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自動車向け生成AIの世界市場 (2025年～2032年)

Global Gen AI in Automotive Market - 2025-2032

出版日
ページ情報
英文 225 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
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価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
自動車向け生成AIの世界市場 (2025年～2032年)
出版日: 2025年03月11日
発行: DataM Intelligence
ページ情報: 英文 225 Pages
納期: 即日から翌営業日
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  • 目次
概要

世界の自動車向け生成AI市場は、2024年に5億1,450万米ドルに達し、2032年には26億900万米ドルに達すると予測され、予測期間2025年のCAGRは22.50%で成長する見込みです。世界の自動車向け生成AI（Gen AI）市場は、AIを活用した設計、自律走行、パーソナライズされた顧客体験の進歩により、急速な拡大を遂げています。インテリジェントな自動化、データ主導の洞察、リアルタイムの意思決定に対する需要が、自動車アプリケーション全体における生成AIの採用に拍車をかけています。

自動車メーカーは、AIを活用した設計最適化、予知保全、インテリジェントな車両システムを統合し、安全性と効率を高めています。政府の取り組み、持続可能性の目標、コネクテッドカー・エコシステムの進化が、市場の成長をさらに後押ししています。2030年までに、AIとEVによる電力需要は2023年比で55%増加すると予想されますが、供給は約15%しか伸びない可能性があります。

自律走行車の開発における生成AIの台頭は加速しており、テスラのような企業は車両学習や予測分析にAIを活用しています。ディープラーニングモデルを搭載したテスラのオートパイロットシステムは、30億マイルを超える走行データを分析し、ADAS機能を継続的に強化しています。

アジア太平洋地域は、急速なデジタル化、政府のインセンティブ、AIベースの自動車技術への投資によって、AI自動車市場の急成長を目の当たりにしています。中国と日本は、政府が支援するインテリジェント・モビリティ・ソリューションの推進により、AIを活用した車両統合をリードしています。チャイナ・エコノミック・ネットによると、BYD、吉利、東風、奇瑞などの大手自動車メーカーは、AIを活用して効率を高め、サービスをパーソナライズしています。2025年には、中国の新車の75％以上にインテリジェント・コックピットが搭載されると予想されています。

市場動向

AI搭載自律走行車に対する需要の高まり

自律走行車の導入拡大が、自動車分野における生成AI市場の成長を大きく後押ししています。AIを搭載したADAS（先進運転支援システム）や自動運転技術は、交通安全を強化しヒューマンエラーを減らすことでモビリティに革命をもたらしています。これらの技術は、AIを活用して複雑な運転シナリオを分析し対応することで、自動車の安全性と効率を向上させています。

テスラ、ウェイモ、ゼネラルモーターズなどの大手自動車メーカーは、AIを活用した自動運転システムに多額の投資を行っており、これが市場の成長を加速させています。自律走行車は、ヒューマンエラーによる交通事故の最大90%を防止し、年間約1,900億米ドルを大幅に節約する可能性を秘めています。この事故の大幅な減少は、交通安全に対するAIの変革的影響を浮き彫りにし、自動車産業を再形成する自律走行車の将来の可能性を強調しています。

AIを活用した予知保全とスマート製造

AIを活用した予知保全とスマート製造は、業務効率と費用対効果を高めることで自動車産業を変革しています。予測分析は、生産ラインの最適化、欠陥の検出、ダウンタイムの最小化において重要な役割を果たしています。このアプローチにより、メーカーは潜在的な問題が深刻化する前に予見して対処することができ、全体的な生産性と信頼性の大幅な向上につながります。例えば、予知保全は予期せぬ故障を減らすことができ、これは継続的な生産を維持し、修理コストを削減する上で特に有益です。

予知保全にAIを統合することで、予期せぬ故障が70％減少し、業務生産性が25％向上し、保全コストが25％削減されます。さらに、AIを活用した品質管理システムは、ミスを最小限に抑えながら高い生産水準を確保しています。例えば、BMWのレーゲンスブルク工場では、車両組立工程で高度な分析システムを活用し、潜在的な欠陥を早期に特定することで、車両組立の中断を大幅に削減しました。このプロアクティブなアプローチは、安全性と効率を高めるだけでなく、より持続可能で信頼性の高い製造プロセスにも貢献しています。

