Autonomy in aviation has reached a significant milestone, largely due to advancements in technologies such as precision navigation and flight management systems. These systems allow aircraft to perform many functions autonomously, crucially navigating, communicating, and managing flight paths without constant human intervention.
Automation is at the core of Advanced Air Mobility (AAM) and is what makes it scalable, safe, and efficient. This concept refers to aircraft that can make their own smart decisions, manage flight routes, avoid conflicts, and respond to hazards. Automation makes AAM a viable business.
Without automation, a human must be in the loop for every decision made, which is not feasible for the scale at which AAM intends to operate. Leveraging automation in AAM is like giving the aircraft a smart copilot made up of algorithms, always alert and continuously determining the best action to take given the current state of the environment.
Think of automation as the glue holding AAM together. Right now, the current airspace system already faces a shortage of air traffic controllers, but incorporating autonomy will lead to safe high-density operations without overwhelming humans.
Technological Enablers
Algorithms—pieces of logic written in code that are used to process data and provide an output based on a desired outcome—constitute each service at SkyGrid. They are developed and improved using a combination of simulations and real-world testing. Algorithms reduce the effort required to perform specific tasks.
Algorithms can be rule-based; for instance, if X occurs, then perform Action Y. They can also be powered by artificial intelligence (AI) and reinforcement learning, where a system learns to optimize decisions over time, or they can be optimization algorithms, such as Dijkstra, A*, or RRT. These algorithms fall into categories like path planning, prediction models, and machine learning models that improve over time.
Real-time data is integral to every aspect of automation. From weather updates to changes in traffic patterns, the state of the environment is constantly changing. Real-time data and analytics ensure that autonomous systems remain up-to-date on environmental conditions, can make proactive decisions based on this information, and respond as needed pre-flight and in-flight both tactically and strategically. Without real-time data, aircraft cannot respond to hazards and environmental changes, and therefore are not capable of making smart decisions.
Emerging technologies that could further enhance automation in AAM include 6G networks for faster data exchanges, decision-making on the aircraft itself facilitated by Edge computing, more powerful AI models, and digital twins.
Enabling Scalable AAM Through Intelligent Automation
For an autonomous system to function effectively, it must adhere to four principles: the ability to observe, infer, decide, and act. Utilizing autonomy can assist with functions such as flight planning, conflict detection and resolution, monitoring aircraft health, real-time rerouting, and coordination with air traffic management (ATM).
At SkyGrid, data and digital information services—including weather data, Ground-Based Traffic Surveillance (GBTS), NOTAMs, airspace data, and global navigation satellite system (GNSS) performance data—inform algorithms about the environment. These capabilities are then employed to facilitate strategic planning and tactical planning services, including Advanced Flight Planning, Flight Plan Validation, Demand-Capacity Balancing (DCB), Dynamic Rerouting, Hazard and Constraint Monitoring, and Ground-Based Detect-and-Avoid (GBDAA), thereby enabling end-to-end autonomy.
Prioritizing Safety and Predictability of Operations
AI and machine learning enhance safety in AAM operations. When machine learning is applied, algorithms can learn and improve. Typically, a human curates or labels the data and the algorithm figures out which patterns are most useful to learn from. The goal is to produce outputs—whether that’s a decision, prediction, or classification—that closely mimic or even improve upon what a human would do in the same situation.
This increases flight safety, as AI can identify patterns in data that humans might overlook, enabling systems to detect potential issues before they arise. Integrating AI and machine learning provides an additional layer of awareness and oversight, assisting in identifying the lowest risk options in real-time.
Human error accounts for the majority of aviation incidents today. In contrast to humans, autonomous systems are consistent and do not experience fatigue or distraction. Rigorous testing and certification ensure these systems are reliable, and they are designed with fallback modes to address system failures. Autonomy standardizes system behavior and avoids improvisation in decision-making, resulting in more predictable AAM operations and enhancing safety across the entire AAM ecosystem.
Integration Challenges
In the current aviation landscape, integrating autonomy can be challenging. The existing airspace system and infrastructure were not designed for the level of automation required for high-frequency and low-altitude operations that Advanced Air Mobility (AAM) introduces. This degree of automation and reliance on cloud-based data and digital exchanges also presents increased cybersecurity risks.
To address these challenges, regulatory frameworks must evolve. New standards should be developed to accommodate existing aircraft under current flight rules and new advanced aircraft under both current and future flight rules. To prevent excessive reliance on automation, human oversight will remain essential. This will also serve as a safeguard in the event that an autonomous system misinterprets a scenario.
As a Third-Party Service Provider (TSP), SkyGrid is building a high-assurance interoperable system that helps AAM aircraft operate within the National Airspace System (NAS) without necessitating changes to existing infrastructure and rules. This means our autonomous functions can integrate into current ATM systems and communicate with traditional aircraft, while also enabling new advanced aircraft like eVTOLs to integrate seamlessly.
To ensure safety in AAM, automation is the optimal solution to accommodate the increased density of operations. Automation will bridge the gap between the current state of ATM and its envisioned future. Once Autonomous Flight Rules (AFR) are developed and autonomy in aviation becomes regulated, our system will evolve to remain compliant. Greater autonomy could lead to a centralized air traffic control (ATC) system, which would be more efficient for a high-density airspace.
