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自律走行車向けエッジコンピューティングの世界市場：2024年～2031年

Global Edge Computing for Autonomous Vehicles Market - 2024-2031

出版日
ページ情報
英文 212 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
価格
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=146.82円
自律走行車向けエッジコンピューティングの世界市場：2024年～2031年
出版日: 2024年11月21日
発行: DataM Intelligence
ページ情報: 英文 212 Pages
納期: 即日から翌営業日
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概要

概要

自律走行車向けエッジコンピューティングの世界市場は2023年に75億米ドルに達し、2031年には384億米ドルに達すると予測され、予測期間2024-2031年のCAGRは22.65%で成長する見込みです。

エッジコンピューティングは、ユーザーの位置またはその近くに配置されたさまざまなネットワークやデバイスを包含するコンピューティングの新たなパラダイムを表しています。このアプローチは、より発生源に近い場所でデータを処理することに重点を置いており、それによってより高速で大量のデータ処理が可能になり、より実用的なリアルタイムの洞察につながります。エッジコンピューティングと統合された自律走行車の未来は、交通業界を変革する大きな可能性を秘めています。

自律走行車は、安全性、快適性、利便性を高めることで、すでに移動の形を変えつつあります。エッジコンピューティングは、クラウドではなく、デバイスやネットワークエッジで直接、ローカルなデータ処理や分析を容易にする技術であり、自律走行車の運用に新たなレベルの効率性とスピードをもたらします。待ち時間、帯域幅の使用、データストレージの要件を大幅に削減することで、エッジコンピューティングは自律走行車をより効果的かつコスト効率よく運用することを可能にします。

その結果、自律走行車とエッジコンピューティングの融合は、より安全で、より利用しやすく、持続可能な輸送の未来を予告します。このような背景から、エッジコンピューティングは、移動に革命を起こす上で極めて重要な役割を果たす態勢が整っており、自律走行車の進歩に不可欠な技術としての地位を確固たるものにしています。2022年11月、エヌビディアは、クラスタリング、インフォテインメント、自動運転、駐車などの機能を単一のコスト効率の高いシステムに統合した集中型自動車用コンピュータであるDRIVE Thorを発表しました。

ダイナミクス

MEC対応アプリケーション

自律走行車へのモバイルエッジコンピューティング（MEC）の組み込みは急速に進んでおり、車両の効率を向上させ、新しいサービスを促進しています。オートモーティブエッジコンピューティングコンソーシアム（AECC）のような組織は、インテリジェントドライビングソリューションへのMECの実装を提唱し、これらの技術革新を進める上で重要な役割を果たしています。

調査チームは、MECがクラウドコンピューティングに支えられたダイナミックマッピングや運転支援システムなどのリアルタイムデータ駆動型アプリケーションを促進すると予想しています。これらの技術を成功させるためには、車両は大量のデータを送信できる大容量ネットワークに接続され、中断のない機能を確保する必要があります。MECはまた、各車両をデータリポジトリに変換することで、モビリティ・アズ・ア・サービスへの移行を可能にします。これにより、ナビゲーションアシスタンス、ライドシェアリング、交通管制システムといった外部サービスのチャンスが生まれます。

さらに、車両エッジコンピューティングは、運転行動のリアルタイムモニタリングを通じて保険会社が利用ベースの補償を提供できるようにすることで、金融・保険業界を強化する可能性があります。セルラー、Wi-Fi、低電力広域（LPWA）ネットワークなど、多様な接続選択肢が自動車を分散コンピューティングプラットフォームにつなぎ、サービス提供と運用効率を高める。

効率性とコネクティビティの向上における5Gの影響

5G技術は、コネクテッドカーアプリケーションに必要な帯域幅、低遅延、信頼性を提供することで、自律走行車のエッジコンピューティング能力を著しく向上させる態勢を整えています。エンハンストモバイルブロードバンド（EMBB）により、5Gは毎秒最大10ギガビット（4G技術の5～10倍）の速度を実現し、車載インフォテインメント、車両遠隔操作、リアルタイムのヒューマンマシンインターフェースのレンダリングなどの広帯域幅アプリケーションを容易にします。