高い導入コストとデータ・プライバシーの懸念

自動車分野における生成AIの統合は、大きな変革をもたらす準備が整っていますが、同時に大きな課題にも直面しています。大きなハードルのひとつは、導入コストの高さです。AIを活用したソリューションには、ハードウェア、ソフトウェア、トレーニングへの多額の投資が必要となります。例えば、自動車製造にAIを導入する場合、1施設あたり5億米ドルものコストがかかります。この経済的負担は多くの企業にとって困難であり、普及の大きな障壁となっています。

もうひとつの重要な課題は、特にAIを活用したドライバー・モニタリング・システムにおける、データ・プライバシーに関する懸念です。これらのシステムは、コンプライアンスを確保し、消費者の信頼を維持するために対処しなければならない規制上の問題を提起しています。データのプライバシーとセキュリティは、AI技術を取り巻く規制環境に直接影響するため、最も重要です。これらの懸念に対処することは、市場の持続的成長と自動車産業における生成AIの統合を成功させるために極めて重要です。これらの課題を克服することで、企業はAIの可能性を最大限に引き出し、設計、製造、顧客体験を強化し、最終的にこの分野におけるイノベーションと競合を促進することができます。

目次

第1章 分析手法と分析範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • AI搭載型の自動運転車の需要増大
      • AIを活用した予知保全とスマート製造
    • 抑制要因
      • 高い導入コストとデータプライバシーの懸念
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • 持続可能な分析
  • DMIの見解

第6章 コンポーネント別

  • マイクロプロセッサ
  • GPU (グラフィックスプロセッシングユニット)
  • FPGA (フィールドプログラマブルゲートアレイ)
  • メモリ・ストレージシステム
  • イメージセンサー
  • 生体認証スキャナ
  • その他

第7章 システムの種類別

  • 乗用車
  • 商用車

第8章 技術別

  • 深層学習
  • 機械学習
  • コンピュータビジョン
  • コンテキスト認識コンピューティング
  • その他

第9章 プロセス別

  • 信号認識
  • 画像認識
  • データマイニング
  • その他

第10章 用途別

  • 車両設計・製造最適化
  • ADAS（先進運転支援システム）
  • ヒューマンマシンインターフェース（HMI）
  • コネクテッドカー技術
  • 自動運転技術
  • その他の用途

第11章 サスティナビリティ分析

  • 環境分析
  • 経済分析
  • ガバナンス分析

第12章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他欧州地域
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第13章 競合情勢

  • 競合シナリオ
  • 市況・シェア分析
  • M&A分析

第14章 企業プロファイル

  • Microsoft Corporation
    • 企業概要
    • 製品ポートフォリオ・概要
    • 財務概要
    • 主な動向
  • Intel Corporation
  • Alphabet Inc.
  • Nvidia Corporation
  • International Business Machines Corporation
  • Qualcomm Inc.
  • Tesla, Inc
  • Amazon Web Services, Inc.
  • Accenture
  • Advanced Micro Devices, Inc.

第15章 付録

目次
Product Code: ICT9233

Global Gen AI in Automotive Market reached US$ 514.50 million in 2024 and is expected to reach US$ 2,609.00 million by 2032, growing with a CAGR of 22.50% during the forecast period 2025-2032. The global generative AI (Gen AI) market in the automotive sector is experiencing rapid expansion, driven by advancements in AI-powered design, autonomous driving, and personalized customer experiences. The demand for intelligent automation, data-driven insights, and real-time decision-making has fueled the adoption of Gen AI across automotive applications.

Automakers are integrating AI-driven design optimization, predictive maintenance, and intelligent vehicle systems to enhance safety and efficiency. Government initiatives, sustainability goals, and the evolution of connected car ecosystems further support market growth. By 2030, demand for power from AI and EVs is expected to increase by 55% relative to 2023, while supply may grow only by about 15%.

The rise of generative AI in autonomous vehicle development has accelerated, with companies like Tesla leveraging AI for fleet learning and predictive analytics. Tesla's Autopilot system, powered by deep learning models, has analyzed over 3 billion miles of driving data, continuously enhancing ADAS capabilities.

The Asia-Pacific region is witnessing the fastest growth in the Gen AI automotive market, driven by rapid digitalization, government incentives, and investments in AI-based vehicle technologies. China and Japan lead AI-powered vehicle integration, with government-backed initiatives promoting intelligent mobility solutions. According to China Economic Net, major automobile manufacturers such as BYD, Geely, Dongfeng, and Chery are leveraging AI to enhance efficiency and personalise services. It is expected that over 75% of new cars in China will be equipped with intelligent cockpits in 2025.

