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--- en:safeav:as:autolevels [2025/10/20 13:57] – rczyba
+++ en:safeav:as:autolevels [2026/04/07 12:05] (current) – airi
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 <todo @rczyba #rczyba:2025-10-12></todo>
-Autonomy of unmanned systems refers to their ability to self-manage, make decisions, and complete tasks with minimal or no human intervention. The scope of autonomy ranges from zero to full capability, often defined through models, and encompasses four fundamental functions: perception, orientation, problem-solving (planning), and action. Advances in autonomy enable unmanned systems to learn, adapt to changing environmental conditions, and perform complex tasks, driving innovation in various fields.
+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.
 ===== Levels of Ground Vehicle Autonomy =====
-There are several ways to classify autonomy levels based on various criteria. 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.
+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.
 <figure Ref.Pic.1>
-{{ :en:safeav:as:pic1_loa_automotive.png?600 |}}
+{{ :en:safeav:as:pic1_loa_automotive.png?700 |}}
 <caption>Levels of autonomous driving - SAE International classification ((https://www.eloy.co.uk/insights/driverless-cars-the-5-levels-of-automation/))</caption>
 </figure>
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   *   **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.
+===== Levels of Airborne Autonomy =====
-===== Levels of Drone Autonomy =====
 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 ((INSIGHT. The Journey Towards Autonomy in Civil Aerospace. Technical report. Cranfield, United Kingdom: Aerospace Technology Institute (ATI); 2020)). 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 ((Chen H, Wang XM, Li Y. A Survey of Autonomous Control for UAV. Washington, D.C., United States: IEEE Computer Society; 2009)). 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 {{ref>Ref.Pic.2}}).
 <figure Ref.Pic.2>
-{{:en:safeav:as:pic3.png?400|}}
+{{ :en:safeav:as:pic3.png?700 |}}
 <caption>Pilot authority and tasks control [2]</caption>
 </figure>
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 <figure Ref.Pic.3>
-{{:en:safeav:as:pic4_loa_drone.png?400|}}
+{{ :en:safeav:as:pic4_loa_drone.png?700 |}}
-<caption>Levels of Drone Autonomy [4]</caption>
+<caption>Levels of Drone Autonomy ((https://droneii.com/drone-autonomy?srsltid=AfmBOorGZxbiXskupiw9dLFzHPLMkuXeV_Aoyl0R9rVcSWhW3UvNBDaU))</caption>
 </figure>
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   *   **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.
+Another general but useful model describing autonomy levels in unmanned systems is the Autonomy Levels for Unmanned Systems (ALFUS) model ((Chen TB. Management of Multiple Heterogenous Unmanned Aerial Vehicles Through Capacity Transparency [thesis]. Queensland, Australia: Queensland University of Technology; 2016)). 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 ((EASA. Easy Access Rules for Unmanned Aircraft Systems. Technical report. Cologne, Germany: European Union Aviation Safety Agency; 2022)). 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 {{ref>Ref.Pic.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.
+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 ((D. Cvetković, Ed., ‘Drones - Various Applications’. IntechOpen, Dec. 08, 2023. doi: 10.5772/intechopen.1000551)). 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 {{ref>Ref.Pic.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.
 <figure Ref.Pic.4>
-{{:en:safeav:as:pic5_loa_multi.png?400|}}
+{{ :en:safeav:as:pic5_loa_multi.png?700 |}}
 <caption>Autonomy Levels for Multi-Drone Systems</caption>
 </figure>
-==== References ====
+===== Marine autonomy (IMO MASS levels) and Space autonomy (NASA ALFUS framework) =====
-[1] https://www.eloy.co.uk/insights/driverless-cars-the-5-levels-of-automation/
-[2] INSIGHT. The Journey Towards Autonomy in Civil Aerospace. Technical report. Cranfield, United Kingdom: Aerospace Technology Institute (ATI); 2020.
-[3] Chen H, Wang XM, Li Y. A Survey of Autonomous Control for UAV. Washington, D.C., United States: IEEE Computer Society; 2009.
+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.
-[4] https://droneii.com/drone-autonomy?srsltid=AfmBOorGZxbiXskupiw9dLFzHPLMkuXeV_Aoyl0R9rVcSWhW3UvNBDaU
+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.
-[5] Chen TB. Management of Multiple Heterogenous Unmanned Aerial Vehicles Through Capacity Transparency [thesis]. Queensland, Australia: Queensland University of Technology; 2016
+**Why marine and space autonomy frameworks differ from ground autonomy:**
-[6] EASA. Easy Access Rules for Unmanned Aircraft Systems. Technical report. Cologne, Germany: European Union Aviation Safety Agency; 2022
+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.
-[7] D. Cvetković, Ed., ‘Drones - Various Applications’. IntechOpen, Dec. 08, 2023. doi: 10.5772/intechopen.1000551
+^ Unified Level ^ Ground (SAE J3016) ^ Airborne (NASA / UAV / DoD) ^ Marine (IMO MASS / DNV) ^ Space (NASA ALFUS) ^ Description ^
+| Level 0 | Level 0 – No automation | Manual flight | AL 0 – Manual ship | ALFUS 0 – Manual | Human performs all sensing, planning, and control |
+| Level 1 | Level 1 – Driver assistance | Basic autopilot (e.g., altitude hold, heading hold) | MASS 1 – Decision support | ALFUS 1 – Teleoperation assist | Automation assists human but does not replace decision-making |
+| Level 2 | Level 2 – Partial automation | Automated flight execution with supervision | MASS 2 – Remotely controlled with crew onboard | ALFUS 2 – Automated execution | System performs control functions but human supervises continuously |
+| Level 3 | Level 3 – Conditional automation | Supervisory autonomy | MASS 3 – Remotely controlled without crew | ALFUS 3 – Supervisory autonomy | System performs mission tasks but human intervenes when needed |
+| Level 4 | Level 4 – High automation | High autonomy UAV | MASS 4 – Fully autonomous ship | ALFUS 4–5 – High autonomy spacecraft | System operates independently in defined environments |
+| Level 5 | Level 5 – Full automation | Fully autonomous UAV | Fully autonomous ship (advanced DNV AL 4+) | ALFUS 6 – Full autonomy | System operates independently in all environments |
+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.

en/safeav/as/autolevels.1760957857.txt.gz · Last modified: 2025/10/20 13:57 by rczyba