智慧（AI）市場：按動力類型分類

  • 內燃機車
  • 電池式電動車（BEV）
  • 插電式混合動力車（PHEV）
  • 混合動力電動車（HEV）
  • 燃料電池電動車（FCEV）

第8章 全球汽車人工智慧（AI）市場：依部署模式分類

  • 本地部署/車載人工智慧
  • 基於雲端的人工智慧
  • 邊緣人工智慧

第9章 全球汽車人工智慧（AI）市場：按應用領域分類

  • 自動駕駛
  • 高級駕駛輔助系統（ADAS）
  • 人機介面（HMI）
  • 預測性保護
  • 智慧交通管理
  • 車隊管理
  • 保險遠距資訊處理與風險評估
  • 製造和生產最佳化
  • 網路安全和詐欺偵測

第10章：全球汽車人工智慧（AI）市場：按最終用戶分類

  • 汽車原廠設備製造商
  • 一級供應商
  • 車隊營運商
  • 交通行動服務(MaaS) 供應商
  • 汽車經銷商和服務供應商

第11章 全球汽車人工智慧（AI）市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • NVIDIA Corporation
  • Mobileye Global Inc.
  • Qualcomm Incorporated
  • Robert Bosch GmbH
  • Continental AG
  • DENSO Corporation
  • Aptiv PLC
  • ZF Friedrichshafen AG
  • Valeo SA
  • Magna International Inc.
  • NXP Semiconductors NV
  • Renesas Electronics Corporation
  • Tesla, Inc.
  • Waymo LLC
  • Hyundai Mobis Co., Ltd.
Product Code: SMRC37656

According to Stratistics MRC, the Global Automotive Artificial Intelligence (AI) Market is accounted for $15.0 billion in 2026 and is expected to reach $53.4 billion by 2034 growing at a CAGR of 17.2% during the forecast period. Automotive artificial intelligence refers to computational systems that enable vehicles to perceive their environment, interpret complex scenarios, make decisions, and learn from experience through machine learning, computer vision, and natural language processing technologies. These systems process vast quantities of sensor data from cameras, radar, lidar, and ultrasonic devices to construct comprehensive environmental models that support navigation, collision avoidance, and occupant interaction.

Market Dynamics:

Driver:

Autonomous Driving Development

Automotive artificial intelligence is experiencing unprecedented investment as manufacturers race to develop autonomous driving capabilities that promise transformative improvements in road safety and transportation efficiency. Machine learning algorithms trained on diverse driving scenarios enable vehicles to handle complex urban environments, construction zones, and adverse weather conditions that challenge rule-based programming approaches. The competitive pressure to achieve higher levels of automation has created demand for increasingly sophisticated AI models, larger training datasets, and more powerful inference hardware. Consumer interest in advanced driver assistance features that reduce driving burden during commutes and long trips sustains market growth.

Restraint:

Validation Complexity

The automotive artificial intelligence market faces substantial challenges related to the verification and validation of machine learning systems that lack deterministic behavior and transparent decision-making processes. Traditional automotive development relies on exhaustive testing against specifications, yet neural networks operate as black boxes whose responses to novel inputs cannot be fully predicted or explained. Regulatory bodies and liability frameworks have not yet established clear standards for AI system approval that balance innovation incentives against safety assurance requirements. The edge cases and corner cases that contribute disproportionately to accidents require training data that is inherently rare and difficult to collect.

Opportunity:

In-Vehicle Personalization

The integration of artificial intelligence into vehicle systems creates significant opportunities for personalized experiences that adapt to individual driver preferences, physiological states, and contextual needs. Natural language processing enables conversational interfaces that control vehicle functions, retrieve information, and manage communications without distracting visual-manual interaction. Computer vision systems can monitor driver attention, detect fatigue, and identify medical emergencies that require intervention. As vehicles become more autonomous, AI-powered interior sensing can optimize seating positions, climate control, and entertainment content based on occupant profiles learned through ongoing interaction.

Threat:

Algorithmic Bias Risks

The automotive artificial intelligence market confronts emerging threats from algorithmic biases that may compromise system performance across diverse populations and operating conditions. Training datasets that underrepresent certain demographics, geographic regions, or weather patterns can produce models that perform inconsistently, potentially creating safety disparities or discriminatory outcomes. Public awareness of AI limitations is growing, with high-profile incidents involving autonomous vehicle crashes generating media coverage that influences consumer trust and regulatory attitudes. The concentration of AI development among a small number of technology companies raises concerns about competitive fairness and supply chain resilience.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted automotive artificial intelligence development through laboratory closures and restrictions on data collection activities that require physical presence. However, the crisis accelerated interest in autonomous delivery and transportation solutions that minimize human contact, redirecting investment toward AI applications for logistics and mobility services. Remote work practices adopted during the pandemic improved tools for distributed AI development teams, enabling continued progress in model training and simulation-based validation. Post-pandemic, the semiconductor shortage highlighted the importance of efficient AI algorithms that can deliver acceptable performance on less powerful hardware.

