自动驾驶长途卡车市场机会、成长驱动因素、产业趋势分析及预测（2025-2034年）

2024 年全球自动驾驶长途卡车市场价值 27 亿美元，预计到 2034 年将以 32% 的复合年增长率增长至 426 亿美元。

随着L4级自动驾驶技术逐步投入商业应用，各大货运网路正加速普及。人工智慧的进步、货运量的成长以及持续存在的司机短缺问题，正推动自动驾驶卡车走向大规模部署。预计从2024年到2034年，市场规模将成长近20至25倍，主要得益于持续自动驾驶带来的营运成本节省，最高可达40%。美国政府的监管支持也促进了市场发展，联邦交通部门为安全部署自动化货运业务提供了更清晰的路径。多个州在自动驾驶技术研发方面发挥核心作用，强化了向长途自动驾驶运输的转型，并使物流公司能够在长途运输中更加依赖自动化系统。核心营运模式日益围绕连接主要货运枢纽展开，旨在提高效率、减少停机时间并优化长途运输调度。这些因素共同表明，自动驾驶卡车正迅速融入国家供应链的各个环节。

2024年，柴油动力总成市占率达到69%，预计2025年至2034年将以32.2%的复合年增长率成长。柴油动力之所以持续领先，是因为它拥有成熟的基础设施、高能量密度以及相对于新兴替代能源更低的初始成本。与柴油平台整合的自动驾驶系统可以在无需车队转型使用陌生的推进技术的情况下，提高生产效率。儘管柴油动力仍然是长途运输的首选，但加州和欧洲部分地区日益严格的排放法规给行业带来了监管压力，该行业必须应对这些压力。

2024年，7级卡车市占率达到86%，预计2025年至2034年间将以32.6%的复合年增长率成长。此重量等级涵盖广泛用于货运、城市物流和工业收集服务的重型车辆。 7级卡车在自动驾驶应用领域保持领先地位，因为它们的运作模式与结构化的枢纽导向路线相契合。该细分市场受益于实用的载货能力和更灵活的合规要求。不断扩展的自动驾驶运输网路、更广泛的自动驾驶系统整合以及与原始设备製造商更深入的合作，都进一步推动了其成长。

美国自动驾驶长途卡车市场预计将在2025年至2034年间持续成长。由于强劲的货运量、先进的高速公路自动化项目以及Aurora Innovation和Kodiak Robotics等公司不断推出的创新技术，美国仍然是全球需求的最大贡献者。全美各地的承运商和物流供应商都在投资自动化货运技术，以提高可靠性、降低营运成本并改善关键枢纽之间的运输效率。人工智慧驱动的工具，包括车队分析、远端支援功能和预测性服务，正成为该生态系统不可或缺的一部分。

