自動駕駛長途卡車市場：商業機會、成長要素、產業趨勢分析及2026-2035年預測

2025 年全球自動駕駛長途卡車市場價值 32 億美元，預計到 2035 年將達到 331 億美元，年複合成長率為 26.9%。

隨著物流營運商將效率、安全性和成本最佳化置於貨運營運的首要位置，自動駕駛長途卡車產業正在迅速發展。市場收入主要來自自動駕駛專用組件和服務，包括感測器技術、車載運算系統、冗餘控制機制、軟體授權、空中升級、整合服務、遠端操作、維護和數據分析，但不包括車輛的基本價格。隨著L4級自動駕駛技術從監督部署發展到在指定路線上完全自動駕駛，預計未來五年下半葉市場成長將進一步加速。籌資策略正從試點部署轉向將標準化產品和服務整合到製造商的產品線中。監管框架和檢驗體係正在推動商業化進程，使車隊營運商能夠加強安全規程和營運準備。技術創新、系統化的部署策略以及不斷增強的行業信心正在塑造市場，推動其在全球物流網路中持續擴張。

營運部署策略著重於透過系統化的路線規劃和高運轉率模式最佳化長途貨運。車隊營運商優先考慮物流樞紐之間的特定距離範圍，在這些範圍內持續營運可顯著提高資產生產力並縮短週轉時間。市場擴張分階段進行，部署進度取決於基礎設施建設和監管合規情況。這種有針對性的方法能夠實現可控的擴充性，並在主要貨運路線上更有效率地部署自動駕駛卡車系統。

L3級自動駕駛系統佔89.4%的市場佔有率，預計到2025年市場規模將達到28億美元。這一主導地位反映了當前監管要求，即在保持人工監督的同時，允許實現高級自動化。 L3級系統超越了基本的駕駛輔助功能，融合了更先進的操作能力，在提高效率和安全性的同時，也確保了駕駛者的參與。系統功能和檢驗流程的不斷進步降低了採用門檻，並增強了車隊營運商的信心。

預計2026年至2035年間，樞紐間運輸市場將以27.8%的複合年成長率成長。這種運作模式與目前自動駕駛系統的能力高度契合，因為它專注於路況相對可預測的長途運輸路線。這些營運的結構化特性有助於高效率的物流協調，使企業能夠簡化指定中轉點之間的貨物運輸。這種方法形成了混合營運框架：自動駕駛系統負責長途運輸路線的管理，而人工干預則支援本地化的配送營運。

美國自動駕駛長途卡車市場預計到2025年將達到11億美元，並在2026年至2035年間以27.4%的複合年成長率成長。憑藉其先進的物流生態系統、完善的交通基礎設施以及強大的技術開發商和製造商網路，美國仍然是創新和應用的關鍵中心。大規模的貨運需求和法規結構的不斷改進正在推動試點項目和商業化進程。結構化運輸模式的採用進一步加速了自動駕駛卡車技術融入主流物流營運的進程。