The Software segment is expected to be the largest during the forecast period

The Software segment is expected to account for the largest market share during the forecast period, due to its central role in implementing the algorithms, middleware, and application layers that define artificial intelligence functionality in vehicles. Software components including machine learning frameworks, computer vision pipelines, and sensor fusion algorithms represent the primary value creation mechanism that differentiates competing AI platforms. As hardware commoditization reduces differentiation at the chip level, software optimization and ecosystem integration become increasingly important competitive factors.

The Battery Electric Vehicles (BEVs) segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the Battery Electric Vehicles (BEVs) segment is predicted to witness the highest growth rate, driven by the convergence of electrification and intelligence as complementary trends that reinforce each other in next-generation vehicle platforms. BEVs provide favorable electrical architectures for AI computing with high-capacity batteries that can sustain power-hungry inference processors without compromising driving range significantly. Leading electric vehicle manufacturers are positioning AI capabilities as core brand attributes.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the concentration of leading AI technology companies and substantial venture capital investment in autonomous driving development. The United States maintains leadership in machine learning research, with prominent technology companies and research institutions producing foundational advances that translate into automotive applications.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to massive automotive production, government support for intelligent vehicle development, and rapid consumer adoption of advanced technologies. China has designated artificial intelligence as a strategic priority with substantial national funding and policy support for domestic capabilities across the entire technology stack.

Key players in the market

Some of the key players in Automotive Artificial Intelligence (AI) include NVIDIA Corporation, Mobileye Global Inc., Qualcomm Incorporated, Robert Bosch GmbH, Continental AG, DENSO Corporation, Aptiv PLC, ZF Friedrichshafen AG, Valeo SA, Magna International Inc., NXP Semiconductors N.V., Renesas Electronics Corporation, Tesla, Inc., Waymo LLC and Hyundai Mobis Co., Ltd.

Key Developments:

In June 2026, NVIDIA Corporation launched an updated Drive Thor platform combining autonomous driving and in-cabin AI processing on a unified architecture for production vehicles in 2027.

In May 2026, Mobileye Global Inc. expanded its SuperVision hands-free driving system to additional OEM partners, integrating crowd-sourced mapping data for enhanced navigation accuracy.

In February 2026, Tesla, Inc. unveiled an updated full self-driving neural network trained on expanded fleet data, improving performance in challenging urban intersection scenarios.

Components Covered:

  • Hardware
  • Software
  • Services

Vehicle Types Covered:

  • Passenger Cars
  • Commercial Vehicles
  • Light Commercial Vehicles (LCVs)
  • Medium Commercial Vehicles (MCVs)
  • Heavy Commercial Vehicles (HCVs)

Propulsion Types Covered:

  • Internal Combustion Engine (ICE) Vehicles
  • Battery Electric Vehicles (BEVs)
  • Plug-in Hybrid Electric Vehicles (PHEVs)
  • Hybrid Electric Vehicles (HEVs)
  • Fuel Cell Electric Vehicles (FCEVs)

Deployment Modes Covered:

  • On-Premise / On-Board AI
  • Cloud-Based AI
  • Edge AI

Applications Covered:

  • Autonomous Driving
  • Advanced Driver Assistance Systems (ADAS)
  • Human-Machine Interface (HMI)
  • Predictive Maintenance
  • Intelligent Traffic Management
  • Fleet Management
  • Insurance Telematics & Risk Assessment
  • Manufacturing & Production Optimization
  • Cybersecurity & Fraud Detection

End Users Covered:

  • Automotive OEMs
  • Tier-1 Suppliers
  • Fleet Operators
  • Mobility-as-a-Service (MaaS) Providers
  • Automotive Dealers & Service Providers

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Automotive Artificial Intelligence (AI) Market, By Component

  • 5.1 Hardware
    • 5.1.1 AI Processors
    • 5.1.2 Sensors
    • 5.1.3 Cameras
    • 5.1.4 Radar
    • 5.1.5 LiDAR
    • 5.1.6 Edge Computing Devices
  • 5.2 Software
    • 5.2.1 Machine Learning Platforms
    • 5.2.2 Computer Vision Software
    • 5.2.3 Natural Language Processing (NLP) Software
    • 5.2.4 Predictive Analytics Software
    • 5.2.5 Autonomous Driving Software
  • 5.3 Services

