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IoT 云端服务边缘运算

市场调查报告书

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1594864

自动驾驶汽车市场的全球边缘运算 - 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个工作天内

价格

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简介目录

概述

全球自动驾驶汽车边缘运算市场规模在2023年达到75亿美元，预计到2031年将达到384亿美元，2024-2031年预测期间复合年增长率为22.65%。

边缘运算代表了一种新兴的运算范式，涵盖位于用户位置或附近的各种网路和设备。这种方法着重于处理更接近资料来源的资料，从而实现更快、更大容量的资料处理，从而获得更具可操作性的即时见解。与边缘运算整合的自动驾驶汽车的未来为交通运输业的转型带来了巨大的潜力。

自动驾驶汽车已经透过提高安全性、舒适性和便利性来重塑出行方式。边缘运算是一种直接在设备或网路边缘而不是在云端促进本地资料处理和分析的技术，为自动驾驶汽车操作带来了新的效率和速度。透过显着减少延迟、频宽使用和资料储存要求，边缘运算使自动驾驶车辆能够更有效、更经济地运行。

因此，自动驾驶汽车和边缘运算的整合预示着更安全、更便捷和永续交通的未来。在此背景下，边缘运算有望在出行革命中发挥关键作用，巩固其作为自动驾驶汽车发展关键技术的地位。 2022 年 11 月，NVIDIA 推出了 DRIVE Thor，这是一款集中式汽车计算机，它将集群、资讯娱乐、自动驾驶和停车等功能统一到一个经济高效的单一系统中。

动力学

支援 MEC 的应用程式

将移动边缘运算 (MEC) 融入自动驾驶汽车的进程正在迅速推进，从而提高了车辆效率并促进了新服务的发展。汽车边缘运算联盟 (AECC) 等组织在推动这些创新方面发挥着至关重要的作用，倡导在智慧驾驶解决方案中实施 MEC。

研究人员预计，MEC 将促进由云端运算支援的即时数据驱动应用程序，例如动态地图和驾驶员辅助系统。为了使这些技术蓬勃发展，车辆必须连接到能够发送大量资料的高容量网络，以确保功能不间断。 MEC 还透过将每辆车转换为资料储存库来实现向行动即服务的转变。这为导航辅助、共乘和交通控制系统等外部服务创造了机会。

此外，车辆边缘运算可以使保险公司透过即时监控驾驶行为提供基于使用情况的保险，从而增强金融和保险业的发展。蜂窝、Wi-Fi 和低功耗广域 (LPWA) 网路等多样化的连接选择将把汽车连接到分散式运算平台，从而增强服务产品和营运效率。

5G 对提高效率和连接性的影响

5G 技术将为连网汽车应用提供必要的频宽、低延迟和可靠性，从而显着提高自动驾驶汽车的边缘运算能力。增强型行动宽频 (EMBB) 使 5G 能够提供高达每秒 10 GB 的速度，比 4G 技术快五到十倍，有利于车载资讯娱乐、车辆远端操作和即时人工操作等高频宽应用。

此外，5G广泛的物联网功能可实现每平方公里多达100万个连接，确保众多汽车和连网基础设施能够顺利运行，而不会造成网路拥塞或中断。 5G 提供的超低延迟通讯 (URLLC) 延迟可能达到 1 毫秒，比 4G 高出五到十五倍，这对于即时车辆操作（包括物件追踪和智慧交通管理）至关重要。这种低延迟、高可靠性的连接有助于将非安全关键工作负载（包括资讯娱乐和交通控制）从车载系统或云端传输到边缘

实施成本高

建立和实施边缘运算系统需要复杂的设备，包括高效能 CPU、感测器和资料储存解决方案，而这些设备的成本可能很高。此外，需要包括 5G 网路在内的弹性连接基础设施来促进即时资料处理，这也增加了总支出。大量的初始投资可能会带来挑战，特别是对于小型汽车製造商和技术提供者来说，他们可能会发现很难验证广泛实施所需的财务承诺。

此外，边缘运算系统的持续维护和增强也会增加营运费用。随着技术的迅速发展，持续增强和结合新颖功能的必要性可能会增加边缘运算的长期费用。这种财务负担是更广泛采用的障碍，因为公司必须相对于增强车辆自主性和性能的预期优势来评估安装费用。因此，费用的增加仍然是自动驾驶汽车行业边缘运算扩张的重大障碍。

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