Authors:
Preeti Wadhwani, Manish Verma
Download free PDF
Autonomous Long-Haul Trucking Market Size & Share 2026-2035
Report ID: GMI15396
|
Published Date: August 2026
|
Report Format: PDF/Excel/Dashboard/Platform
Download Free PDF
Explore Our Licensing Options:
Download Free PDF
Autonomous Long-Haul Trucking Market
Get a free sample of this report
What are you hoping to find?
Your PDF is on its way. Tell us little about your research goal, and we'll help you find the most relevant market insights.
Autonomous Long-Haul Trucking Market Size
The autonomous long-haul trucking market was valued at USD 3.2 billion in 2025 and is projected to increase from USD 3.9 billion in 2026 to USD 33.1 billion by 2035, expanding at a 26.9% CAGR.
Autonomous Long-Haul Trucking Market Key Takeaways
Market Leader: Continental led with over 25% market share in 2025.
Leading Players: Top 5 players in this market include Applied Intuition, Continental, Daimler Truck (Torc Robotics), Einride, TRATON, which collectively held a market share of 46.5% in 2025.
The market covers SAE Level 3 to Level 5 systems used in commercial freight vehicles for highway-based freight movements, including sensors, onboard computing, HD mapping, V2X communications, software, and operating models.
The near-term revenue base remains concentrated in driver-supervised systems, but the growth profile is shaped by the commercial transition toward defined Level 4 highway operations. Level 3 generated USD 2,844.4 million in 2025, while Level 4 accounted for USD 338.0 million and is forecast to grow faster, at 29.8% annually. Level 5 had no commercial revenue in 2025 because unrestricted operation across all roads, weather conditions, and freight interfaces remains outside current commercial operating domains.
A labor constraint gives the market its most durable demand foundation. Scania reported 3.6 million unfilled driver positions across 36 countries representing 70% of global GDP; 31.6% of drivers were aged 55 or above, while only 6.5% were aged 25 or younger. A further 3.4 million drivers are expected to retire by 2030 [1]SCANIA.COM - Autonomous trucks can help address transport industry's driver shortage issue, 2025. Autonomous systems do not eliminate every role in freight movement, particularly at terminals, docks, and exception-heavy first- and last-mile locations. They can, however, reduce reliance on scarce long-haul driving capacity on repeatable highway corridors.
Electrification changes the economic case without removing the deployment constraint. Electric trucks represented 19.4% of autonomous long-haul trucking revenue in 2025 and are forecast to grow at 30.8% annually. The International Energy Agency reported more than 90,000 electric truck sales globally in 2024, with battery-electric heavy-duty trucks about 55% more energy-efficient than comparable diesel trucks [2]IEA.ORG - Trends in heavy-duty electric vehicles - Global EV Outlook 2025, 2025. The opportunity is strongest where a fleet can pair predictable routes, charging dwell time, and centralized maintenance with autonomous operating domains.
GMI Analyst View
Commercial adoption is likely to proceed corridor by corridor rather than through a uniform replacement of conventional fleets. The value proposition becomes strongest where the same route carries sufficient freight volume to justify a mapped operating domain, remote-support procedures, and vehicle-specific maintenance capability. Driver scarcity creates the economic pressure, but route repeatability and fleet density determine whether that pressure converts into deployments.
The market's growth also reflects a change in what fleets purchase. Early revenue is led by assisted-driving hardware and software installed in conventional operating models. The forecast's acceleration depends on higher-value Level 4 operating systems, fleet integration, monitoring infrastructure, and recurring software and support services. This makes regulatory permission, safety validation, and operating reliability more consequential than the availability of a standalone sensor stack.
Key Drivers
Driver Shortage Crisis & Rising Labor Costs. Long-haul operators face a demographic supply problem rather than a temporary recruitment cycle. The projected retirement of 3.4 million drivers by 2030 increases the value of systems that can assume repeatable highway driving tasks while human labor is redeployed to complex handoffs, customer facilities, and exception management. Large carriers are positioned to adopt first because they can concentrate autonomous assets on high-volume lanes and establish dedicated support operations.