6 Global Automotive Artificial Intelligence (AI) Market, By Vehicle Type

  • 6.1 Passenger Cars
  • 6.2 Commercial Vehicles
    • 6.2.1 Light Commercial Vehicles (LCVs)
    • 6.2.2 Medium Commercial Vehicles (MCVs)
    • 6.2.3 Heavy Commercial Vehicles (HCVs)

7 Global Automotive Artificial Intelligence (AI) Market, By Propulsion Type

  • 7.1 Internal Combustion Engine (ICE) Vehicles
  • 7.2 Battery Electric Vehicles (BEVs)
  • 7.3 Plug-in Hybrid Electric Vehicles (PHEVs)
  • 7.4 Hybrid Electric Vehicles (HEVs)
  • 7.5 Fuel Cell Electric Vehicles (FCEVs)

8 Global Automotive Artificial Intelligence (AI) Market, By Deployment Mode

  • 8.1 On-Premise / On-Board AI
  • 8.2 Cloud-Based AI
  • 8.3 Edge AI

9 Global Automotive Artificial Intelligence (AI) Market, By Application

  • 9.1 Autonomous Driving
  • 9.2 Advanced Driver Assistance Systems (ADAS)
  • 9.3 Human-Machine Interface (HMI)
  • 9.4 Predictive Maintenance
  • 9.5 Intelligent Traffic Management
  • 9.6 Fleet Management
  • 9.7 Insurance Telematics & Risk Assessment
  • 9.8 Manufacturing & Production Optimization
  • 9.9 Cybersecurity & Fraud Detection

10 Global Automotive Artificial Intelligence (AI) Market, By End User

  • 10.1 Automotive OEMs
  • 10.2 Tier-1 Suppliers
  • 10.3 Fleet Operators
  • 10.4 Mobility-as-a-Service (MaaS) Providers
  • 10.5 Automotive Dealers & Service Providers

11 Global Automotive Artificial Intelligence (AI) Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 NVIDIA Corporation
  • 14.2 Mobileye Global Inc.
  • 14.3 Qualcomm Incorporated
  • 14.4 Robert Bosch GmbH
  • 14.5 Continental AG
  • 14.6 DENSO Corporation
  • 14.7 Aptiv PLC
  • 14.8 ZF Friedrichshafen AG
  • 14.9 Valeo SA
  • 14.10 Magna International Inc.
  • 14.11 NXP Semiconductors N.V.
  • 14.12 Renesas Electronics Corporation
  • 14.13 Tesla, Inc.
  • 14.14 Waymo LLC
  • 14.15 Hyundai Mobis Co., Ltd.

