A self-driving truck, also known as an autonomous truck or robo-truck, is an application of self-driving technology aiming to create trucks that can operate without human input. Alongside light, medium, and heavy-duty trucks, many companies are developing self-driving technology in semi trucks to automate highway driving in the delivery process.
In September 2022, Guidehouse Insights listed Waymo, Aurora, TuSimple, Gatik, Plus, Kodiak, Daimler Truck, Einride, Locomation, and Embark Trucks (acquired by Applied Intuition) as the top 10 vendors in automated trucking. And, Transport Topics in November 2022 is listing fourteen companies to know about self-driving truck; Aurora, Waymo, TuSimple, Gatik, Locomation, Torc Robotics, Waabi, Einride, Plus, Embark, Kodiak, Robotic Research, Outrider and Pronto. In February 2024, this list was updated to reflect the exit of Waymo, TuSimple, Embark, and Locomation, as well as the addition of Stack AV.
Since 2022, daily testing occurs with human safety drivers behind the wheel, often performing commercial pilots for customers. Only in limited validation runs on test tracks have these autonomous trucking companies performed driverless operations where no human is located in the vehicle anymore. The reason is a self-imposed high acceptance bar for safe deployment of this technology.
In December 2024, Kodiak became the first company to launch commercial driverless operations of autonomous trucks in the United States. Operating on private lease roads in West Texas, the company provides a driver-as-a-service solution on customer-owned heavy-duty trucks. Self-driving trucks are expected to be deployed more widely on highways in the United States by 2027.
Several government agencies in the U.S. and Europe have announced new legislation surrounding the use of autonomous trucks. Some challenges of bringing self-driving trucks on public roads include, but are not limited to, road safety, the need for human drivers inside the vehicle, and the lack of specific regulations surrounding driverless vehicles.
History
1990s As recorded in June 1995 in Popular Science Magazine, self-driving trucks were being developed for combat convoys, whereby only the lead truck would be driven by a human and the following trucks would rely on satellite navigation, an inertial guidance system and ground-speed sensors.
2000s Komatsu made the earliest development in autonomous trucks testing a fleet of five Ultra Class trucks in Codelco Mine Radomiro Tomic in Chile in 2005(article), then in 2007 was installed the first working fleet in the mine Gabriela Mistral in Chile, also a Codelco property.
2010s
Lockheed Martin, with funding from the U.S. Army, developed an autonomous truck convoy system that uses a lead truck operated by a human driver with a number of trucks following autonomously. Developed as part of the Army's Autonomous Mobility Applique System (AMAS), the system consists of an autonomous driving package that, , has been installed on more than nine types of vehicles and has completed more than 55,000 hours of driving at speeds up to . , the Army was planning to field 100–200 trucks as part of a rapid-fielding program.
In Europe, truck platooning was being considered with the Safe Road Trains for the Environment approach, a project that ended in September 2012.
Since May 2015, Daimler has been testing self-driving trucks on public roads in Nevada, USA. In the USA, self-driving vehicles (such as the Inspiration Truck) are already permitted under certain conditions.
2020s
MAN develops autonomous trucks. In test runs at Hamburg's CTA terminal in 2021, a truck drove autonomously across the terminal, navigated in mixed traffic, and parked with precise reverse maneuvering. Outside the terminal, control was handled by a driver.
In 2026, after launching in 2025, Aurora trucks became able to drive nonstop from Phoenix to Fort Worth, a roughly 1,000 mile, 15 hour trip. At the time Its autonomous fleet numbered 5 trucks operating between Dallas/Ft Worth, El Paso, and Houston.
Terminology
US truck classification Self-driving trucks are being developed for all classes of trucks including, light duty, medium duty.
Light-duty trucks The EPA defines a light-duty truck to be any truck with a maximum payload capacity of 4,000 pounds and weight of 8,500 pounds (in the US only). In Europe, a light-duty truck is classified as a truck with a maximum weight of 7,720 pounds.
The U.S. Census Bureau defines a light-duty truck to have a maximum weight of 10,000 pounds.
Examples of light-duty trucks include pickup trucks, SUVs, and vans. The 2023 Ford F-150 is an example of a light-duty truck with the new Ford BlueCruise 1.2 Technology.
Medium-duty trucks The U.S. Census Bureau, unlike the EPA, established a third classification for medium duty trucks. A medium-duty truck is classified as a truck with a weight range of 10,000 to 26,000 pounds.
Examples of medium-duty trucks include box trucks and school buses. The UK government recently awarded €81 million for the production of self-driving technology in public buses.
Heavy-duty trucks The EPA defines a heavy-duty truck to have a minimum weight of 8500 pounds (in the US). The US Census Bureau defines a heavy-duty truck with a minimum weight of 26,000 pounds.
Examples of heavy-duty trucks include cement mixers, mobile cranes, and semi trucks.
