Definitions, Classification, and Levels of Autonomy

[rczyba][✓ rczyba, 2025-10-12]

Intuitively, autonomy of unmanned systems refers to their ability to self-manage, make decisions, and complete tasks with minimal or no human intervention. To collaborate with other systems or humans, autonomy requires a clear system definition. This definition not only communicates function to partners and users, but also sets an expectation function. Expectation functions are central to many technical (validation), governance (licensing), and legal (liability) processes. Each of the physical domains have built somewhat similar “levels” of autonomy which start setting expectation functions.

For ground vehicles, in 2014, the American organization Society of Automotive Engineers (SAE) International adopted a classification of six levels of autonomous driving, which was subsequently modified in 2016. Based on a decision by the National Highway Traffic Safety Administration (NHTSA), this is the officially applicable standardization in the United States, which is also the most popular in studies on autonomous driving technologies in Europe.

To clarify the situation, SAE International has defined 5 levels of automation for autonomous vehicles, which have been adopted as an industry standard (see Figure 1).

• Level 0: The driver has full control of the vehicle and there are no automated systems.

• Level 1: Also known as “hands-on,” the driver controls all standard driving functions, such as steering, acceleration, braking, and parking. Some automated systems, such as cruise control, parking assist, and lane-keeping assist, will be built into the car. The driver must monitor their surroundings and be able to take full control at any time.

• Level 2: “Hands-free” automation means that the automated system can take full control of the vehicle, steering, accelerating, and braking. However, the driver must be ready to take control of the vehicle if necessary. The “hands-free” principle shouldn't be taken literally, and the SAE recommends that the hands remain in contact with the steering wheel to confirm that the driver is ready to take control.

• Level 3: Level 3 is referred to as “without looking into the eyes” automation. The driver can focus on activities other than driving, such as using a phone or watching a movie. The automated system will be able to respond to situations requiring immediate action, such as emergency braking, but the driver will still need to intervene if notified by the technology.

• Level 4: The next level is “mind-off” automation. It's essentially similar to Level 3 in that the driver doesn't need to monitor their surroundings. In fact, they can fall asleep, as driver intervention isn't required, even in emergency situations. However, this level of autonomy is only supported in limited areas or under specific circumstances, such as traffic jams.

• Level 5: Level 5 means “steering wheel optional.” The car is fully autonomous and requires no human intervention.

Today, these levels have become the shorthand to communicate expectations and the object of regulatory and legal battles.

In general, autonomy or autonomous capability is defined in the context of decision-making or self-governance within a system. According to the Aerospace Technology Institute (ATI), autonomous systems can essentially decide independently how to achieve mission objectives, without human intervention ^[2]. These systems are also capable of learning and adapting to changing operating environment conditions. However, autonomy may depend on the design, functions, and specifics of the mission or system ^[3]. Autonomy can be broadly viewed as a spectrum of capabilities, from zero autonomy to full autonomy. The Pilot Authorization and Task Control (PACT) model assigns authorization levels, from level 0 (full pilot authority) to level 5 (full system autonomy), also used in the automotive industry for autonomous vehicles (see Figure 2).

Levels of autonomy in drone technology are typically divided into five distinct levels, each representing a gradual increase in the drone's ability to operate independently.

• Level 0: The pilot has complete control over every movement. The platforms are always 100% manually controlled.

• Level 1 – Remote Control: The pilot retains control of overall operations and vehicle safety. However, the drone can take over one or more essential functions for a specified period. While the pilot does not have continuous control of the vehicle and never simultaneously controls speed and direction, it can assist with navigation and/or maintain altitude and position. The drone is supported by GNSS for flight stabilization, and all inputs regarding direction, altitude, and speed are entered manually. Obstacle detection functions are available at this level, but avoidance is performed manually by the pilot.

• Level 2 - Automated Flight Control (Assisted Autonomy): A pre-programmed flight path is transmitted to the autopilot, and the drone begins its mission, flying along waypoints. The pilot is still responsible for the safe control of the vehicle and must be ready to take control of the drone if an unexpected event occurs. However, under certain conditions, the drone itself can take control of the drone’s heading, altitude, and speed. The pilot still has full control, including monitoring airspace, flight conditions, and responding to emergencies. Most manufacturers currently build drones at this level, where the platform can assist with navigation functions and allow the pilot to disengage from certain tasks.

• Level 3 - Partial Autonomy (Semi-Autonomous): Similar to Level 2, the drone can fly autonomously, but the pilot must be alert and ready to take control at any time. The drone notifies the pilot of the need for intervention, acting as an emergency system. This level means that the drone can perform all functions “under certain conditions.”

• Level 4 - Cognitive Autonomy (Advanced Semi-Autonomous): A drone can be controlled by a human, but it doesn't always have to be. Under the right conditions, it can fly autonomously at all times. It's expected that the drone will have redundant systems so that if one system fails, it will continue to operate. At this level of autonomy, the “sense and avoid” function becomes “sense and navigate.” This means that the drone detects obstacles along its flight path and actively avoids contact by changing its flight trajectory.