Dynamics

Rising Demand for AI-Powered Autonomous Vehicles

The increasing adoption of autonomous vehicles is significantly driving the growth of the generative AI market in the automotive sector. AI-powered Advanced Driver Assistance Systems (ADAS) and self-driving technologies are revolutionizing mobility by enhancing road safety and reducing human error. These technologies utilize AI to analyze and respond to complex driving scenarios, thereby improving vehicle safety and efficiency.

Major automakers such as Tesla, Waymo, and General Motors are heavily investing in AI-driven self-driving systems, which is accelerating market growth. Autonomous vehicles have the potential to prevent up to 90% of road accidents caused by human error, significantly saving approximately US$ 190 billion per year. This substantial reduction in accidents highlights the transformative impact of AI on road safety and underscores the future potential of autonomous vehicles in reshaping the automotive industry.

AI-Enabled Predictive Maintenance & Smart Manufacturing

AI-powered predictive maintenance and smart manufacturing are transforming the automotive industry by enhancing operational efficiency and cost-effectiveness. Predictive analytics play a crucial role in optimizing production lines, detecting defects, and minimizing downtime. This approach allows manufacturers to anticipate and address potential issues before they escalate, leading to significant improvements in overall productivity and reliability. For instance, predictive maintenance can reduce unexpected breakdowns, which are particularly beneficial for maintaining continuous production and reducing repair costs.

The integration of AI in predictive maintenance decreases unexpected breakdowns by 70%, boosts operational productivity by 25%, and lowers maintenance costs by 25%. Furthermore, AI-driven quality control systems are ensuring high production standards with minimal errors. For example, BMW's Regensburg plant utilized an advanced analytical system in its vehicle assembly process to identify potential faults early, significantly reducing disruptions in vehicle assembly. This proactive approach not only enhances safety and efficiency but also contributes to a more sustainable and reliable manufacturing process.

High Implementation Costs & Data Privacy Concerns

The integration of generative AI in the automotive sector is poised to bring about significant transformations, but it also encounters substantial challenges. One of the major hurdles is the high cost of implementation. AI-driven solutions necessitate considerable investments in hardware, software, and training. For instance, implementing AI in automotive manufacturing costs up to $500 million per facility. This financial burden be daunting for many companies, making it a significant barrier to widespread adoption.

Another critical challenge is data privacy concerns, particularly with AI-powered driver monitoring systems. These systems raise regulatory issues that must be addressed to ensure compliance and maintain consumer trust. Data privacy and security are paramount, as they directly impact the regulatory environment surrounding AI technology. Addressing these concerns is crucial for sustained market growth and the successful integration of generative AI in the automotive industry. By overcoming these challenges, companies can unlock the full potential of AI to enhance design, manufacturing, and customer experiences, ultimately driving innovation and competitiveness in the sector.

Segment Analysis

The global Gen AI in Automotive market is segmented based on component, vehicle type, technology, process, application, and region.

Passenger Vehicles Represent The Largest Segment

Passenger vehicles dominate the global generative AI in automotive market, with significant adoption of AI-powered solutions. Major automakers like Mercedes-Benz, BMW, and Tesla are at the forefront of integrating generative AI into various aspects of vehicle technology. For instance, Mercedes-Benz has introduced a GPT-powered voice assistant in over 900,000 vehicles, enhancing driver interaction by answering complex queries and providing real-time recommendations. Additionally, BMW's Emotional Intelligence system, featured in the 2024 7 Series, evaluates driver emotions to improve safety and comfort. These advancements underscore the growing role of AI in enhancing the driving experience.

The integration of generative AI extends beyond infotainment systems to autonomous functionalities and design optimization. Tesla's Autopilot system, for example, processes data from millions of vehicles to continuously refine its self-driving algorithms. European Road Safety Council assumes that advanced driver assistance systems will be able to reduce the number of road fatalities by up to 30 percent due to their use of AI. Furthermore, companies like Toyota Research Institute are using generative AI to assist vehicle designers by integrating engineering constraints with creative inputs. This trend highlights the potential for AI to transform both the design and operational efficiency of vehicles in the coming years.

Geographical Penetration

Strong R&D Investments and Regulatory Frameworks Supports Gen AI In North America

North America is at the forefront of the generative AI automotive market, driven by robust R&D investments, supportive regulatory frameworks, and technological advancements. The U.S. and Canada are leading the charge in AI adoption across various sectors, including autonomous driving, predictive analytics, and smart manufacturing. Canada's national AI strategy has invested over $2 billion to support AI and digital research and innovation on sustainable automotive solutions. Similarly, US is actively developing AI-based vehicle safety regulations, further bolstering the region's leadership in this field.