Demand for 24/7 Freight Operations & Asset Utilization. Conventional long-haul utilization is limited by required rest periods and the cost of staffing additional shifts. Defined hub-to-hub operations permit trucks to operate over the most standardized portion of a freight movement, increasing equipment availability without requiring a proportional expansion of driver headcount. This model is most relevant for freight networks with scheduled distribution-center flows, where delayed arrivals affect dock labor, inventory availability, and onward transportation planning.
Safety Improvements & Accident Reduction Potential. The U.S. Federal Motor Carrier Safety Administration's Automated CMV Evaluation program used three Class 8 tractors equipped with SAE Level 3 automation and tested maneuverability, automated lane changes, sensor-failure management, and law-enforcement interaction across 45 runs at the Aberdeen Test Center [3]FMCSA.DOT.GOV - Automated CMV Evaluation (ACE) Program. The commercial significance is not merely the ability to automate a lane change; fleets must show that a vehicle can degrade safely, document system behavior, and integrate with roadside enforcement and incident-response procedures.
Fuel Efficiency Gains & Operating Cost Reduction. Electrification provides a measurable energy-efficiency advantage where charging and duty cycles are suitable. Battery-electric heavy-duty trucks are about 55% more energy-efficient than comparable diesel trucks, while fuel-cell electric trucks are about 30% more efficient. Autonomous driving software can support route and speed consistency, but the economic benefit depends on the whole operating system: energy price, charging access, payload, route length, and vehicle uptime. It should therefore be evaluated as part of fleet total cost of ownership rather than as an isolated fuel-saving feature.
Key Restraints
High Initial Technology & Vehicle Acquisition Costs. Level 4 deployment requires redundant sensing, computing, braking and steering systems, remote-support capability, validation tooling, and ongoing software operations. Electric truck acquisition costs add another capital burden: the IEA estimated battery-electric trucks cost two to three times as much as diesel trucks in 2024. The constraint is particularly acute for smaller fleets that lack concentrated routes, balance-sheet capacity, and technical staff to absorb early deployment risk.
Public Safety Concerns & Acceptance Barriers. Autonomous trucking requires more than demonstrated vehicle performance; it requires a credible liability, insurance, monitoring, and incident-reporting framework. Brazil's Chamber of Deputies approved a proposal in July 2025 requiring testing documentation, insurance coverage, monitoring systems, immediate accident reporting, driver training, and infrastructure adaptation for autonomous vehicles [4]CAMARA.LEG.BR - Comissão aprova regras para a circulação de carros autônomos no Brasil, July 2025. Such conditions can improve public confidence, but they also add compliance steps before fleets can scale commercial operations.
GMI Analyst View
The chief adoption bottleneck is the gap between technical capability and operational readiness. A vehicle may perform well on a highway route, yet commercial deployment still requires a fleet to define fallback behavior, maintain sensors and computing hardware, secure insurance, manage roadside interactions, and communicate a safety case to regulators and customers. These requirements favor partnerships between autonomous-system developers, truck OEMs, and large logistics operators.
Cost pressure will not disappear simply because component prices decline. Lower hardware costs improve unit economics, but they do not substitute for utilization. The fleets most likely to cross the investment threshold are those that can spread technology and support costs across recurring, high-mileage freight lanes. Smaller or fragmented operators may participate later through leasing, managed services, or shared autonomous freight capacity rather than direct ownership.
Autonomous Long-Haul Trucking Market Segment Analysis
By Automation Level
Level 3. Level 3 represented USD 2,844.4 million, or approximately 89.4% of 2025 revenue, and is forecast to grow at 26.5% annually. Its lead reflects the ability to add highway automation while retaining a driver in the operating model. Germany's first type approval for Automated Lane Keeping Systems at speeds up to 95 km/h in 2024 illustrates the progression of supervised automation under defined conditions [5]BMV.DE - Strategy for Autonomous Driving in Road Traffic. The segment remains commercially relevant as a lower-risk entry point for fleets, although it does not capture the full labor-substitution potential of Level 4.
Level 4. Level 4 generated USD 338.0 million in 2025 and is projected to reach USD 4,763.9 million by 2035. Its 29.8% CAGR reflects the additional commercial value of driverless operation inside a defined operational domain. China's autonomous trucks logging more than one million kilometers daily demonstrate the importance of accumulated operating data in expanding validated driving conditions. Level 4 deployment will remain concentrated on controlled routes because expanding beyond those routes raises the validation and support burden.