List of Tables

  • Table 1 Global Automotive Artificial Intelligence (AI) Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Automotive Artificial Intelligence (AI) Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Automotive Artificial Intelligence (AI) Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global Automotive Artificial Intelligence (AI) Market Outlook, By AI Processors (2023-2034) ($MN)
  • Table 5 Global Automotive Artificial Intelligence (AI) Market Outlook, By Sensors (2023-2034) ($MN)
  • Table 6 Global Automotive Artificial Intelligence (AI) Market Outlook, By Cameras (2023-2034) ($MN)
  • Table 7 Global Automotive Artificial Intelligence (AI) Market Outlook, By Radar (2023-2034) ($MN)
  • Table 8 Global Automotive Artificial Intelligence (AI) Market Outlook, By LiDAR (2023-2034) ($MN)
  • Table 9 Global Automotive Artificial Intelligence (AI) Market Outlook, By Edge Computing Devices (2023-2034) ($MN)
  • Table 10 Global Automotive Artificial Intelligence (AI) Market Outlook, By Software (2023-2034) ($MN)
  • Table 11 Global Automotive Artificial Intelligence (AI) Market Outlook, By Machine Learning Platforms (2023-2034) ($MN)
  • Table 12 Global Automotive Artificial Intelligence (AI) Market Outlook, By Computer Vision Software (2023-2034) ($MN)
  • Table 13 Global Automotive Artificial Intelligence (AI) Market Outlook, By Natural Language Processing (NLP) Software (2023-2034) ($MN)
  • Table 14 Global Automotive Artificial Intelligence (AI) Market Outlook, By Predictive Analytics Software (2023-2034) ($MN)
  • Table 15 Global Automotive Artificial Intelligence (AI) Market Outlook, By Autonomous Driving Software (2023-2034) ($MN)
  • Table 16 Global Automotive Artificial Intelligence (AI) Market Outlook, By Services (2023-2034) ($MN)
  • Table 17 Global Automotive Artificial Intelligence (AI) Market Outlook, By Vehicle Type (2023-2034) ($MN)
  • Table 18 Global Automotive Artificial Intelligence (AI) Market Outlook, By Passenger Cars (2023-2034) ($MN)
  • Table 19 Global Automotive Artificial Intelligence (AI) Market Outlook, By Commercial Vehicles (2023-2034) ($MN)
  • Table 20 Global Automotive Artificial Intelligence (AI) Market Outlook, By Light Commercial Vehicles (LCVs) (2023-2034) ($MN)
  • Table 21 Global Automotive Artificial Intelligence (AI) Market Outlook, By Medium Commercial Vehicles (MCVs) (2023-2034) ($MN)
  • Table 22 Global Automotive Artificial Intelligence (AI) Market Outlook, By Heavy Commercial Vehicles (HCVs) (2023-2034) ($MN)
  • Table 23 Global Automotive Artificial Intelligence (AI) Market Outlook, By Propulsion Type (2023-2034) ($MN)
  • Table 24 Global Automotive Artificial Intelligence (AI) Market Outlook, By Internal Combustion Engine (ICE) Vehicles (2023-2034) ($MN)
  • Table 25 Global Automotive Artificial Intelligence (AI) Market Outlook, By Battery Electric Vehicles (BEVs) (2023-2034) ($MN)
  • Table 26 Global Automotive Artificial Intelligence (AI) Market Outlook, By Plug-in Hybrid Electric Vehicles (PHEVs) (2023-2034) ($MN)
  • Table 27 Global Automotive Artificial Intelligence (AI) Market Outlook, By Hybrid Electric Vehicles (HEVs) (2023-2034) ($MN)
  • Table 28 Global Automotive Artificial Intelligence (AI) Market Outlook, By Fuel Cell Electric Vehicles (FCEVs) (2023-2034) ($MN)
  • Table 29 Global Automotive Artificial Intelligence (AI) Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 30 Global Automotive Artificial Intelligence (AI) Market Outlook, By On-Premise / On-Board AI (2023-2034) ($MN)
  • Table 31 Global Automotive Artificial Intelligence (AI) Market Outlook, By Cloud-Based AI (2023-2034) ($MN)
  • Table 32 Global Automotive Artificial Intelligence (AI) Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 33 Global Automotive Artificial Intelligence (AI) Market Outlook, By Application (2023-2034) ($MN)
  • Table 34 Global Automotive Artificial Intelligence (AI) Market Outlook, By Autonomous Driving (2023-2034) ($MN)
  • Table 35 Global Automotive Artificial Intelligence (AI) Market Outlook, By Advanced Driver Assistance Systems (ADAS) (2023-2034) ($MN)
  • Table 36 Global Automotive Artificial Intelligence (AI) Market Outlook, By Human-Machine Interface (HMI) (2023-2034) ($MN)
  • Table 37 Global Automotive Artificial Intelligence (AI) Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 38 Global Automotive Artificial Intelligence (AI) Market Outlook, By Intelligent Traffic Management (2023-2034) ($MN)
  • Table 39 Global Automotive Artificial Intelligence (AI) Market Outlook, By Fleet Management (2023-2034) ($MN)
  • Table 40 Global Automotive Artificial Intelligence (AI) Market Outlook, By Insurance Telematics & Risk Assessment (2023-2034) ($MN)
  • Table 41 Global Automotive Artificial Intelligence (AI) Market Outlook, By Manufacturing & Production Optimization (2023-2034) ($MN)
  • Table 42 Global Automotive Artificial Intelligence (AI) Market Outlook, By Cybersecurity & Fraud Detection (2023-2034) ($MN)
  • Table 43 Global Automotive Artificial Intelligence (AI) Market Outlook, By End User (2023-2034) ($MN)
  • Table 44 Global Automotive Artificial Intelligence (AI) Market Outlook, By Automotive OEMs (2023-2034) ($MN)
  • Table 45 Global Automotive Artificial Intelligence (AI) Market Outlook, By Tier-1 Suppliers (2023-2034) ($MN)
  • Table 46 Global Automotive Artificial Intelligence (AI) Market Outlook, By Fleet Operators (2023-2034) ($MN)
  • Table 47 Global Automotive Artificial Intelligence (AI) Market Outlook, By Mobility-as-a-Service (MaaS) Providers (2023-2034) ($MN)
  • Table 48 Global Automotive Artificial Intelligence (AI) Market Outlook, By Automotive Dealers & Service Providers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.