SAE levels of self driving SAE International (formerly the Society of Automotive Engineers) defines six levels of driving automation ranging from "No Driving Automation" at Level 0 to "Full Driving Automation" at Level 5. This classification system is based on the degree of driver intervention required for driving. Levels 0, 1, and 2 focus on driver support features, such as adaptive cruise control and highway lane centering, while Levels 3, 4, and 5 focus on completely autonomous features, such as manual steering, control in traffic, and driving "everywhere in all conditions".
Self-driving truck technology shares similar characteristics to self-driving car technology, but additional complexity such as the weight added in the process of carrying freight contribute to some differences. There are currently light-duty trucks on the market, such as Ford's BlueCruise, that contain SAE Level 2 features, but companies like Torc are moving toward Level 4 and 5 features.
Partially automated truck platooning systems contain at least SAE Level 1 features and provide economic and environmental benefits.
Technology
Sensors Sensors, such as Lidar, radar, cameras, ultrasonic, and GPS, are crucial to self-driving technology. Due to different sensors performing best at different functions required for autonomous driving and the financial and technological challenges associated with relying on only one sensor, a successful self-driving truck system will likely feature many.
Multi-sensor solutions, such as one manufactured by Continental integrate many pre-calibrated sensors that work synchronously to achieve self-driving features SAE Level 4 and above in modern trucks.
Mapping and localization High-resolution mapping technology, often combined with real-time sensor data, can build and update a detailed map that provides better access to vehicle localization information.
Artificial intelligence Machine learning is a subset of artificial intelligence that is well-suited to self-driving truck technology as machine learning algorithms allow the vehicle to learn from its environment and past experiences and make attempts to improve its ability to make more accurate and informed decisions about how to operate on the road. Self-driving truck companies like Plus are developing predictive analytic systems to predict potential hazards and risks a truck may encounter and take proactive measures to avoid accidents or other safety issues.
Sensors that measure speed, distance, and distance in combination with a detailed map and an accurate sense of the vehicle's sense of location within that map often provide the input for artificial intelligence systems built to interpret the context.
AI also monitors distance, weather conditions, freight density, truck stop and warehouse density, and autonomous vehicle legislation to generate truck routes.
Humans are thought to be better than modern artificial intelligence systems at interpreting contextual information when put against modern autonomous vehicle systems, but self-driving artificial intelligence systems are expected to improve exponentially in the next decade.
Navigation and control systems The software and hardware components govern a truck's steering, acceleration, braking, and other critical functions, such as obstacle avoidance and collision detection. Similar to self-driving cars, self-driving trucks may use a combination of GPS, inertial measurement units (IMUs), and other sensors to determine their position and orientation on the road such that it can adjust its movements, turn, make stops, gain directional cues, and make decisions on how to operate.
Economics and advancements
Market The trucking industry in the United States annually generates around $740 billion in revenue. In 2021, there was approximately 2.1 million truck drivers in the United States; American truck drivers earn a median annual salary of $48,310.
According to Allied Market Research, the global self-driving truck market may have generated as much as $1 billion in revenue in 2020, and is projected to reach the market size of about $1.7 billion by 2025, growing at a compound annual growth rate of 10.4 percent from 2020 to 2025.
Employment The deployment of autonomous trucks is projected to fundamentally restructure the United States logistics workforce, primarily impacting the 2.1 million truck drivers. According to the UC Berkeley Labor Center, approximately 294,000 long-distance drivers are at the highest risk of displacement, as the industry shifts toward a "transfer hub" model. In this framework, autonomous systems handle the long distance highway segments while human drivers perform the short distance drives through urban environments. While this could potentially create 26,400 to 35,100 new technical and local logistics roles annually by 2026, research suggests these new positions may not offer the same earnings or benefits associated with traditional long-haul careers.
The geographic and demographic distribution of these impacts is notably uneven, with the "Sun Belt" states—Texas, Florida, and Arizona—serving as the primary corridors for early adoption due to favorable weather and legislative environments. Workforce demographics play a critical role in the pace of displacement. Data indicates the trucking industry faces consistently faces an aging workforce with high retirement rates, which leads to more "natural attrition" than mass layoffs. As a result, a shortage of 160,000 truck drivers is expected by 2030. If the adoption of Level 4 autonomous systems remains gradual through this timeframe, the technology may function more as a solution to chronic driver shortages rather than a direct competitor to existing workers.
Current policy discussions in 2026 emphasize the necessity of "high-road" labor standards to ensure that productivity gains from automation are shared with the workforce. The UC Berkeley Labor Center argues that without proactive intervention, the transition could exacerbate wealth inequality by shifting profits toward large carriers while degrading job quality for the remaining human operators. Emerging roles in Remote Operations Centers - offices where drivers can remotely pilot trucks - and autonomous maintenance offer potential pathways for displaced drivers, but these require significant reskilling initiatives - which research shows a high percentage of drivers are unwilling to undergo.
External Economic Impacts