目录

第一章：方法论

第二章：执行概要

第三章：行业洞察

• 产业生态系分析
  • 供应商格局
  • 利润率分析
  • 成本结构
  • 每个阶段的价值增加
  • 影响价值链的因素
  • 中断
• 产业影响因素
  • 成长驱动因素
    • 卡车司机严重短缺
    • 需要降低运输成本
    • 全天候不间断运营
    • 人工智慧、感测器和高速公路自动化技术的进步
    • 扩大受控的枢纽间货运网络
  • 产业陷阱与挑战
    • 高昂的资本和技术成本
    • 各地区的监管不确定性
  • 市场机会
    • 为大型零售商和第三方物流公司提供自主货运服务
    • 与电动和氢燃料长途卡车的整合
    • 远端营运中心（ROC）和远端驾驶服务
    • 亚太地区的高成长市场
• 成长潜力分析
• 监管环境
  • 美国联邦框架（NHTSA、FMCSA、DOT）
  • 美国州级立法和许可（34个州+哥伦比亚特区）
  • 联合国欧洲经济委员会工作小组29和GRVA国际协调
  • 联合国法规（R155 网路安全、R156 OTA、R157 ALKS）
  • 4-5级系统的服务时间（HOS）影响
  • 检验标准与CVSA强化型商用车检验计划
  • 资料记录、隐私和ISMR报告要求
• 波特的分析
• PESTEL 分析
• 技术与创新格局
  • 当前技术趋势
    • SAE 3-5级自动化能力
    • 感测器融合架构（光达、雷达、摄影机）
    • 感知与规划中的人工智慧与机器学习
    • 冗余和故障安全系统设计
  • 新兴技术
    • V2X 通讯与连网车辆技术
    • 高清地图和定位
    • 网路安全与OTA软体更新管理
• 定价分析
  • 技术成本结构
  • 车辆购置成本溢价
  • 营运成本经济性
  • 旅行即服务 (TaaS) 定价模型与费率结构
  • 成本削减路线图（2024-2034）
• 生产统计
  • 生产中心
  • 消费中心
  • 进出口
• 成本細項分析
  • 总拥有成本 (TCO) 和经济回报分析
  • 自动驾驶卡车与传统卡车的总体拥有成本框架
  • 资本支出分析
  • 营运支出分析
  • 收入和利用率影响
  • 依部署模型进行投资回收期分析
• 专利分析
  • 按技术领域分類的专利申请趋势（2015-2024 年）
  • 主要专利受让人（原始设备製造商、ADS开发商、供应商）
  • 关键专利集群：感知、规划、控制、冗余
  • 地理专利活动（美国专利商标局、欧洲专利局、中国国家智慧财产局）
• 永续性和环境方面
  • 永续实践
  • 减少废弃物策略
  • 生产中的能源效率
  • 环保倡议
  • 碳足迹考量
• 运行部署模式
  • 枢纽间营运及转运枢纽经济
  • 专用走廊策略
  • 工业及受控环境应用
  • 混合式人机协作车队管理
• 安全与效能基准测试
  • 安全案例框架和验证方法
  • 实际无人驾驶行驶里程（2023-2025 年）
  • 脱离接触与重大事件报告
  • 与人类驾驶员基线性能的比较
• 保险与责任框架演变
  • 目前责任归属挑战
  • 传统商业汽车保险与自动驾驶专用产品
  • 产业试点计画和保险公司合作关係
  • 责任框架中的监管漏洞
• 真实世界绩效资料与离职分析
  • 主要参与者累积的无人驾驶里程
  • 脱离率定义与衡量标准
  • 关键事件分类
  • 天气与环境性能
  • 比较分析：自动驾驶与人类驾驶事故率

第四章：竞争格局

• 介绍
• 公司市占率分析
  • 北美洲
  • 欧洲
  • 亚太地区
  • 拉丁美洲
  • MEA
• 主要市场参与者的竞争分析
• 竞争定位矩阵
• 战略展望矩阵
• 关键进展
  • 併购
  • 合作伙伴关係与合作
  • 新产品发布
  • 扩张计划和资金

第五章：市场估算与预测：以推进方式划分，2021-2034年

• 柴油引擎
• 电的
• 杂交种

第六章：市场估算与预测：依类别划分，2021-2034年

• 7级（26,001-33,000磅）
• 8级（33,001磅以上）

第七章：市场估计与预测：依自主程度划分，2021-2034年

• 3级
• 4级
• 5级

第八章：市场估算与预测：依应用领域划分，2021-2034年

• 长途货运
• 高速公路编队行驶
• 跨境物流
• 枢纽到枢纽的运营
• 港口和码头物流
• 其他的

第九章：市场估算与预测：依最终用途划分，2021-2034年

• 物流公司
• 零售与电子商务
• 快速消费品和食品供应链
• 工业品供应商
• 其他的

第十章：市场估计与预测：依地区划分，2021-2034年

• 北美洲
  • 我们
  • 加拿大
• 欧洲
  • 德国
  • 英国
  • 法国
  • 义大利
  • 西班牙
  • 俄罗斯
  • 北欧
  • 葡萄牙
  • 克罗埃西亚
• 亚太地区
  • 中国
  • 印度
  • 日本
  • 澳洲
  • 韩国
  • 新加坡
  • 泰国
  • 印尼
  • 越南
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
• MEA
  • 南非
  • 沙乌地阿拉伯
  • 阿联酋
  • 土耳其

第十一章：公司简介

• 全球参与者
  • Aurora Innovation
  • Daimler Truck / Freightliner
  • Einride
  • Inceptio Technology
  • Kodiak Robotics
  • Locomation
  • Navistar
  • Paccar
  • Plus (PlusAI)
  • Tesla
  • Torc Robotics
  • TRATON
  • TuSimple
  • Volvo Autonomous Solutions
  • Waymo
• 区域玩家
  • Gatik
  • Voyage / Geely-backed autonomous trucking unit
  • Waabi
• 新兴及小众玩家
  • Applied Intuition
  • Embark Trucks
  • Ike Robotics
  • Outrider
  • Stack AV

The Global Autonomous Long-Haul Trucking Market was valued at USD 2.7 billion in 2024 and is estimated to grow at a CAGR of 32% to reach USD 42.6 billion by 2034.