Level 5. Level 5 recorded no commercial revenue in 2025 and is projected to contribute USD 10.3 million by 2035. Full automation across unrestricted environments remains materially different from Level 4 corridor operations. Its limited forecast value indicates that fleets are expected to prioritize commercial systems that solve defined highway use cases before pursuing universal vehicle autonomy.
By Propulsion
Diesel. Diesel led the market with USD 2,123.2 million in 2025, or 66.7% of revenue, and is forecast to reach USD 18,894.3 million by 2035. Existing fuel infrastructure and long-range operating familiarity support early adoption, particularly on routes where charging remains unavailable. Diesel's 24.9% CAGR trails the overall market because new autonomous platforms increasingly need to accommodate emissions policy, customer decarbonization requirements, and alternative-fuel infrastructure.
Electric. Electric autonomous trucks accounted for USD 616.4 million in 2025 and are projected to reach USD 8,717.9 million by 2035. China accounted for more than 80% of global electric truck sales in 2024, while Europe recorded more than 10,000 sales and the United States more than 1,700. Electric propulsion is best suited to autonomous corridors with planned charging stops, controlled dwell periods, and stable daily mileage. The International Energy Agency expects total-cost-of-ownership parity for electric heavy-duty trucks around 2030 in the EU and U.S., which could reduce a major adoption barrier for electrified autonomous fleets.
Hybrid. Hybrid propulsion contributed USD 442.8 million in 2025 and is expected to reach USD 5,535.7 million by 2035. The segment provides a transitional option for operators that require some emissions and fuel-use reduction but cannot yet rely on high-power charging across their freight networks. Its 29.1% CAGR indicates that it can support adoption during infrastructure buildout, although it does not offer the same zero-emission operating proposition as battery-electric or fuel-cell platforms.
By Vehicle Class
Class 7 (26,001–33,000 lbs GVWR). Class 7 generated USD 798.7 million in 2025 and is forecast to reach USD 7,060.5 million by 2035. These vehicles address regional and specialized freight routes where lower vehicle weight can reduce energy demand and broaden the range of electric operating models. Their 24.8% CAGR is lower than Class 8 because the market definition is centered on long-haul freight, where heavier tractor-trailer configurations carry more freight value and accumulate more highway operating hours.
Class 8 (33,001+ lbs GVWR). Class 8 accounted for USD 2,383.7 million, or 74.9% of 2025 revenue, and is projected to reach USD 26,087.4 million by 2035. The segment's 27.5% CAGR reflects its alignment with the most attractive autonomous use case: high-mileage, highway-dominant freight between major hubs. FMCSA's ACE testing of Class 8 tractors reinforces that commercial-vehicle automation evaluation is centered on the vehicle class that moves the largest volumes of long-haul freight.
By Application
Hub-to-Hub Operations. Hub-to-hub operations generated USD 951.0 million in 2025 and are expected to reach USD 10,640.5 million by 2035. The model separates the standardized highway middle mile from higher-complexity urban pickup and delivery tasks. Japan's Ministry of Land, Infrastructure, Transport and Tourism opened two 2025 solicitation rounds for autonomous trunk-route freight demonstrations intended to address driver shortages and improve productivity [6]MLIT.GO.JP - Demonstration project solicitation for autonomous trunk-route freight transport, 2025. The model's commercial advantage is its ability to concentrate mapping, maintenance, remote support, and charging infrastructure at a limited number of hubs.
Long-Distance Freight Transport. Long-distance freight transport was the largest application, generating USD 1,445.9 million in 2025 and projected to reach USD 14,618.2 million by 2035. It includes routes exceeding 500 miles that may extend beyond standardized hub pairs. Its 26.5% CAGR reflects substantial labor and utilization value, offset by greater route variability, more diverse weather exposure, and more complex pickup and delivery interfaces than dedicated hub-to-hub lanes.
Port & Terminal Logistics. Port and terminal logistics accounted for USD 476.0 million in 2025 and is forecast to reach USD 5,104.8 million by 2035. Controlled terminals can provide predictable movement patterns and centralized vehicle management, but the operating environment also involves dense equipment interaction, loading schedules, and site-specific safety protocols. Adoption is likely to advance where autonomous yard, gate, and terminal systems can be integrated with highway drayage rather than operated as disconnected solutions.
