首頁 > 市場調查報告書 > 通訊

IoT 雲端服務邊緣運算

市場調查報告書

商品編碼

1594864

自動駕駛汽車市場的全球邊緣運算 - 2024-2031

Global Edge Computing for Autonomous Vehicles Market - 2024-2031

出版日期: 2024年11月21日 | 出版商:

DataM Intelligence | 英文 212 Pages | 商品交期: 最快1-2個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

概述

全球自動駕駛汽車邊緣運算市場規模在2023年達到75億美元，預計到2031年將達到384億美元，2024-2031年預測期間複合年成長率為22.65%。

邊緣運算代表了一種新興的運算範式，涵蓋位於用戶位置或附近的各種網路和設備。這種方法著重於處理更接近資料來源的資料，從而實現更快、更大容量的資料處理，從而獲得更具可操作性的即時見解。與邊緣運算整合的自動駕駛汽車的未來為交通運輸業的轉型帶來了巨大的潛力。

自動駕駛汽車已經透過提高安全性、舒適性和便利性來重塑出行方式。邊緣運算是一種直接在設備或網路邊緣而不是在雲端促進本地資料處理和分析的技術，為自動駕駛汽車操作帶來了新的效率和速度。透過顯著減少延遲、頻寬使用和資料儲存要求，邊緣運算使自動駕駛車輛能夠更有效、更經濟地運行。

因此，自動駕駛汽車和邊緣運算的整合預示著更安全、更便捷和永續交通的未來。在此背景下，邊緣運算有望在出行革命中發揮關鍵作用，鞏固其作為自動駕駛汽車發展關鍵技術的地位。 2022 年 11 月，NVIDIA 推出了 DRIVE Thor，這是一款集中式汽車電腦，它將集群、資訊娛樂、自動駕駛和停車等功能統一到一個經濟高效的單一系統中。

動力學

支援 MEC 的應用程式

將移動邊緣運算 (MEC) 融入自動駕駛汽車的進程正在迅速推進，從而提高了車輛效率並促進了新服務的發展。汽車邊緣運算聯盟 (AECC) 等組織在推動這些創新方面發揮著至關重要的作用，倡導在智慧駕駛解決方案中實施 MEC。

研究人員預計，MEC 將促進由雲端運算支援的即時數據驅動應用程式，例如動態地圖和駕駛員輔助系統。為了使這些技術蓬勃發展，車輛必須連接到能夠發送大量資料的高容量網路，以確保功能不間斷。 MEC 還透過將每輛車轉換為資料儲存庫來實現向行動即服務的轉變。這為導航輔助、共乘和交通控制系統等外部服務創造了機會。

此外，車輛邊緣運算可以使保險公司透過即時監控駕駛行為提供基於使用情況的保險，從而增強金融和保險業的發展。蜂窩、Wi-Fi 和低功耗廣域 (LPWA) 網路等多樣化的連接選擇將把汽車連接到分散式運算平台，從而增強服務產品和營運效率。

5G 對提高效率和連接性的影響

5G 技術將為連網汽車應用提供必要的頻寬、低延遲和可靠性，從而顯著提高自動駕駛汽車的邊緣運算能力。增強型行動寬頻 (EMBB) 使 5G 能夠提供高達每秒 10 GB 的速度，比 4G 技術快五到十倍，有利於車載資訊娛樂、車輛遠端操作和即時人工操作等高頻寬應用。

此外，5G廣泛的物聯網功能可實現每平方公里多達100萬個連接，確保眾多汽車和連網基礎設施能夠順利運行，而不會造成網路擁塞或中斷。 5G 提供的超低延遲通訊 (URLLC) 延遲可能達到 1 毫秒，比 4G 高出五到十五倍，這對於即時車輛操作（包括物件追蹤和智慧交通管理）至關重要。這種低延遲、高可靠性的連接有助於將非安全關鍵工作負載（包括資訊娛樂和交通控制）從車載系統或雲端傳輸到邊緣

實施成本高

建立和實施邊緣運算系統需要複雜的設備，包括高效能 CPU、感測器和資料儲存解決方案，而這些設備的成本可能很高。此外，需要包括 5G 網路在內的彈性連接基礎設施來促進即時資料處理，這也增加了總支出。大量的初始投資可能會帶來挑戰，特別是對於小型汽車製造商和技術提供者來說，他們可能會發現很難驗證廣泛實施所需的財務承諾。

此外，邊緣運算系統的持續維護和增強也會增加營運費用。隨著技術的迅速發展，持續增強和結合新穎功能的必要性可能會增加邊緣運算的長期費用。這種財務負擔是更廣泛採用的障礙，因為公司必須相對於增強車輛自主性和性能的預期優勢來評估安裝費用。因此，費用的增加仍然是自動駕駛汽車行業邊緣運算擴張的重大障礙。

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

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