With Level 4 capabilities progressing into commercial use, adoption is accelerating across major freight networks. Advancements in artificial intelligence, rising cargo movement, and persistent driver shortages are pushing autonomous trucking toward large-scale deployment. From 2024 through 2034, the overall market size is projected to increase by nearly 20 to 25 times, supported by operational savings that can reach as much as 40% due to continuous autonomous operation. Supportive regulatory guidance in the United States is also encouraging market movement, as federal transportation authorities provide clearer pathways for the safe rollout of automated freight operations. Several states remain central to development efforts, reinforcing the shift toward long-distance autonomous transport and enabling logistics companies to rely more heavily on automated systems for extended routes. Core operating models increasingly revolve around connecting major freight hubs to improve efficiency, reduce downtime, and streamline long-haul scheduling. These combined forces indicate that autonomous trucking is moving rapidly toward broader integration across national supply chains.

The diesel powertrain segment held a 69% share in 2024 and is projected to grow at a 32.2% CAGR from 2025 to 2034. Diesel continues to lead because it offers an established infrastructure, high energy density, and lower initial costs relative to emerging alternatives. Autonomous systems integrated with diesel platforms can deliver improved productivity without requiring fleets to transition to unfamiliar propulsion technologies. Although diesel remains the preferred option for long-distance hauling, tightening emissions mandates in regions such as California and parts of Europe introduce regulatory pressures that the industry must navigate.

The Class 7 truck segment held an 86% share in 2024 and is expected to grow at a CAGR of 32.6% between 2025 and 2034. This weight class covers heavy-duty vehicles widely used for freight movement, urban logistics, and industrial collection services. Class 7 models maintain a leading position within autonomous applications because their operational patterns align with structured, hub-oriented routes. This segment benefits from practical load capacity and more flexible compliance requirements. Growth is reinforced by expanding autonomous transport networks, broader integration of self-driving systems, and deeper collaboration with original equipment manufacturers.

United States Autonomous Long-Haul Trucking Market is projected to see sustained expansion from 2025 to 2034. The country remains the largest contributor to global demand due to strong freight volumes, advanced highway-automation initiatives, and ongoing innovations introduced by companies such as Aurora Innovation and Kodiak Robotics. Carriers and logistics providers across the nation are investing in automated freight technologies to raise reliability, cut operating expenses, and improve movement between key hubs. Tools driven by artificial intelligence, including fleet analytics, remote support functions, and predictive servicing, are becoming essential to the ecosystem.

Leading companies in the Global Autonomous Long-Haul Trucking Market include Aurora Innovation, Einride, Inceptio Technology, Kodiak Robotics, Locomation, Plus.ai, Tesla, Torc Robotics, TuSimple, and Waymo. Companies in the Autonomous Long-Haul Trucking Market are reinforcing their positions by expanding testing programs, advancing Level 4 software, and increasing investment in purpose-built platforms. Many firms are partnering with freight carriers, fleet operators, and truck manufacturers to accelerate integration and secure long-term commercial pathways. To strengthen competitiveness, businesses are improving sensor technology, refining AI-driven perception, and enhancing safety-critical redundancies. Several organizations are also building scalable operational centers to manage autonomous fleets and support remote oversight. Cost efficiency, reliability, and regulatory compliance remain top priorities, driving continuous improvements in system performance.

