首頁 > 市場調查報告書 > 汽車工業

商用車

市場調查報告書

商品編碼

1876859

人工智慧及其在商用車市場的應用，全球（2024-2029）

AI and its Application in the Commercial Vehicles Market, Global, 2024-2029

出版日期: 2025年10月03日 | 出版商:

Frost & Sullivan | 英文 48 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

本報告檢視了全球商用車市場，並分析了影響商用車產業的當前趨勢和市場力量、與商用車相關的人工智慧技術的進步，以及有關主要企業及其戰略舉措的資訊。

人工智慧對商用車產業三大戰略要務的影響

變革性大趨勢

原因：從車輛設計製造到銷售、營運和安全，人工智慧正在徹底改變商用車產業，為整個生態系統創造價值。人工智慧對於電動車和自動駕駛汽車的無縫整合至關重要，而這項變革將引領產業走向永續的高效運作。
弗若斯特的觀點：人工智慧將在商用車生命週期的關鍵轉型趨勢中發揮關鍵作用。隨著數據生成呈指數級成長，人工智慧對於提高效率和建立所有營運環節的標準化至關重要。

顛覆性技術

原因：人工智慧賦能的車輛設計速度更快、效率更高、外觀更美觀，正在革新商用車產業，協助打造個人化、高效節能的汽車。車載資訊服務和物流是人工智慧推動快速變革的關鍵領域，分別透過整合預測性維護和貨物視覺化，改變市場動態以及車隊管理者與其車隊之間的互動方式。
弗羅斯特的觀點：人工智慧對於革新車隊管理和營運方式至關重要。產業需求不斷成長，包括更快的交付速度、更有效率的新設計、更高的安全性以及個人化的車內體驗。人工智慧是滿足這些需求的關鍵。此外，人工智慧正在革新維護和遠端資訊處理技術，有助於最大限度地降低總營運成本。

壓縮客戶價值鏈

原因：在商用車領域，人工智慧透過降低維護成本和提高營運效率，縮短了客戶價值鏈。汽車人工智慧技術透過語音助理和高級駕駛輔助系統（ADAS）改善駕駛體驗，同時透過減少事故來盈利。
弗若斯特的觀點：隨著每秒產生數TerabyteTB的數據，人工智慧強大的處理能力將簡化傳統決策流程的各個階段，從而實現更快、更有效的決策並創造價值。人工智慧將繼續透過提高營運效率和降低總營運成本，積極推動價值鏈的壓縮。

成長促進因素

車載資訊系統與聯網汽車的發展
不斷擴大的物流和電子商務領域
對效率的需求日益成長
安全性提高
競爭優勢

成長抑制因素

監管限制
假陽性
資料隱私和安全問題
前期成本高
用戶驗收

人工智慧在電腦視覺生命週期中的應用案例
人工智慧在電腦視覺生命週期各階段的應用
競爭環境
主要競爭對手
生態系統 1：供應鏈解決方案 - 人工智慧應用概述
案例研究：FourKites，一家領先的貨物可視性公司
生態系統 2：軟體設計 - 人工智慧應用概述
案例研究：達梭系統，一家領先的設計軟體公司
生態系統 3：遠端資訊處理—人工智慧應用概述
案例研究：Samsara，一家領先的遠端資訊處理和故障預測公司

推動人工智慧在電腦視覺領域應用的關鍵趨勢和案例研究

推動人工智慧在電腦視覺領域應用的關鍵趨勢
趨勢一：自動駕駛
趨勢二：情緒智商
趨勢三：失敗預測
趨勢四：自動化工作指導

人工智慧在電腦視覺產業中的成長要素

成長指標
成長促進因素
成長抑制因素
預測考量
電腦視覺產業的AI收入管道
經營模式及收入管道映射
電腦視覺產業人工智慧總收入估計值
按關鍵電腦視覺應用場景分類的訂閱式人工智慧收入
按地區分類的訂閱式人工智慧收入明細
收入預測
預測分析
價格趨勢

人工智慧在全部區域的應用

人工智慧應用區域概況
影響人工智慧成長的區域因素
區域人工智慧採用率評分
電腦視覺產業人工智慧應用主要區域比較

成長機會：人工智慧在履歷行業的應用

發展機會 1：高品質的車內體驗
成長機會 2：自動化車隊管理營運
成長機會 3：自動配送與駕駛輔助

附錄：後續步驟

簡介目錄

Product Code: PFO8-42

AI is Driving Transformational Growth in Commercial Vehicles

This study examines the development prospects that artificial intelligence (AI) offers the commercial vehicle (CV) industry, focusing on both the revolutionary potential of AI and the difficulties businesses face in fostering growth, including complicated regulations, high capital expenditure, and challenges in incorporating new technology into pre-existing systems as the industry becomes more competitive. Owing to these obstacles, businesses are challenged to scale and maintain growth. In such a scenario, AI is a potential facilitator, providing solutions to boost safety, optimize operations, and improve customer experiences-all of which eventually promote expansion in an industry that is changing quickly.