さらに、5Gの広範なIoT機能は、1平方キロメートルあたり最大100万接続を容易にし、多数の自動車や相互接続されたインフラがネットワークの混雑や中断なしに円滑に機能することを保証します。5Gが提供する超低遅延通信（URLLC）は、レイテンシが1ミリ秒に達する可能性があり、4Gの5～15倍優れています。この低遅延、高信頼性の接続は、車載システムやクラウドからエッジへのインフォテインメントや交通制御などの非セーフティクリティカルなワークロードの転送を容易にします。

高い導入コスト

エッジコンピューティングシステムの構築と実装には、高性能CPU、センサー、データストレージソリューションなどの高度な機器が必要であり、コストがかかります。さらに、リアルタイムのデータ処理を促進するために、5Gネットワークを含む弾力性のある接続インフラが必要なことも、総出費の一因となっています。特に小規模な自動車メーカーやテクノロジープロバイダにとっては、大規模な導入に必要な財政的コミットメントを検証することが難しいため、多額の初期投資が課題となる可能性があります。

さらに、エッジコンピューティングシステムの継続的なメンテナンスと機能強化は、運用経費を増大させる。技術が急速に進歩するにつれて、継続的な機能拡張と新機能の組み込みが必要になり、エッジコンピューティングの長期的な費用が増大する可能性があります。企業は、車両の自律性と性能の向上という将来的なメリットと比較して、導入費用を評価しなければならないため、この金銭的な負担は、普及の障害となっています。このように、出費の増加は、自律走行車業界におけるエッジコンピューティングの拡大にとって引き続き大きな障害となっています。

目次

第1章 調査手法と調査範囲

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
      • MEC対応アプリケーション
      • 5Gが効率性と接続性の向上に与える影響
    • 抑制要因
      • 導入コストが高い
    • 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
  • ロシア・ウクライナ戦争の影響分析
  • DMIの見解

第6章 コンポーネント別

  • ハードウェア
  • ソフトウェア
  • サービス

第7章 展開別

  • オンプレミス
  • クラウドベース
  • ハイブリッド

第8章 接続性別

  • 5G
  • 4G/LTE
  • Wi-Fi
  • DSRC

第9章 車両別

  • 乗用車
  • 商用車

第10章 用途別

  • 自動運転
  • 予知保全
  • 車両テレマティクス
  • 交通管理
  • フリート管理
  • インフォテインメントとデジタルコックピット
  • その他

第11章 エンドユーザー別

  • OEM
  • フリートオペレーター
  • その他

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
  • アジア太平洋地域
    • 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

  • NVIDIA Corporation
    • 会社概要
    • 製品ポートフォリオと概要
    • 財務概要
    • 主な発展
  • Intel Corporation（Mobileye）
  • Qualcomm Technologies, Inc.
  • Tesla
  • Baidu Apollo
  • Bosch
  • Huawei
  • Waymo（Alphabet Inc.）
  • Amazon Web Services（AWS）
  • Microsoft（Azure）

第15章 付録

目次
Product Code: ICT8786

Overview

Global Edge Computing for Autonomous Vehicles Market reached US$ 7.5 billion in 2023 and is expected to reach US$ 38.4 billion by 2031, growing with a CAGR of 22.65% during the forecast period 2024-2031.

Edge computing represents an emerging paradigm in computing that encompasses various networks and devices positioned at or near the user's location. This approach focuses on processing data closer to its source, thereby enabling faster and higher-volume data handling, which leads to more actionable, real-time insights. The future of autonomous vehicles integrated with edge computing holds tremendous potential for transforming the transportation industry.