Table of Contents

Chapter 1 Methodology

• 1.1 Market scope and definition
• 1.2 Research design
  • 1.2.1 Research approach
  • 1.2.2 Data collection methods
• 1.3 Data mining sources
  • 1.3.1 Global
  • 1.3.2 Regional/Country
• 1.4 Base estimates and calculations
  • 1.4.1 Base year calculation
  • 1.4.2 Key trends for market estimation
• 1.5 Primary research and validation
  • 1.5.1 Primary sources
• 1.6 Forecast model
• 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

• 2.1 Industry 360⁰ synopsis, 2021 - 2034
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Propulsion
  • 2.2.3 Class
  • 2.2.4 Autonomy Level
  • 2.2.5 Application
  • 2.2.6 End Use
• 2.3 TAM Analysis, 2025-2034
• 2.4 CXO perspectives: Strategic imperatives
  • 2.4.1 Executive decision points
  • 2.4.2 Critical success factors
• 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 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 Severe truck driver shortage
    • 3.2.1.2 Need for lower transportation costs
    • 3.2.1.3 24/7 continuous operations
    • 3.2.1.4 Advancements in AI, sensors, and highway automation
    • 3.2.1.5 Expansion of controlled hub-to-hub freight networks
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 High capital and technology costs
    • 3.2.2.2 Regulatory uncertainty across regions
  • 3.2.3 Market opportunities
    • 3.2.3.1 Autonomous freight services for major retailers and 3PLs
    • 3.2.3.2 Integration with electric and hydrogen long-haul trucks
    • 3.2.3.3 Remote operations centers (ROC) and tele-driving services
    • 3.2.3.4 High-growth markets in Asia-Pacific
• 3.3 Growth potential analysis
• 3.4 Regulatory landscape
  • 3.4.1 United States federal framework (NHTSA, FMCSA, DOT)
  • 3.4.2 US state-level legislation & permitting (34 states + DC)
  • 3.4.3 UNECE WP.29 & GRVA international harmonization
  • 3.4.4 UN regulations (R155 cybersecurity, R156 OTA, R157 ALKS)
  • 3.4.5 Hours-of-service (HOS) implications for Level 4-5 systems
  • 3.4.6 Inspection standards & CVSA enhanced CMV inspection program
  • 3.4.7 Data recording, privacy & ISMR reporting requirements
• 3.5 Porter's analysis
• 3.6 PESTEL analysis
• 3.7 Technology and innovation landscape
  • 3.7.1 Current technological trends
    • 3.7.1.1 SAE levels 3-5 automation capabilities
    • 3.7.1.2 Sensor fusion architectures (LiDAR, radar, camera)
    • 3.7.1.3 AI & machine learning in perception & planning
    • 3.7.1.4 Redundancy & fail-safe system design
  • 3.7.2 Emerging technologies
    • 3.7.2.1 V2X communication & connected vehicle technologies
    • 3.7.2.2 HD mapping & localization
    • 3.7.2.3 Cybersecurity & OTA software update management
• 3.8 Pricing analysis
  • 3.8.1 Technology cost structure
  • 3.8.2 Vehicle acquisition cost premium
  • 3.8.3 Operating cost economics
  • 3.8.4 TaaS pricing models & rate structures
  • 3.8.5 Cost reduction roadmap (2024-2034)
• 3.9 Production statistics
  • 3.9.1 Production hubs
  • 3.9.2 Consumption hubs
  • 3.9.3 Export and import
• 3.10 Cost breakdown analysis
  • 3.10.1 Total cost of ownership (TCO) & economic payback analysis
  • 3.10.2 TCO framework for autonomous trucks vs conventional trucks
  • 3.10.3 Capital expenditure analysis
  • 3.10.4 Operating expenditure analysis
  • 3.10.5 Revenue & utilization impacts
  • 3.10.6 Payback period analysis by deployment model
• 3.11 Patent analysis
  • 3.11.1 Patent filing trends by technology domain (2015-2024)
  • 3.11.2 Leading patent assignees (OEMs, ADS developers, suppliers)
  • 3.11.3 Key patent clusters: perception, planning, control, redundancy
  • 3.11.4 Geographic patent activity (USPTO, EPO, CNIPA)
• 3.12 Sustainability and environmental aspects
  • 3.12.1 Sustainable practices
  • 3.12.2 Waste reduction strategies
  • 3.12.3 Energy efficiency in production
  • 3.12.4 Eco-friendly Initiatives
  • 3.12.5 Carbon footprint considerations
• 3.13 Operational deployment models
  • 3.13.1 Hub-to-hub operations & transfer hub economics
  • 3.13.2 Dedicated corridor strategies
  • 3.13.3 Industrial & controlled environment applications
  • 3.13.4 Hybrid human-autonomous fleet management
• 3.14 Safety & performance benchmarking
  • 3.14.1 Safety case frameworks & validation methods
  • 3.14.2 Real-world driverless miles achieved (2023-2025)
  • 3.14.3 Disengagement & critical event reporting
  • 3.14.4 Comparison to human driver baseline performance
• 3.15 Insurance & liability framework evolution
  • 3.15.1 Current liability attribution challenges
  • 3.15.2 Traditional commercial auto insurance vs autonomous-specific products
  • 3.15.3 Industry pilot programs & insurer partnerships
  • 3.15.4 Regulatory gaps in liability frameworks
• 3.16 Real-world performance data & disengagement analytics
  • 3.16.1 Driverless miles accumulated by key players
  • 3.16.2 Disengagement rate definitions & measurement standards
  • 3.16.3 Critical event taxonomy
  • 3.16.4 Weather & environmental performance
  • 3.16.5 Comparative analysis: autonomous vs human driver incident rates