The study starts by outlining AI in terms of its use throughout the CV life cycle. AI is defined, and several subsets of technologies are examined, including robotics, machine learning, and natural language processing, all of which can be applied in CVs. These technologies improve the efficiency and performance of commercial fleets across several critical fleet activities, including autonomous driving, ADAS and driver behavior, predictive maintenance, and real-time decision-making. From enhancing car design to revolutionizing supply chain operations, AI's influence spans the entire CV life cycle, highlighting its widespread applicability and promise in this field.

The study also discusses how AI is used in design, sales, operations, and in-vehicle features. Each life cycle stage's key ecosystems are examined, and a case study is used to show how AI is impacting the industry. The study includes real-world examples of how businesses are successfully incorporating AI into their operations for each ecosystem and its key fleet applications. Leaders in AI adoption include Dassault Systemes for its ongoing innovation in software-generated designs, FourKites, which uses AI to track vehicle data and monitor fleet performance, and Samsara, which employs AI to monitor fleet performance. These case studies highlight the advantages AI offers CV operations, including increased productivity, reduced expenses, and better service.

The study then explores the major global trends of AI in the CV industry, including work order automation, prognostics, emotional intelligence, and autonomous driving. While emotional intelligence improves user-vehicle connections and makes cars safer and more proactive, autonomous driving technology is predicted to transform transportation by decreasing human intervention and boosting efficiency. Work order automation improves overall efficiency by streamlining operations and decreasing administrative burdens, while prognostics-the capacity to anticipate vehicle breakdowns before they happen-helps businesses save maintenance costs.

With an emphasis on the major business models propelling AI adoption, the study also discusses the competitive landscape in the AI-driven CV space. The primary business models for the CV industry to acquire revenue traction are hardware-integrated solutions, software-as-a-service (SaaS) models, and subscription-based services. In addition, the business models are dissected ecosystem- and fleet-operation-wise, and an AI-based revenue estimate for the entire CV industry is calculated. Furthermore, the study compares global regions using criteria that have a significant impact on the regional development of AI and important areas of AI's rapid expansion in the CV industry.

The study concludes by highlighting several significant potential prospects in the AI-driven CV space. As AI develops, it will play a crucial role in fostering innovation and expansion in the CV industry and assisting businesses in streamlining processes, cutting expenses, and maintaining their competitiveness in a world that is becoming increasingly automated. By adopting AI, the CV industry can open new growth prospects and revolutionize the international transportation of products and services.

Scope

Market Dynamics
- Analysis of current trends and market forces impacting the commercial vehicle sector.
Technology Trends
- Examination of advancements in AI technologies relevant to commercial vehicles.
Competitive Landscape
- Overview of key players and their strategic initiatives.

The Impact of the Top 3 Strategic Imperatives of AI in the CV Industry

Transformative Megatrends

Why: From vehicle design and manufacturing to sales, operations, and safety, AI is revolutionizing the CV industry and generating value throughout the ecosystem. AI is essential for the seamless integration of electric and autonomous vehicles, leading to a shift that drives the sector toward sustainable efficiency.
Frost Perspective: AI will play a key role in major transformative trends across the entire life cycle of CVs. With exponential data generation, AI is crucial for enhancing efficiency and establishing standardization across all operations.

Disruptive Technologies

Why: The CV industry is being disrupted by faster, more efficient, and aesthetically pleasing vehicle designs enabled by AI, facilitating the production of personalized and efficient automobiles. Telematics and logistics are key areas where AI is driving rapid disruption by integrating prognostics and freight visibility, respectively, altering market dynamics and the interaction between fleet operators and their fleets.
Frost Perspective: AI will be pivotal in disrupting conventional vehicle management and operational practices. Industry demands are continually escalating, characterized by shorter delivery times, new and efficient designs, safer vehicles, and personalized cabin experiences. AI will be vital in addressing these demands. In addition, maintenance and telematics are experiencing AI disruptions, where it is being leveraged to minimize total operational costs.

Customer Value Chain Compression

Why: In CVs, AI shortens the customer value chain by simultaneously reducing maintenance costs and enhancing operational efficiency. In-cabin AI features improve the overall driving experience with voice assistants and ADAS, while also reducing accidents, leading to increased profitability.
Frost Perspective: With millions of terabytes (TB) of data generated every second, AI, with its high processing power, cuts through multiple layers of conventional decision-making with faster and more efficient decisions, generating value. AI is and will continue to actively contribute to value chain compression by minimizing total operational costs through increased operational efficiency.

Competitive Environment

Number of Competitors
- >25
Competitive Factors
- Technology, accuracy, partnerships, cost, performance, support, reliability, ease of integration, customer relationships
Key End-user Industry Verticals
- CVs, passenger vehicles (PVs), two wheelers (2Ws)
Leading Competitors
- Amazon, Apple, Baidu, Nvidia, Meta, Microsoft, IBM, Uber
Other Notable Competitors
- Adobe, Dell, Intel, AMD, Salesforce
Distribution Structure
- Technology companies, data science companies, OEMs, fleet managers
Notable Mergers and Acquisitions
- Microsoft acquired OpenAI; Google acquired Waymo