Cross-Border Logistics. Cross-border logistics generated USD 228.1 million in 2025 and is expected to reach USD 2,154.6 million by 2035. The segment's 25.7% CAGR is restrained by different vehicle rules, insurance requirements, communications systems, customs procedures, and liability frameworks across jurisdictions. The International Energy Agency identified the Megawatt Charging System and ChaoJi standards as relevant for charging above 350 kW, while noting recognition of MCS in the EU and U.S. Standards alignment can lower infrastructure complexity, but it does not eliminate the need for regulatory interoperability.
Others. Other applications represented USD 81.4 million in 2025 and are projected to reach USD 629.8 million by 2035. This group includes specialized routes that do not fit established freight or terminal categories. Its lower 23.1% CAGR reflects less route repetition and thinner deployment volumes, which make it harder to amortize autonomous-system validation and support costs.
By End-Use
Logistics & Fleet Operators. Logistics and fleet operators accounted for an estimated USD 2,164.0 million in 2025, equivalent to 68.0% of market revenue. These operators are the primary adopters because they control routes, dispatch systems, maintenance facilities, and freight volume. China's ZTO Express operated more than 2,000 unmanned vehicles, delivered more than 200,000 parcels daily across more than 200 cities, and had logged over 20 million kilometers by 2025. Large-scale operators can use such operating data to improve route design and determine where automated capacity provides the greatest service benefit.
Retail & E-Commerce. Retail and e-commerce accounted for an estimated USD 700.1 million in 2025, or 22.0% of revenue. Demand is driven by scheduled distribution-center flows, peak-season capacity pressure, and the cost of missed delivery windows. The segment is particularly suited to hub-based deployment where retailers operate or contract predictable lanes between fulfillment centers and regional distribution sites.
FMCG & Food Supply Chains. FMCG and food supply chains represented an estimated USD 159.1 million in 2025. Time-sensitive replenishment and temperature-controlled freight create a value proposition for more consistent long-haul operations, but deployment must integrate with temperature monitoring, loading schedules, food-safety procedures, and exception management. The commercial case strengthens where the same production and distribution sites are connected by recurring high-volume lanes.
Industrial Goods Suppliers. Industrial goods suppliers represented an estimated USD 95.5 million in 2025. Manufacturers can use autonomous freight on recurring plant-to-warehouse or supplier-to-assembly routes, but adoption depends on whether freight volumes are predictable enough to justify dedicated operating domains. The segment is likely to favor Class 8 and long-distance applications where cargo density and route repetition are high.
Others. Other end uses accounted for an estimated USD 63.6 million in 2025. This category includes specialized supply chains that may benefit from automation but lack the scale, standardization, or infrastructure concentration required for rapid deployment. Managed autonomous freight services may be more practical than direct ownership for these users.
GMI Analyst View
Segment performance is determined less by a vehicle's nominal autonomy level than by the operating environment around it. Class 8, long-distance freight, and hub-to-hub applications lead because they combine high equipment utilization with routes that can be mapped, monitored, and supported repeatedly. By contrast, cross-border and specialized applications require technology to function within multiple institutional systems, raising the cost of commercialization even where the freight need is clear.
Electric and hybrid platforms widen the strategic choices available to fleets, but they also make operating design more important. A diesel autonomous truck can exploit existing fuel networks, while electric systems can offer stronger energy economics where charging is planned and utilization is controlled. The commercially durable models will be those that align automation, propulsion, route design, and terminal operations instead of treating each decision independently.
Autonomous Long-Haul Trucking Market Regional Analysis
North America
North America was the largest regional market in 2025, with USD 1,281.8 million in revenue and a 40.3% share. It is projected to reach USD 13,424.9 million by 2035 at a 27.0% CAGR. The region's advantage lies in its extensive long-haul freight corridors, concentrated fleet operators, and technology ecosystem. The United States is the principal deployment market; FMCSA's ACE program provides a federal testing reference point for Class 8 automation. Canada adds potential for electric and cold-weather operating models, although commercial deployment will depend on corridor-specific validation and charging availability.