Autonomous vehicles are already reshaping travel by enhancing safety, comfort and convenience. Edge computing, a technology that facilitates local data processing and analysis directly on the device or at the network edge rather than in the cloud, introduces a new level of efficiency and speed to autonomous vehicle operations. By significantly reducing latency, bandwidth usage and data storage requirements, edge computing allows autonomous vehicles to operate more effectively and cost-efficiently.

Consequently, the convergence of autonomous vehicles and edge computing heralds a future of safer, more accessible and sustainable transportation. In this context, edge computing is poised to play a pivotal role in revolutionizing travel, solidifying its status as a critical technology for the advancement of autonomous vehicles. In November 2022, NVIDIA introduced DRIVE Thor, a centralized automotive computer that unifies functions such as clustering, infotainment, automated driving and parking into a single, cost-effective system.

Dynamics

MEC-Enabled Applications

The incorporation of Mobile Edge Computing (MEC) into autonomous vehicles is progressing swiftly, improving vehicle efficiency and facilitating new services. Organizations such as the Automotive Edge Computing Consortium (AECC) play a crucial role in advancing these innovations, advocating for the implementation of MEC in intelligent driving solutions.

Researchers anticipate that MEC will facilitate real-time data-driven applications, like dynamic mapping and driver assistance systems, supported by cloud computing. For these technologies to thrive, vehicles must be linked to high-capacity networks capable of sending substantial data quantities, ensuring uninterrupted functionality. MEC also enables the shift to mobility-as-a-service by converting each vehicle into a data repository. This creates chances for external services such as navigation assistance, ride-sharing and traffic control systems.

Moreover, vehicle edge computing may enhance the finance and insurance industries by enabling insurers to provide usage-based coverage through real-time monitoring of driving behavior. Diverse connectivity choices, such as cellular, Wi-Fi and low-power wide-area (LPWA) networks, will link automobiles to distributed computing platforms, thereby enhancing service offerings and operating efficiency.

Impact of 5G on Enhancing Efficiency and Connectivity

5G technology is poised to markedly improve edge computing capabilities for autonomous vehicles by delivering the necessary bandwidth, low latency and dependability for connected-car applications. Enhanced mobile broadband (EMBB) allows 5G to deliver speeds of up to 10 gigabits per second, which is five to ten times faster than 4G technology, facilitating high-bandwidth applications such as in-car infotainment, vehicle teleoperation and real-time human-machine interface rendering.

Moreover, 5G's extensive IoT capabilities facilitate up to one million connections per square kilometer, guaranteeing that numerous cars and interconnected infrastructure can function smoothly without network congestion or interruptions. The ultra-low-latency communications (URLLC) provided by 5G, with latency potentially reaching one millisecond-five to fifteen times superior than 4G-are essential for real-time vehicle operations, including object tracking and intelligent traffic management. This low-latency, high-reliability connection facilitates the transfer of non-safety-critical workloads, including infotainment and traffic control, from onboard systems or the cloud to the edge

High Implementing Cost

Establishing and implementing edge computing systems necessitates sophisticated gear, including high-performance CPUs, sensors and data storage solutions, which can be costly. Furthermore, the necessity for a resilient connectivity infrastructure, encompassing 5G networks, to facilitate real-time data processing contributes to the total expenditure. Significant initial investments might pose a challenge, especially for smaller automakers and technology providers who may find it difficult to validate the financial commitment necessary for extensive implementation.

Additionally, continuous maintenance and enhancements to edge computing systems escalate operational expenses. As technology advances swiftly, the necessity for ongoing enhancements and the incorporation of novel functionalities may escalate the long-term expenses of edge computing. This financial encumbrance is an obstacle for wider adoption, as companies must evaluate the expense of installation relative to the prospective advantages of enhanced vehicle autonomy and performance. Thus, the elevated expenses continue to be a significant impediment to the expansion of edge computing within the autonomous car industry.

Segment Analysis

The global edge computing for autonomous vehicles market is segmented based on component, deployment, connectivity, vehicle, application, end-user and region.