目錄

第1章：調查方法

第2章執行摘要

第3章 行業洞察

• 產業生態系分析
  • 供應商情況
  • 利潤率
  • 成本結構
  • 每個階段增加的價值
  • 影響價值鏈的因素
  • 中斷
• 影響產業的因素
  • 促進因素
    • 促進要素短缺危機和人事費用上升
    • 全天候貨運服務和資產利用需求
    • 提高安全性和減少事故的潛力
    • 提高燃油效率並降低營運成本
  • 產業潛在風險與挑戰
    • 高昂的初始技術成本與車輛引進成本
    • 關於公共安全的擔憂和接受障礙
  • 市場機遇
    • 政府對智慧道路的基礎建設投資
    • 與倉庫自動化和最後一公里解決方案整合
    • 對現有卡車車隊進行自動駕駛系統改造
• 技術與創新展望
  • 最新技術
    • 基於LiDAR的感知系統
    • 雷達和攝影機感測器融合系統
    • 高清（HD）映射技術
  • 新興技術
    • 車聯網（V2X）協作通訊系統
    • 用於貨物編隊運輸的 5G/6G 超低延遲連接
    • 基於人工智慧的端到端營運平台模型
• 成長潛力分析
• 監理情勢
  • 北美洲
    • 美國 - 聯邦汽車運輸安全管理局 (FMCSA)
    • 美國 - 聯邦汽車運輸安全管理局 (FMCSA)
    • 加拿大 - 加拿大運輸部
  • 歐洲
    • 歐盟交通運輸總司（DG MOVE）
    • 德國 - 聯邦機動車輛管理局 (KBA)
  • 亞太地區
    • 中國 - 工業及資訊化部（工信部）
    • 韓國國土交通部
  • 拉丁美洲
    • 巴西 - 國家陸上運輸管理局 (ANTT)
    • 墨西哥 - 基礎設施、通訊和運輸部(SICT)
  • 中東和非洲
    • 阿拉伯聯合大公國 - 道路和交通管理局 (RTA)
    • 沙烏地阿拉伯 - 交通運輸總局 (TGA)
• 波特的分析
• PESTLE分析
• 專利趨勢
• 貿易數據分析
  • 進出口量及進口額趨勢
  • 主要貿易路線及關稅的影響
• 生產能力和生產情況
  • 生產能力：按地區和主要生產商分類
  • 運轉率和擴張計劃
• 成本細分分析
  • 車輛硬體和感測器套件的成本
  • 自主軟體開發和許可成本
  • 連接和通訊基礎設施成本
  • 車輛運作、遠端監控和維護成本
• 基礎設施發展現狀與智慧道路網路
  • V2I通訊系統
  • 5G和專用短程通訊（DSRC）的部署
  • 智慧高速公路先導計畫和自動駕駛專用車道
  • 自動駕駛電動卡車的充電和氫氣基礎設施
  • 高精度測繪覆蓋範圍和更新信息
• 人工智慧和生成式人工智慧對市場的影響
  • 利用人工智慧改造現有經營模式
  • 按細分市場分類的生成式人工智慧用例和部署藍圖
  • 風險、限制和監管考量
• 預測假設和情境分析
  • 基本案例：驅動複合年成長率的關鍵宏觀經濟與產業變量
  • 樂觀情境：宏觀經濟與產業的順風
  • 悲觀情景：宏觀經濟放緩或產業逆風

第4章 競爭情勢

• 介紹
• 企業市佔率分析
  • 北美洲
  • 歐洲
  • 亞太地區
  • LATAM
  • 中東和非洲
• 主要市場公司的競爭分析
• 競爭定位矩陣
• 主要進展
  • 併購
  • 夥伴關係和聯盟
  • 新產品發布
  • 業務拓展計劃及資金籌措
• 按公司規模進行基準測試
  • 排名分類標準與遴選標準
  • 按銷售額、地區和創新能力分類的層級定位矩陣。

第5章 市場估算與預測：依自動化程度分類，2022-2035年

• 3級
• 4級
• 5級

第6章 市場估計與預測：依促進因素分類，2022-2035年

• 柴油引擎
• 電的
• 混合

第7章 市場估價與預測：依車輛類型分類，2022-2035年

• 7級（總重26,001-33,000磅）
• 8 類（總重 33,001 英鎊或以上）

第8章 市場估計與預測：依應用領域分類，2022-2035年

• 樞紐間營運
• 長途貨物運輸
• 港口和碼頭物流
• 跨境物流
• 其他

第9章 市場估計與預測：依最終用途分類，2022-2035年

• 物流和車輛營運商
• 零售與電子商務
• 快速消費品和食品供應鏈
• 工業產品供應商
• 其他

第10章 市場估價與預測：依地區分類，2022-2035年

• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 德國
  • 英國
  • 法國
  • 義大利
  • 西班牙
  • 俄羅斯
  • 荷蘭
  • 挪威
  • 瑞典
• 亞太地區
  • 中國
  • 日本
  • 韓國
  • 印度
  • 澳洲
  • 新加坡
  • 越南
  • 馬來西亞
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
  • 智利
• 中東和非洲
  • 南非
  • 沙烏地阿拉伯
  • UAE

第11章：公司簡介

• 世界公司
  • Aurora Innovation
  • Waymo（Alphabet）
  • Kodiak Robotics
  • Daimler Truck（Torc Robotics）
  • Volvo Autonomous Solutions
  • CreateAI
  • Plus.ai
  • Applied Intuition
  • Stack AV
  • Einride
  • Waabi Innovation
  • Continental
• 當地公司
  • Inceptio Technology
  • Pony AI
  • TRATON
  • Hino Motors
  • Gatik AI
• 新興企業
  • Outrider Technologies
  • Minus Zero
  • FERNRIDE

The Global Autonomous Long-Haul Trucking Market was valued at USD 3.2 billion in 2025 and is estimated to grow at a CAGR of 26.9% to reach USD 33.1 billion by 2035.