Chapter 4 Competitive Landscape, 2024

• 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 Strategic outlook matrix
• 4.6 Key developments
  • 4.6.1 Mergers & acquisitions
  • 4.6.2 Partnerships & collaborations
  • 4.6.3 New Product Launches
  • 4.6.4 Expansion Plans and funding

Chapter 5 Market Estimates & Forecast, By Propulsion, 2021 - 2034 (USD Mn, Units)

• 5.1 Key trends
• 5.2 Diesel
• 5.3 Electric
• 5.4 Hybrid

Chapter 6 Market Estimates & Forecast, By Class, 2021 - 2034 (USD Mn, Units)

• 6.1 Key trends
• 6.2 Class 7 (26,001-33,000 lbs)
• 6.3 Class 8 (33,001+ lbs)

Chapter 7 Market Estimates & Forecast, By Autonomy Level, 2021 - 2034 (USD Mn, Units)

• 7.1 Key trends
• 7.2 Level 3
• 7.3 Level 4
• 7.4 Level 5

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2034 (USD Mn, Units)

• 8.1 Key trends
• 8.2 Long-distance freight transport
• 8.3 Highway platooning
• 8.4 Cross-border logistics
• 8.5 Hub-to-hub operations
• 8.6 Port and terminal logistics
• 8.7 Others

Chapter 9 Market Estimates & Forecast, By End Use, 2021 - 2034 (USD Mn, Units)

• 9.1 Key trends
• 9.2 Logistics companies
• 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, 2021 - 2034 (USD 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 Nordics
  • 10.3.8 Portugal
  • 10.3.9 Croatia
• 10.4 Asia Pacific
  • 10.4.1 China
  • 10.4.2 India
  • 10.4.3 Japan
  • 10.4.4 Australia
  • 10.4.5 South Korea
  • 10.4.6 Singapore
  • 10.4.7 Thailand
  • 10.4.8 Indonesia
  • 10.4.9 Vietnam
• 10.5 Latin America
  • 10.5.1 Brazil
  • 10.5.2 Mexico
  • 10.5.3 Argentina
• 10.6 MEA
  • 10.6.1 South Africa
  • 10.6.2 Saudi Arabia
  • 10.6.3 UAE
  • 10.6.4 Turkey

Chapter 11 Company Profiles

• 11.1 Global Players
  • 11.1.1 Aurora Innovation
  • 11.1.2 Daimler Truck / Freightliner
  • 11.1.3 Einride
  • 11.1.4 Inceptio Technology
  • 11.1.5 Kodiak Robotics
  • 11.1.6 Locomation
  • 11.1.7 Navistar
  • 11.1.8 Paccar
  • 11.1.9 Plus (PlusAI)
  • 11.1.10 Tesla
  • 11.1.11 Torc Robotics
  • 11.1.12 TRATON
  • 11.1.13 TuSimple
  • 11.1.14 Volvo Autonomous Solutions
  • 11.1.15 Waymo
• 11.2 Regional Players
  • 11.2.1 Gatik
  • 11.2.2 Voyage / Geely-backed autonomous trucking unit
  • 11.2.3 Waabi
• 11.3 Emerging & Niche Players
  • 11.3.1 Applied Intuition
  • 11.3.2 Embark Trucks
  • 11.3.3 Ike Robotics
  • 11.3.4 Outrider
  • 11.3.5 Stack AV