Real-Time Data Processing And Decision-Making in Passenger Vehicles

Edge computing facilitates local data processing within the vehicle, hence diminishing latency and enabling autonomous vehicles to make swifter, more precise judgments. This leads to improved navigation, superior obstacle recognition and enhanced traffic management, all of which augment safety and efficiency on the roadways. Edge computing enables vehicles to communicate with one another and with surrounding infrastructure, thereby augmenting situational awareness and mitigating accidents.

Besides enhancing safety, edge computing diminishes dependence on cloud systems, thereby reducing bandwidth consumption, data storage expenses and the risk of network interruptions. This enables autonomous vehicles to function more efficiently and economically, especially in regions with inadequate network connectivity. With the expansion of the autonomous vehicle market, edge computing will be essential for facilitating advanced functionalities such as predictive maintenance, tailored services and enhanced traffic management, rendering it a pivotal technology for the future of transportation.

Geographical Penetration

Rising Edge Computing In North America

The growing use of IoT devices, the increased need for low-latency processing and the development of 5G technology are all contributing to the notable rise of the edge computing industry in autonomous vehicles in North America. To enable autonomous vehicle applications that need real-time data processing for navigation, safety and operational efficiency, major industry participants are making significant investments in edge computing infrastructure.

North America's dominance in this market is further supported by the region's well-established technology hubs and robust edge computing ecosystem. North America is in a strong position to maintain its leadership in the global edge computing market for autonomous vehicles because to ongoing investments in edge infrastructure and collaborations to support creative use cases.

Competitive Landscape

The major global players in the market include NVIDIA Corporation, Intel Corporation (Mobileye), Qualcomm Technologies, Inc., Tesla, Baidu Apollo, Bosch, Huawei, Waymo (Alphabet Inc.), Amazon Web Services (AWS) and Microsoft (Azure).

Russia-Ukraine War Impact Analysis

Cyberattacks on Ukraine's digital infrastructure exposed weaknesses while simultaneously fostering breakthroughs in digital resilience, resulting in increased dependence on cloud-based systems for uninterrupted operation. The modifications have influenced edge computing, as organizations seek to provide real-time processing in autonomous vehicles via cloud integration and enhanced cybersecurity measures.

The battle has highlighted the necessity for resilient digital infrastructure, becoming edge computing a crucial component in the technological framework of autonomous vehicle development. It has expedited the transition to cloud computing, which has directly impacted the development of edge computing in autonomous vehicles. In their pursuit of developing more robust systems, particularly in edge computing organizations in North America and beyond have drawn insights from the infrastructure assaults in Ukraine to enhance the design of secure and adaptive technology.

In this context, edge computing is essential for facilitating low-latency processing and secure data transfer for autonomous cars, as the demand for real-time decision-making and operational efficiency increases. The conflict has influenced global technology firms and digital geopolitics, prompting heightened investments in solutions that guarantee digital sovereignty and safe operational continuity, hence enhancing the edge computing ecosystem for autonomous vehicles.

Component

  • Hardware
  • Software
  • Services

Deployment

  • On-Premises
  • Cloud-Based
  • Hybrid

Connectivity

  • 5G
  • 4G/LTE
  • Wi-Fi
  • DSRC

Vehicle

  • Passenger Vehicles
  • Commercial Vehicles

Application

  • Autonomous Driving
  • Predictive Maintenance
  • Vehicle Telematics
  • Traffic Management
  • Fleet Management
  • Infotainment and Digital Cockpits
  • Others

End-User

  • OEMs
  • Tier 1 Suppliers
  • Fleet Operators
  • Others

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

Key Developments

  • In January 2023, Belden launched its Single Pair Ethernet (SPE) family of connectivity products aimed at enhancing Ethernet connectivity in challenging settings, such as industrial and transportation sectors. The SPE range comprises IP20-rated PCB jacks, patch cords and cord sets for clean-area connections, as well as IP65/IP67-rated circular M8/M12 patch cables, cord sets and receptacles for dependable industrial Ethernet connections to field devices.
  • In February 2023, Digi International made an announcement. The Digi IX10 cellular router, debuting at DistribuTECH 2023, enhances its portfolio of private cellular network (PCN) solutions, providing essential connectivity for smart grid devices via the CBRS shared spectrum and Anterix Band 8 900 MHz licensed spectrum.
  • In March 2022, Cisco announced a collaboration with Verizon, showcasing a successful proof-of-concept demonstration in Las Vegas that illustrated how cellular and mobile edge computing (MEC) technology can enable autonomous driving solutions without the necessity of costly physical roadside units to enhance the radio signal.