The autonomous long-haul trucking industry is evolving rapidly as logistics providers prioritize efficiency, safety, and cost optimization across freight operations. Market revenue is generated through autonomy-focused components and services, including sensor technologies, onboard computing systems, redundant control mechanisms, software licensing, over-the-air upgrades, integration services, remote operations, maintenance, and data analytics, while excluding the base vehicle cost. Growth momentum is expected to accelerate toward the latter part of the decade as Level 4 autonomy progresses from supervised deployment to fully driverless operation within defined routes. Procurement strategies are shifting from pilot-based deployments to standardized offerings integrated into manufacturer portfolios. Regulatory development and validation frameworks are supporting commercialization efforts, enabling fleet operators to advance safety protocols and operational readiness. The combination of technological innovation, structured deployment strategies, and increasing industry confidence is positioning the market for sustained expansion across global logistics networks.

Operational deployment strategies are focused on optimizing long-distance freight movement through structured route planning and high-utilization models. Fleet operators are prioritizing specific distance ranges between logistics nodes, where continuous operation significantly improves asset productivity and reduces turnaround times. Market expansion is occurring in a phased manner, with deployment determined by infrastructure readiness and regulatory alignment. This targeted approach is enabling controlled scalability and more efficient implementation of autonomous trucking systems across key freight corridors.

The Level 3 segment held an 89.4% share, generating USD 2.8 billion in 2025. This dominance reflects current regulatory requirements that maintain human oversight while allowing advanced automation features. Level 3 systems have progressed beyond basic driver assistance by incorporating enhanced operational capabilities that improve efficiency and safety while maintaining driver involvement. Continued advancements in system functionality and validation processes are reducing adoption barriers and strengthening confidence among fleet operators.

The hub-to-hub operations segment is projected to grow with a CAGR of 27.8% between 2026 and 2035. This operating model aligns well with the current capabilities of autonomous systems, as it focuses on long-distance routes with relatively predictable driving conditions. The structured nature of these operations supports efficient logistics coordination, allowing companies to streamline freight movement between designated transfer points. This approach enables a hybrid operational framework, where autonomous systems manage extended routes while human intervention supports localized distribution tasks.

U.S. Autonomous Long-Haul Trucking Market reached USD 1.1 billion in 2025 and is expected to grow at a CAGR of 27.4% from 2026 to 2035. The country remains a key hub for innovation and deployment due to its advanced logistics ecosystem, extensive transportation infrastructure, and strong presence of technology developers and manufacturers. Large-scale freight demand and ongoing advancements in regulatory frameworks are supporting continued testing and commercialization efforts. The adoption of structured transport models is further facilitating the integration of autonomous trucking technologies into mainstream logistics operations.

Key companies operating in the Global Autonomous Long-Haul Trucking Market include Kodiak Robotics, Aurora Innovation, Applied Intuition, Continental, Daimler Truck (Torc Robotics), Einride, Hino Motors, Plus.ai, Pony AI, and TRATON. Companies in the autonomous long-haul trucking market are adopting a range of strategic initiatives to strengthen their market position. Firms are heavily investing in research and development to enhance autonomy capabilities, improve system reliability, and accelerate the transition toward higher levels of automation. Strategic collaborations with logistics providers and technology partners are being leveraged to expand deployment opportunities and validate real-world performance. Companies are also focusing on scalable business models, including software-based revenue streams and service-oriented offerings. Expansion into key logistics corridors and infrastructure partnerships is enabling more efficient deployment.

