Autonomous aircraft operating in complex, dynamic airspace need sophisticated digital infrastructure that can process, analyze, and act on vast amounts of data in real-time.
Data is the foundation of autonomy. As we move away from traditional piloted flight, the foundational principles of aviation safety—situational awareness, informed decision-making, and seamless coordination—must be translated into a new paradigm where data, artificial intelligence (AI), and ground-based systems assume critical roles.
At its core, autonomous flight is only as safe and scalable as the data systems that fuel it, making a robust and reliable digital backbone the single most important element for unlocking this next era of flight.
Dependence on Data for Autonomy
In data-driven autonomy, high-integrity data is fundamental. If you don’t have high-quality data inputs, safe and reliable outputs aren’t possible. Multiple high-assurance data sources create operational redundancy while enabling cross-validation between different information streams.
Traditionally, onboard pilots make decisions using aircraft instruments, radar, sensors, visual cues, weather observations, and traffic cooperation. For uncrewed operations, these same functions must be seamlessly transferred to onboard systems and ground-based infrastructure that collectively create the awareness and decision-making capabilities of the onboard pilot.
The safety of autonomous flight depends on meticulous data acquisition, validation, and curation to ensure the predictability and reliability of all outputs. Data must be trusted, authenticated, and accurate, especially in safety-critical contexts.
From Data to Decisions with AI
AI acts as the key engine that converts raw data into smart, actionable decisions. Usually, humans curate and label data sets, then use machine learning and algorithms to find valuable patterns to learn from and improve their capabilities. Through AI, data becomes information, and information turns into decisions.
AI and algorithms can analyze countless data points from disparate sources simultaneously, uncovering patterns and predictions that humans may overlook. By identifying patterns in historical data that precede specific events, these systems become increasingly effective at predicting future occurrences. For example, rapid temperature changes within a city may indicate impending high winds, while runway configuration changes at one airport could signal broader airspace modifications requiring traffic management adjustments. A recent NASA study demonstrated the effectiveness of Flight Path Management (FPM) automation in high-density environments, further validating the feasibility of vehicle-centric autonomy at scale.
Autonomous systems are consistent, do not experience fatigue or distractions, and avoid improvising when making decisions—advantages since human error accounts for most aviation incidents today. When algorithms are trained to produce outputs—whether a decision, a prediction, or a classification—that closely mimic or even outperform what a human would do in the same situation, flight safety naturally improves. Rigorous testing and certification ensure these systems are dependable, while built-in fallback modes address potential system failures.
The Role of Ground-Based TSPs
Third-Party Service Providers (TSPs) like SkyGrid provide the comprehensive context necessary for safe and efficient autonomous operations. Ground-based systems typically have access to more data sources than an individual aircraft, providing full situational awareness of the operating environment and real-time decision support.
If an area of the airspace closes suddenly, our system can determine available alternate routes, identify congestion levels at potential landing sites, assess weather conditions on alternate routes, and provide other critical data elements that may not be available onboard the aircraft. This situational awareness enables operators to fly aircraft safely through changing environments and conditions.
Aviation data like METARs, TFRs, and PIREPs were designed to be read, contextualized, and understood by people. Incorporating AI into data services bridges this gap by being trained on diverse data sources, allowing systems to understand context cues and external references. This expertise in data fusion and interpretation is what allows us to provide operators and aircraft with the precise information they need to respond safely to dynamic conditions.
At SkyGrid, we’re enabling autonomy through interoperable data infrastructure. Leveraging our team’s collective skillsets in aviation, AI/ML, engineering, human factors, and other disciplines helps us design and build systems that make flight safer. Ultimately, it is this expertise of those building the skies that will truly make our skies smarter.
Why Standards Matter
The digital ecosystem supporting autonomous flight should be based on performance-based standards, enabling the industry to optimize and improve solutions over time while ensuring safety targets are met. If a single, prescriptive solution is set, innovation may be hindered and future growth limited.
The AAM ecosystem needs to invest now in data infrastructure, AI capabilities, and digital services that support autonomy. Industry and regulators should work together on shared data standards, ethical AI practices, and advancing autonomy. The more we test, the more we learn, and the safer and smarter autonomy becomes. At SkyGrid, we’re dedicated to leading this transformation through innovation, expertise, and a steadfast focus on safety.