Why Purchase the Report?

  • To visualize the global edge computing for autonomous vehicles market segmentation based on component, deployment, connectivity, vehicle, application, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of the edge computing for autonomous vehicles market-level with 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 edge computing for autonomous vehicles market report would provide approximately 86 tables, 86 figures and 212 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 Deployment
  • 3.3. Snippet by Connectivity
  • 3.4. Snippet by Vehicle
  • 3.5. Snippet by Application
  • 3.6. Snippet by End-User
  • 3.7. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. MEC Enabled Application
      • 4.1.1.2. Impact of 5G on Enhancing Efficiency and Connectivity
    • 4.1.2. Restraints
      • 4.1.2.1. High Implementing Cost
    • 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. Russia-Ukraine War Impact 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. Hardware*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Software
  • 6.4. Services

7. By Deployment

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 7.1.2. Market Attractiveness Index, By Deployment
  • 7.2. On-Premises*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Cloud-Based
  • 7.4. Hybrid

8. By Connectivity

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Connectivity
    • 8.1.2. Market Attractiveness Index, By Connectivity
  • 8.2. 5G*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. 4G/LTE
  • 8.4. Wi-Fi
  • 8.5. DSRC

9. By Vehicle

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle
    • 9.1.2. Market Attractiveness Index, By Vehicle
  • 9.2. Passenger Vehicles*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Commercial Vehicles

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. Autonomous Driving*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Predictive Maintenance
  • 10.4. Vehicle Telematics
  • 10.5. Traffic Management
  • 10.6. Fleet Management
  • 10.7. Infotainment and Digital Cockpits
  • 10.8. Others

11. By End-User

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.1.2. Market Attractiveness Index, By End-User
  • 11.2. OEMs*
    • 11.2.1. Introduction
    • 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 11.3. Fleet Operators
  • 11.4. Others

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 Deployment
    • 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Connectivity
    • 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle
    • 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 End-User
    • 12.2.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.2.9.1. US
      • 12.2.9.2. Canada
      • 12.2.9.3. 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 Deployment
    • 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Connectivity
    • 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle
    • 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 End-User
    • 12.3.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.3.9.1. Germany
      • 12.3.9.2. UK
      • 12.3.9.3. France
      • 12.3.9.4. Italy
      • 12.3.9.5. Spain
      • 12.3.9.6. Rest of Europe
  • 12.4. South America
    • 12.4.1. Introduction
    • 12.4.2. Key Region-Specific Dynamics
    • 12.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Connectivity
    • 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle
    • 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 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 Deployment
    • 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Connectivity
    • 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle
    • 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 End-User
    • 12.5.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 12.5.9.1. China
      • 12.5.9.2. India
      • 12.5.9.3. Japan
      • 12.5.9.4. Australia
      • 12.5.9.5. 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 Deployment
    • 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Connectivity
    • 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Vehicle
    • 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 12.6.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

13. Competitive Landscape

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

14. Company Profiles

  • 14.1. NVIDIA 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 (Mobileye)
  • 14.3. Qualcomm Technologies, Inc.
  • 14.4. Tesla
  • 14.5. Baidu Apollo
  • 14.6. Bosch
  • 14.7. Huawei
  • 14.8. Waymo (Alphabet Inc.)
  • 14.9. Amazon Web Services (AWS)
  • 14.10. Microsoft (Azure)

LIST NOT EXHAUSTIVE

15. Appendix

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