Taking a flight in 2030

Viacheslav Gromov, the Founder and CEO of embedded AI manufacturer AITAD, shares a visionary glimpse of how embedded AI could redefine every aspect of aviation experience, from the terminal entrance to the aircraft itself.

The ‘Federal Association of German Air Transport Industry’ highlights a remarkable rebound with a growth rate of 2022, signalling a robust recovery in passenger air travel compared to 2021. Despite this, the Aviation Industry faces multifaceted challenges like staffing shortages, climate change or digitalisation. Here, embedded AI emerges as a pivotal instrument for navigating these challenges.

Whether it is robots autonomously parking your car, digital carpets enhancing shoe security checks, or the proliferation of apps and automated check-in terminals, the industry’s commitment to innovation and automation is evident. Nevertheless, the implementation needs to catch up, marred by regulatory concerns over security and privacy, as well as a significant delay in adopting deeptech innovations.

“The proliferation of artificial intelligence in the aviation industry is unfortunately still rather uncommon, which can be attributed to privacy and security-sensitive regulations,” remarks Gromov.

Embracing embedded AI: the ‘game-changer’

The common association of artificial intelligence with applications like ChatGPT only scratches the surface of the diverse array of AI-supported automation and assistance systems. While large language models effectively address the informational needs of travellers by increasing efficiency and accessibility, the operational requirements in aviation demand a faster and more specialised AI. The conventional server and Cloud-based AI systems, including ChatGPT, often fall short in real-time responsiveness and computational efficiency for machine-level problem-solving. However, embedded AI steps in as a localised, autonomous solution that is capable of instant data processing without network dependency. It bypasses privacy concerns while enabling data analyses and decision-making previously deemed impossible. While an embedded AI processes the most extensive data sets in milliseconds, a more profound and deeper analysis becomes possible, identifying patterns or connections previously undetectable. This shift towards embedded AI marks the next megatrend in technological evolution: autonomous, intelligent machines independent of central network connectivity.  

A flight to 2030

Picture entering an airport in 2030: every gate, counter, and security checkpoint is streamlined through autonomous vision and LiDAR sensors (known as Time-of-Flight sensors), facilitating smart queue management, instantaneous counter operations, and predictive flow control. Embedded AI ensures privacy, as data is processed directly on the chip itself without being transmitted to large servers, thereby eliminating data sharing concerns. This enables real-time processing and decision-making. It also significantly increases efficiency through intelligent user interaction with automated check-ins and baggage drops via gesture-, voice-, or object recognition. While airports struggle with shortages of professional staff as well as heightened hygiene standards, the technology can address all these challenges.

Beyond the check-in, embedded AI also transforms security processes. Advanced NIR (Near InfraRed) sensors and intelligent screening processes deepen threat detection and can even identify individuals’ stress levels significantly more accurately than contemporary XRAY-CT detectors.  

Embedded AI systems promise unmatched precision and speed in baggage handling in larger airports’ unfathomable underground transport and logistic systems. Furthermore, drives and all other technological components of the conveyor belt are equipped with predictive maintenance capabilities to pre-emptively address system wear and future breakdowns, saving time, and reducing labour costs. Embedded AI is also integrated into vehicles used for logistic transportation, not only enabling the detection of future malfunctions but also an automated navigation and obstacle recognition.  

Within the aircraft, real-time sensors enable immediate adjustments to fuel efficiency and optimal engine performance. They are practically failure-safe as preventive maintenance ensures operational reliability while also allowing for the most resource-efficient maintenance cycles. Additionally, intelligent user interaction can redefine the flight experience both for pilots as well as passengers.  

While ‘flying taxis’ might be less of an unrealistic illusion than one might assume, even here, embedded AI will be crucial. Through predictive maintenance drives must be monitored by intelligent sensors to ensure operational reliability, while autonomous support functions must run on board to guarantee passengers’ safety. Either way, embedded AI will undoubtedly play a pivotal role in revolutionising the aviation industry.