Major automotive companies such as Tesla, Ford, and General Motors are pioneering AI-driven innovations in vehicle connectivity and advanced driver-assistance systems (ADAS). Ford, for instance, uses AI-powered predictive analytics to enhance supply chain resilience, mitigating risks from global semiconductor disruptions. In manufacturing, companies like BMW are leveraging AI for quality control, ensuring superior production standards. Beyond manufacturing, AI is also transforming retail strategies. For instance, CarMax's AI PriceOptimize which is an AI-driven pricing optimization systems that adjust vehicle prices in real-time based on numerous variables, enhancing market competitiveness.

Competitive Landscape

The major global players in the market include Microsoft Corporation, Intel Corporation, Alphabet Inc., Nvidia Corporation, International Business Machines Corporation, Qualcomm Inc., Tesla, Inc, Amazon Web Services, Inc., Accenture, and Advanced Micro Devices, Inc.

Sustainable Analysis

The integration of generative AI in the automotive sector is closely aligned with goals of sustainability, safety, and efficiency. Automakers are utilizing AI to drive design innovation, enhance predictive maintenance, and optimize manufacturing processes. Additionally, AI-powered digital assistants are improving user experiences by offering personalized services, while autonomous driving systems are contributing to enhanced road safety. These advancements are transforming the industry by accelerating innovation cycles, reducing costs, and improving overall vehicle performance.

Despite the promising applications of AI in the automotive industry, challenges such as high implementation costs and ethical concerns regarding AI decision-making remain significant hurdles. However, these challenges have not deterred market players from investing heavily in AI research and development. The industry is poised for sustained growth as companies continue to explore new applications of AI. The European Commission's AI Act is expected to provide much-needed regulatory clarity, which will foster responsible AI deployment in automotive applications. This regulatory framework will likely play a crucial role in ensuring that AI technologies are developed and used ethically and effectively across the sector.

Recent Developments

  • January 2025, On January 7, Intel announced the availability of the Adaptive Control Unit (ACU), specifically designed for electric vehicle (EV) powertrains and zonal controller applications. The ACU U310 is a cutting-edge processing unit that consolidates multiple real-time, safety-critical, and cybersecure functions into a single chip, enhancing efficiency and security in modern EV architectures.
  • In December 2024, Waymo, Alphabet's self-driving technology subsidiary, expanded its fully autonomous ride-hailing services in San Francisco and Phoenix. With millions of driverless miles logged, Waymo continues to demonstrate the viability of AI-powered transportation, reshaping urban mobility with its advanced sensor arrays and AI algorithms.
  • October, 2024, Qualcomm and Alphabet announced a strategic partnership to advance AI-driven automotive solutions, enhancing autonomous capabilities and in-car intelligence. Additionally, Mercedes-Benz inked a significant deal to integrate advanced semiconductor technology into its vehicles, reinforcing the industry's shift toward high-performance computing in mobility.
  • December 2023, Audi and Reply partnered with Amazon Web Services (AWS) to improve enterprise search experiences using a Generative AI chatbot. The solution, built on Retrieval Augmented Generation (RAG), utilizes AWS tools such as Amazon SageMaker and Amazon OpenSearch Service to enhance data retrieval and operational efficiency.

By Component

  • Microprocessors
  • Graphics Processing Unit (GPU)
  • Field Programmable Gate Array (FPGA)
  • Memory And Storage Systems
  • Image Sensors
  • Biometric Scanners
  • Others

By Vehicle Type

  • Passenger Vehicles
  • Commercial Vehicles

By Technology

  • Deep Learning
  • Machine Learning
  • Computer Vision
  • Context-Aware Computing
  • Others

By Process

  • Signal Recognition
  • Image Recognition
  • Data Mining
  • Others

By Application

  • Vehicle Design & Manufacturing Optimization
  • Advanced Driver Assistance Systems (Adas)
  • Human - Machine Interface (Hmis)
  • Connected Car Technologies
  • Autonomous Driving Technologies
  • Other Applications

By Region

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Spain
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

Why Purchase the Report?

  • To visualize the global gen AI in automotive market segmentation based on component type, system type, technology, application, end-user, & region.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points at the gen AI in the automotive market level for all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global gen AI in the automotive market report would provide approximately 78 tables, 80 figures, and 225 pages.

Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Component
  • 3.2. Snippet by Vehicle Type
  • 3.3. Snippet by Technology
  • 3.4. Snippet by Process
  • 3.5. Snippet by Application
  • 3.6. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Rising Demand for AI-Powered Autonomous Vehicles
      • 4.1.1.2. AI-Enabled Predictive Maintenance & Smart Manufacturing
    • 4.1.2. Restraints
      • 4.1.2.1. High Implementation Costs & Data Privacy Concerns
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. Sustainable Analysis
  • 5.6. DMI Opinion

6. By Component

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 6.1.2. Market Attractiveness Index, By Component
  • 6.2. Microprocessors*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Graphics Processing Unit (GPU)
  • 6.4. Field Programmable Gate Array (FPGA)
  • 6.5. Memory And Storage Systems
  • 6.6. Image Sensors
  • 6.7. Biometric Scanners
  • 6.8. Others

7. By System Type

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By System Type
    • 7.1.2. Market Attractiveness Index, By System Type
  • 7.2. Passenger Vehicles*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Commercial Vehicles

8. By Technology

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 8.1.2. Market Attractiveness Index, By Technology
  • 8.2. Deep Learning*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Machine Learning
  • 8.4. Computer Vision
  • 8.5. Context-Aware Computing
  • 8.6. Others

9. By Process

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
    • 9.1.2. Market Attractiveness Index, By Process
  • 9.2. Signal Recognition*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Image Recognition
  • 9.4. Data Mining
  • 9.5. Others

10. By Application

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.1.2. Market Attractiveness Index, By Application
  • 10.2. Vehicle Design & Manufacturing Optimization*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Advanced Driver Assistance Systems (Adas)
  • 10.4. Human - Machine Interface (HMIS)
  • 10.5. Connected Car Technologies
  • 10.6. Autonomous Driving Technologies
  • 10.7. Other Applications

11. Sustainability Analysis

  • 11.1. Environmental Analysis
  • 11.2. Economic Analysis
  • 11.3. Governance Analysis

12. By Region

  • 12.1. Introduction
    • 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 12.1.2. Market Attractiveness Index, By Region
  • 12.2. North America
    • 12.2.1. Introduction
    • 12.2.2. Key Region-Specific Dynamics
    • 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
    • 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
    • 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.2.8.1. US
      • 12.2.8.2. Canada
      • 12.2.8.3. Mexico
  • 12.3. Europe
    • 12.3.1. Introduction
    • 12.3.2. Key Region-Specific Dynamics
    • 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
    • 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
    • 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.3.8.1. Germany
      • 12.3.8.2. UK
      • 12.3.8.3. France
      • 12.3.8.4. Italy
      • 12.3.8.5. Spain
      • 12.3.8.6. Rest of Europe
  • 12.4. South America
    • 12.4.1. Introduction
    • 12.4.2. Key Region-Specific Dynamics
    • 12.4.3. Key Region-Specific Dynamics
    • 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
    • 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
    • 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.4.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.4.9.1. Brazil
      • 12.4.9.2. Argentina
      • 12.4.9.3. Rest of South America
  • 12.5. Asia-Pacific
    • 12.5.1. Introduction
    • 12.5.2. Key Region-Specific Dynamics
    • 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
    • 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
    • 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.5.8.1. China
      • 12.5.8.2. India
      • 12.5.8.3. Japan
      • 12.5.8.4. Australia
      • 12.5.8.5. Rest of Asia-Pacific
  • 12.6. Middle East and Africa
    • 12.6.1. Introduction
    • 12.6.2. Key Region-Specific Dynamics
    • 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle Type
    • 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Process
    • 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

13. Competitive Landscape

  • 13.1. Competitive Scenario
  • 13.2. Market Positioning/Share Analysis
  • 13.3. Mergers and Acquisitions Analysis

14. Company Profiles

  • 14.1. Microsoft Corporation*
    • 14.1.1. Company Overview
    • 14.1.2. Product Portfolio and Description
    • 14.1.3. Financial Overview
    • 14.1.4. Key Developments
  • 14.2. Intel Corporation
  • 14.3. Alphabet Inc.
  • 14.4. Nvidia Corporation
  • 14.5. International Business Machines Corporation
  • 14.6. Qualcomm Inc.
  • 14.7. Tesla, Inc
  • 14.8. Amazon Web Services, Inc.
  • 14.9. Accenture
  • 14.10. Advanced Micro Devices, Inc.

LIST NOT EXHAUSTIVE

15. Appendix

  • 15.1. About Us and Services
  • 15.2. Contact Us