Europe
Europe generated USD 837.0 million in 2025 and is projected to reach USD 8,389.7 million by 2035, expanding at 26.4% annually. Germany is the regional regulatory anchor. Its 2021 Act on Autonomous Driving established a statutory basis for Level 4 operations, and the federal strategy reports EUR 642 million in autonomous-driving funding since 2016, 26 test beds, and an ambition to create the world's largest coherent autonomous operational area by 2028. The UK, France, Italy, Spain, Russia, the Netherlands, Norway, and Sweden are relevant freight markets, but commercial timing will vary with national vehicle rules, cross-border procedures, digital-road infrastructure, and the availability of high-power charging. The Mediterranean green-road-corridor initiative between Algeciras and Avignon illustrates the infrastructure logic needed for long-distance zero-emission freight.
Asia Pacific
Asia Pacific generated USD 881.3 million in 2025 and is projected to reach USD 9,878.1 million by 2035, making it the fastest-growing major region at a 27.8% CAGR. China is the principal scale market: more than 6,000 unmanned vehicles were commercially operating, more than 100 cities had opened access to unmanned vehicles, and Beijing and Shenzhen had issued Level 4 test licenses for micro cargo vehicles. China's scale in electric trucks also supports convergence between electrification and autonomy. Japan's trunk-route demonstrations indicate a structured route toward commercial freight deployment. South Korea, India, Australia, Singapore, Vietnam, and Malaysia represent additional long-term opportunities, but adoption will depend on freight-corridor density, local regulations, communications infrastructure, and the economics of vehicle acquisition.
Latin America
Latin America accounted for USD 114.5 million in 2025 and is forecast to reach USD 895.0 million by 2035 at a 23.3% CAGR. Brazil is the region's central market because its July 2025 legislative proposal created a defined framework for autonomous-vehicle testing, insurance, monitoring, reporting, and training. Mexico offers cross-border and manufacturing-corridor potential, while Argentina and Chile provide specialized long-distance freight opportunities. The region's lower growth rate reflects infrastructure and financing constraints, alongside the challenge of deploying capital-intensive technology across fragmented fleet bases.
MEA
MEA generated USD 67.8 million in 2025 and is expected to reach USD 560.2 million by 2035 at a 23.9% CAGR. The UAE is positioned around concentrated logistics corridors and controlled operating environments, whereas Saudi Arabia and South Africa offer larger geographic freight opportunities with more varied infrastructure conditions. Market development will depend on clear operating approvals, fleet investment capacity, communications coverage, and resilience to high-temperature or variable-road conditions.
GMI Analyst View
Regional leadership depends on whether a market can convert regulatory permission into repeatable commercial operations. North America benefits from route scale and fleet concentration; Europe, particularly Germany, brings formal regulatory and digital-infrastructure development; and Asia Pacific combines Chinese deployment scale with Japan's structured trunk-route programs. These are different routes to adoption, and they will produce different competitive advantages.
China's operating scale is strategically important because real-world mileage can accelerate system validation and cost learning. Europe's model is more institutionally coordinated, with emphasis on legal frameworks and interoperable infrastructure. Emerging markets can still create attractive deployments, especially in ports, industrial corridors, and export routes, but their near-term opportunity is likely to be selective rather than broad-based because financing, infrastructure, and regulatory implementation must advance together.
Autonomous Long-Haul Trucking Market Share & Competitive Landscape
The top five companies accounted for approximately 47% of the market in 2025, indicating moderate concentration. Continental held about 25% share, followed by Daimler Truck (Torc Robotics) at approximately 10.3%, TRATON Group at 5.4%, Applied Intuition at 3.3%, and Einride at 2.5%. The remaining share is distributed among OEMs, autonomous-driving software developers, systems suppliers, and emerging specialists.
Continental's position reflects the value of supplying components and integrated systems across multiple vehicle programs. Daimler Truck and Torc Robotics combine autonomous technology with production-truck engineering, service networks, and fleet relationships. TRATON Group has a relevant European platform through MAN, Scania, and International Motors, including the MAN ATLAS-L4 motorway project referenced in Germany's autonomous-driving strategy. Applied Intuition and Einride represent different technology-led approaches: software and validation infrastructure in the former case, and autonomous electric freight operations in the latter.