• Level 5 – Full Autonomy: The drone controls itself independently in any situation, without the need for human intervention. This includes full automation of all flight tasks in all conditions. Currently, such drones do not yet exist. However, it is expected that in the near future, they will be able to utilize artificial intelligence tools for flight planning—in other words, autonomous learning systems with the ability to modify routine behavior.

Another general but useful model describing autonomy levels in unmanned systems is the Autonomy Levels for Unmanned Systems (ALFUS) model ^[5]. European Union Aviation Safety Agency (EASA), in one of its technical reports, provided some information on autonomy levels and guidelines for human-autonomy interactions. According to EASA, the concept of autonomy, its levels, and human-autonomous system interactions are not established and remain actively discussed in various areas (including aviation), as there is currently no common understanding of these terms ^[6]. Since these concepts are still somewhat developmental, this becomes a huge challenge for the unmanned aircraft regulatory environment as they remain largely unestablished.

The classification of autonomy levels in multi-drone systems is somewhat different. In multi-drone systems, several drones cooperate to perform a specific task. Designing multi-drone systems requires that individual drones have an increased level of autonomy. The classification of autonomy levels is directly related to the division into flights performed within the pilot's or observer's line of sight (VLOS) and flights performed beyond the pilot's line of sight (BVLOS), where particular attention is paid to flight safety. One way to address the autonomy issue is to classify the autonomy of drones and multi-drone systems into levels related to the hierarchy of tasks performed ^[7]. These levels will have standard definitions and protocols that will guide technology development and regulatory oversight. For single-drone autonomy models, two distinct levels are proposed: the vehicle control layer (Level 1) and the mission control layer (Level 2), see Figure 4. Multi-drone systems, on the other hand, have three levels: single-vehicle control (Level 1), multi-vehicle control (Level 2), and mission control (Level 3). In this hierarchical structure, Level 3 has the lowest priority and can be overridden by Levels 2 or 1.

For marine systems, the International Maritime Organization (IMO) defines autonomy through its Maritime Autonomous Surface Ship (MASS) framework, which describes four progressive levels of autonomy based on the degree of human involvement and onboard decision-making capability. At lower levels, ships use automation primarily to assist human crews with navigation, propulsion, and safety monitoring, while humans remain onboard and responsible for operational decisions. Intermediate levels allow remote operation, where ships may operate without onboard crew but are supervised and controlled from shore-based control centers. At the highest level, fully autonomous vessels can perceive their environment, make navigation and mission decisions independently, and execute those decisions without human intervention. This framework reflects the operational realities of maritime missions, where long durations, predictable dynamics, and remote monitoring make gradual progression toward autonomy feasible.

In space systems, autonomy is commonly described using NASA’s Autonomy Levels for Unmanned Systems (ALFUS) framework, which evaluates autonomy based on the system’s independence from human control, its ability to handle environmental complexity, and its capacity to accomplish mission objectives without intervention. At lower levels, spacecraft rely heavily on ground operators for command and control, executing predefined instructions with minimal onboard decision-making. As autonomy increases, spacecraft gain the ability to perform functions such as fault detection and recovery, autonomous navigation, and adaptive mission planning. At the highest levels, systems can independently perceive their environment, evaluate mission goals, and dynamically adjust their behavior to achieve objectives without real-time human guidance. This progression is particularly important in deep-space missions, where communication delays make continuous human control impractical.

Why marine and space autonomy frameworks differ from ground autonomy:

Marine and space autonomy frameworks differ fundamentally from ground autonomy because their operational constraints emphasize endurance, remote operation, and system resilience rather than continuous interaction with humans in dense, unpredictable environments. Ground vehicles must operate safely in close proximity to human drivers, pedestrians, and complex infrastructure, requiring highly responsive real-time perception and decision-making. In contrast, marine systems operate in relatively structured environments with fewer immediate hazards, allowing autonomy to focus more on navigation efficiency and remote supervision. Space systems present even greater challenges, including extreme communication latency, harsh environmental conditions, and the impossibility of real-time human intervention, requiring spacecraft to autonomously detect faults, maintain operational health, and ensure mission survival. As a result, autonomy in marine and space systems is driven more by operational independence and mission continuity than by immediate human safety interactions. The table below provides a summary of all four domains.

The classification of autonomy into structured levels is not merely a technical taxonomy; it serves as a foundational construct for legal responsibility, regulatory approval, and ethical governance. These autonomy levels define an expectation function, which specifies who (human or machine) is responsible for sensing, decision-making, and action execution under defined operational conditions. This expectation function becomes the basis for certification, validation, liability assignment, and operational authorization which we will discuss in the next section.