Table of Contents

Chapter 1 Methodology

• 1.1 Research approach
• 1.2 Quality Commitments
  • 1.2.1 GMI AI policy & data integrity commitment
• 1.3 Research Trail & Confidence Scoring
  • 1.3.1 Research Trail Components
  • 1.3.2 Scoring Components
• 1.4 Data Collection
• 1.5 Data mining sources
  • 1.5.1 Paid sources
• 1.6 Base estimates and calculations
  • 1.6.1 Base year calculation for any one approach
• 1.7 Forecast
  • 1.7.1 Quantified market impact analysis
• 1.8 Research transparency addendum
  • 1.8.1 Source attribution framework
  • 1.8.2 Quality assurance metrics
  • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Automation Level
  • 2.2.3 Propulsion
  • 2.2.4 Vehicle Class
  • 2.2.5 Application
  • 2.2.6 End-Use
• 2.3 TAM analysis, 2026-2035
• 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factor affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Driver Shortage Crisis & Rising Labor Costs
    • 3.2.1.2 Demand for 24/7 Freight Operations & Asset Utilization
    • 3.2.1.3 Safety Improvements & Accident Reduction Potential
    • 3.2.1.4 Fuel Efficiency Gains & Operating Cost Reduction
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 High Initial Technology & Vehicle Acquisition Costs
    • 3.2.2.2 Public Safety Concerns & Acceptance Barriers
  • 3.2.3 Market opportunities
    • 3.2.3.1 Government Infrastructure Investments in Smart Roads
    • 3.2.3.2 Integration with Warehouse Automation & Last-Mile Solutions
    • 3.2.3.3 Retrofit Autonomous Systems for Existing Truck Fleets
• 3.3 Technology and innovation landscape
  • 3.3.1 Current technologies
    • 3.3.1.1 LiDAR-based Perception Systems
    • 3.3.1.2 Radar and Camera Sensor Fusion Systems
    • 3.3.1.3 High-Definition (HD) Mapping Technology
  • 3.3.2 Emerging technologies
    • 3.3.2.1 Vehicle-to-Everything (V2X) Cooperative Communication Systems
    • 3.3.2.2 5G/6G Ultra-Low Latency Connectivity for Freight Platooning
    • 3.3.2.3 AI-Based End-to-End Driving Foundation Models
• 3.4 Growth potential analysis
• 3.5 Regulatory landscape
  • 3.5.1 North America
    • 3.5.1.1 US - Federal Motor Carrier Safety Administration (FMCSA)
    • 3.5.1.2 US - Federal Motor Carrier Safety Administration (FMCSA)
    • 3.5.1.3 Canada - Transport Canada
  • 3.5.2 Europe
    • 3.5.2.1 EU - Directorate-General for Mobility and Transport (DG MOVE)
    • 3.5.2.2 Germany - Kraftfahrt-Bundesamt (KBA)
  • 3.5.3 Asia Pacific
    • 3.5.3.1 China - Ministry of Industry and Information Technology (MIIT)
    • 3.5.3.2 South Korea - Ministry of Land, Infrastructure and Transport (MOLIT)
  • 3.5.4 Latin America
    • 3.5.4.1 Brazil - Agencia Nacional de Transportes Terrestres (ANTT)
    • 3.5.4.2 Mexico - Secretaria de Infraestructura, Comunicaciones y Transportes (SICT)
  • 3.5.5 Middle East & Africa
    • 3.5.5.1 UAE - Road and Transport Authority (RTA)
    • 3.5.5.2 Saudi Arabia - Transport General Authority (TGA)
• 3.6 Porter's analysis
• 3.7 PESTEL analysis
• 3.8 Patent landscape (Driven by Primary Research)
• 3.9 Trade Data Analysis (Based on Paid Database)
  • 3.9.1 Import/Export Volume & Value Trends
  • 3.9.2 Key Trade Corridors & Tariff Impact
• 3.10 Capacity & Production Landscape (Driven by Primary Research)
  • 3.10.1 Production Capacity by Region & Key Producer
  • 3.10.2 Capacity Utilization Rates & Expansion Pipelines
• 3.11 Cost breakdown analysis
  • 3.11.1 Vehicle hardware & sensor suite costs
  • 3.11.2 Autonomous software development and licensing costs
  • 3.11.3 Connectivity and communication infrastructure costs
  • 3.11.4 Fleet operations, remote monitoring, and maintenance costs
• 3.12 Infrastructure Readiness and Smart Road Networks
  • 3.12.1 V2I Communication Systems
  • 3.12.2 5G & Dedicated Short-Range Communications (DSRC) Deployment
  • 3.12.3 Smart Highway Pilot Projects & Dedicated Autonomous Lanes
  • 3.12.4 Charging & Fueling Infrastructure for Autonomous Electric Trucks
  • 3.12.5 High-Definition Mapping Coverage & Updates
• 3.13 Impact of AI & Generative AI on the Market (Driven by Primary Research)
  • 3.13.1 AI-Driven Disruption of Existing Business Models
  • 3.13.2 GenAI Use Cases & Adoption Roadmap by Segment
  • 3.13.3 Risks, Limitations & Regulatory Considerations
• 3.14 Forecast assumptions & scenario analysis (Driven by Primary Research)
  • 3.14.1 Base Case - key macro & industry variables driving CAGR
  • 3.14.2 Optimistic Scenarios - Favorable macro and industry tailwinds
  • 3.14.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