The company scope includes Aurora Innovation, Waymo (Alphabet), Kodiak Robotics, Daimler Truck (Torc Robotics), Volvo Autonomous Solutions, CreateAI, Plus.ai, Applied Intuition, Stack AV, Einride, Waabi Innovation, Continental, Inceptio Technology, Pony AI, TRATON Group, Hino Motors, Gatik AI, Outrider Technologies, Minus Zero, and FERNRIDE. Their competitive positions differ by vehicle integration, operating domain, regional access, software architecture, fleet partnerships, and the ability to establish a credible safety case.
Partnerships are becoming a central route to commercialization because no participant controls every required capability. OEMs provide vehicle engineering, manufacturing, servicing, and customer access; software developers provide autonomy stacks, simulation, and data systems; logistics operators provide live routes and operating feedback. The competitive advantage will increasingly rest on whether a partnership can move from a demonstration to a repeatable, supported freight service without diluting safety accountability among its participants.
Recent Industry Developments
Need a specific section of this report?
Purchase regional analysis, country-level analysis, company profiles, or any other segment-level insights separately
based on your research needs.
Frequently Asked Question(FAQ) :
Research methodology, data sources & validation process
This report draws on a structured research process built around direct industry conversations, proprietary modelling, and rigorous cross-validation and not just desk research.
Our 6-step research process
1. Research design & analyst oversight
At GMI, our research methodology is built on a foundation of human expertise, rigorous validation, and complete transparency. Every insight, trend analysis, and forecast in our reports is developed by experienced analysts who understand the nuances of your market.
Our approach integrates extensive primary research through direct engagement with industry participants and experts, complemented by comprehensive secondary research from verified global sources. We apply quantified impact analysis to deliver dependable forecasts, while maintaining complete traceability from original data sources to final insights.
2. Primary research
Primary research forms the backbone of our methodology, contributing nearly 80% to overall insights. It involves direct engagement with industry participants to ensure accuracy and depth in analysis. Our structured interview program covers regional and global markets, with inputs from C-suite executives, directors, and subject matter experts. These interactions provide strategic, operational, and technical perspectives, enabling well-rounded insights and reliable market forecasts.
3. Data mining & market analysis
Data mining is a key part of our research process, contributing nearly 20% to the overall methodology. It involves analysing market structure, identifying industry trends, and assessing macroeconomic factors through revenue share analysis of major players. Relevant data is collected from both paid and unpaid sources to build a reliable database. This information is then integrated to support primary research and market sizing, with validation from key stakeholders such as distributors, manufacturers, and associations.
4. Market sizing
Our market sizing is built on a bottom-up approach, starting with company revenue data gathered directly through primary interviews, alongside production volume figures from manufacturers and installation or deployment statistics. These inputs are then pieced together across regional markets to arrive at a global estimate that stays grounded in actual industry activity.
5. Forecast model & key assumptions
Every forecast includes explicit documentation of:
✓ Key growth drivers and their assumed impact
✓ Restraining factors and mitigation scenarios
✓ Regulatory assumptions and policy change risk
✓ Technology adoption curve parameter
✓ Macroeconomic assumptions (GDP growth, inflation, currency)
✓ Competitive dynamics and market entry/exit expectations
6. Validation & quality assurance
The final stages involve human validation, where domain experts manually review filtered data to identify nuances and contextual errors that automated systems might miss. This expert review adds a critical layer of quality assurance, ensuring data aligns with research objectives and domain-specific standards.
Our triple-layer validation process ensures maximum data reliability:
✓ Statistical Validation
✓ Expert Validation
✓ Market Reality Check
Trust & credibility
Verified data sources
Trade publications
Industry journals, trade publications, and specialized media.
Industry databases
Proprietary and third-party market databases
Regulatory filings
Government procurement records and policy documents
Academic research
University studies and specialist institution reports
Company reports
Annual reports, investor presentations, and filings
Expert interviews
C-suite, procurement leads, and technical specialists
GMI archive
13,000+ published studies across 20+ industry verticals
Trade data
Import/export volumes, HS codes, and customs records
Parameters studied & evaluated
Every data point in this report is validated through primary interviews, true bottom-up modelling, and rigorous cross-checks. Read about our research process →