Autonomous vehicles (AVs) have the potential to transform the trucking industry by reducing shipping costs and mitigating the driver shortage.
However, according to McKinsey’s analysis, their widespread adoption will likely face another year of delay.
Despite these challenges, major OEMs are maintaining their commitment to autonomous trucking and investing in the development of these groundbreaking vehicles, aiming for deployment in the latter half of this decade (see sidebar, “The technology underpinning autonomous trucks”).
Industry and Economic Factors Driving Autonomous Trucking
Driver Shortages
The trucking industry faces critical driver shortages. In the United States, the deficit exceeds 80,000 drivers, a number projected to double by 2030.
The median age of U.S. truck drivers is 46, higher than the workforce average of 42.
In Europe, the situation is even more severe, with over 200,000 truck driving positions unfilled—expected to increase to 745,000 by 2028. Only 5% of European truck drivers are under 25, while 33% are over 55.
Regulations
Regulatory environments are increasingly supportive of AV adoption. The European Union has implemented AV regulations incorporated into member states’ laws.
In the U.S., federal regulations for autonomous driving are still pending, but most states allow testing, and many permit commercial use.
Rising Transportation Costs
Transportation costs have risen significantly in recent years. Spot rates in Europe have increased by 28% since 2017, while the U.S. logistics cost as a percentage of GDP rose from 7.5% in 2020 to 8.7% in 2023.
Higher driver salaries, fuel prices, tolls, and compliance with stricter emissions standards have contributed to these increases. Autonomous driving could offset these costs by reducing operational expenses.
Use Cases for Autonomous Trucking
From 2027 to 2040, autonomous trucking is expected to develop along two overlapping use cases: constrained autonomy and full autonomy.
Constrained Autonomy
In this initial phase, driverless trucks (SAE Level 4) operate on highways and designated “geofenced” routes.
These trucks handle long-haul legs between transfer hubs, where trailers are exchanged for manual trucks to go on local roads.
This model suits scheduled routes between logistics points like distribution centers (DCs) and factories. It is less viable for short routes, such as urban deliveries, due to higher per-trip costs.
Full Autonomy
In the second phase, driverless trucks handle DC-to-DC operations, with transfer hubs used only for recharging, refueling, or trailer swaps for non-DC destinations.
This model is already feasible for certain DCs located near highways and is expected to expand as software improves. Adoption of full autonomy will gradually increase from 2027 to 2040.
Economic Viability of Autonomous Trucking
Cost of Ownership and Route Length
The total cost of ownership (TCO) savings from autonomous trucking will vary by route length. Short routes (less than 100 miles) are unlikely to be profitable for autonomous operations due to high fixed costs.
Longer routes (over 1,500 miles), however, promise significant TCO savings. McKinsey’s analysis predicts a 42% reduction in per-mile TCO for heavy-duty trucks, despite higher costs for AV kits, monitoring services, and redundant systems.
Key cost savings will come from reduced driver salaries, lower fuel consumption, and optimized driving, which decreases repair costs.
As technology matures, further reductions in hardware, remote operations, and insurance costs are expected, enhancing the economic case for AV adoption.
Market Projections and Regional Variations
According to McKinsey, the autonomous heavy-duty trucking market could reach $616 billion by 2035, with China, the U.S., and Europe contributing $327 billion, $178 billion, and $112 billion, respectively.
United States: Rapid adoption driven by high salaries, driver shortages, and long-haul routes, with AVs accounting for 13% of trucks by 2035.
Europe: Slower adoption (4% of trucks by 2035) due to shorter routes, complex infrastructure, and weather conditions.
China: Intermediate adoption (11% by 2035), aided by its logistical space and OEM capabilities, despite low driver salaries reducing immediate financial incentives.
Emerging Business Models
Driver-as-a-Service (DaaS)
This model allows fleet customers to lease or purchase trucks and pay per mile for virtual drivers.
OEMs or AI firms manage truck operations, benefiting from sales and service fees. DaaS offers lower TCO, improved safety, and new revenue streams for OEMs.
Capacity-as-a-Service (CaaS)
Here, OEMs or AV developers manage the entire logistics process, serving end customers directly. While offering higher margins, this model involves greater risks and operational challenges, particularly in last-mile logistics.
Implications for the Mobility Ecosystem
Ecosystem Impact
As costs decline, autonomous trucking is expected to drive increased fleet volumes, encourage industry consolidation, and create opportunities for new players specializing in hub infrastructure and maintenance.
Strategic Actions for Stakeholders
1. Fleet Owners: Conduct pilots, redesign networks, and prioritize routes based on TCO savings and environmental factors.
2. Infrastructure Providers: Develop advanced hubs and collaborate on smart highways.
3. OEMs: Invest in autonomous and zero-emission technologies, and going through partnerships for AV software.
4. AV Developers: Focus on innovation, partnerships, and real-world deployment pilots.
5. Component Suppliers: Develop critical hardware and software for autonomous systems, ensuring functional safety.
Autonomous trucking holds the potential to revolutionize commercial transport, making it more efficient, affordable, and sustainable—a shared benefit for all stakeholders in the mobility ecosystem.