• 4.1 Introduction
• 4.2 Company market share analysis
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 LATAM
  • 4.2.5 MEA
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Key developments
  • 4.5.1 Mergers & acquisitions
  • 4.5.2 Partnerships & collaborations
  • 4.5.3 New product launches
  • 4.5.4 Expansion plans and funding
• 4.6 Company tier benchmarking
  • 4.6.1 Tier classification criteria & qualifying thresholds
  • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates and Forecast, By Automation Level, 2022 - 2035 ($ Mn, Units)

• 5.1 Key trends
• 5.2 Level 3
• 5.3 Level 4
• 5.4 Level 5

Chapter 6 Market Estimates and Forecast, By Propulsion, 2022 - 2035 ($ Mn, Units)

• 6.1 Key trends
• 6.2 Diesel
• 6.3 Electric
• 6.4 Hybrid

Chapter 7 Market Estimates and Forecast, By Vehicle Class, 2022 - 2035 ($ Mn, Units)

• 7.1 Key trends
• 7.2 Class 7 (26,001-33,000 lbs GVWR)
• 7.3 Class 8 (33,001+ lbs GVWR)

Chapter 8 Market Estimates and Forecast, By Application, 2022 - 2035 ($ Mn, Units)

• 8.1 Key trends
• 8.2 Hub-to-Hub Operations
• 8.3 Long-Distance Freight Transport
• 8.4 Port & Terminal Logistics
• 8.5 Cross-Border Logistics
• 8.6 Others

Chapter 9 Market Estimates and Forecast, By End Use, 2022 - 2035 ($ Mn, Units)

• 9.1 Key trends
• 9.2 Logistics & Fleet Operators
• 9.3 Retail & E-Commerce
• 9.4 FMCG & Food Supply Chains
• 9.5 Industrial Goods Suppliers
• 9.6 Others

Chapter 10 Market Estimates & Forecast, By Region, 2022 - 2035 ($Mn, Units)

• 10.1 Key trends
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 France
  • 10.3.4 Italy
  • 10.3.5 Spain
  • 10.3.6 Russia
  • 10.3.7 Netherlands
  • 10.3.8 Norway
  • 10.3.9 Sweden
• 10.4 Asia Pacific
  • 10.4.1 China
  • 10.4.2 Japan
  • 10.4.3 South Korea
  • 10.4.4 India
  • 10.4.5 Australia
  • 10.4.6 Singapore
  • 10.4.7 Vietnam
  • 10.4.8 Malaysia
• 10.5 Latin America
  • 10.5.1 Brazil
  • 10.5.2 Mexico
  • 10.5.3 Argentina
  • 10.5.4 Chile
• 10.6 MEA
  • 10.6.1 South Africa
  • 10.6.2 Saudi Arabia
  • 10.6.3 UAE

Chapter 11 Company Profiles

• 11.1 Global players
  • 11.1.1 Aurora Innovation
  • 11.1.2 Waymo (Alphabet)
  • 11.1.3 Kodiak Robotics
  • 11.1.4 Daimler Truck (Torc Robotics)
  • 11.1.5 Volvo Autonomous Solutions
  • 11.1.6 CreateAI
  • 11.1.7 Plus.ai
  • 11.1.8 Applied Intuition
  • 11.1.9 Stack AV
  • 11.1.10 Einride
  • 11.1.11 Waabi Innovation
  • 11.1.12 Continental
• 11.2 Regional players
  • 11.2.1 Inceptio Technology
  • 11.2.2 Pony AI
  • 11.2.3 TRATON
  • 11.2.4 Hino Motors
  • 11.2.5 Gatik AI
• 11.3 Emerging players
  • 11.3.1 Outrider Technologies
  • 11.3.2 Minus Zero
  • 11.3.3 FERNRIDE