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AI-based autonomous flight control for the aircraft of today and electric VTOLs of tomorrow

Periodic Reporting for period 1 - Raven (AI-based autonomous flight control for the aircraft of today and electric VTOLs of tomorrow)

Reporting period: 2019-11-01 to 2020-10-31

Autonomous flight has the potential to address 3 important challenges that the aviation industry is facing today & will face in the nearest future:
1) Despite the stellar safety record of aviation, pilots are humans & they make mistakes—75% of general aviation accidents in the US are caused by human error
2) Piloting is a very complex activity & requires a lot of training
3) There is already a shortage of qualified pilots globally & it will only get worse
Raven is an airframe-agnostic solution targeted at fly-by-wire aircraft of today (fixed-wing airplanes & helicopters) & tomorrow (eVTOLs & others). It is a combination of custom-designed neural networks, core avionics software, computer vision algorithms & special-purpose aviation-grade hardware.
We are developing a special kind of neural networks that are deterministic by design, built specifically for the purposes of certification in aviation. They learn while they are getting trained, they don’t learn when they are piloting aircraft. They run in the dedicated environment & on certifiable hardware that we are creating. This is a major innovation over the generic state-of-the-art in AI.
The main objective of this EIC Accelerator H2020 project is to industrialize Raven so that it performs better than a human pilot on any measurable dimension and could pass the certification tests currently devised for human pilots.
From the start date of the project (1st of November 2019) to the end date of the first period (31st of October 2020), the following activities have been carried out:

1) We have completed the creation of a simulation engine. As of now, we have generated 625 scenarios (encounters). During the reported period, we completed 10 hours of real test flights on fixed-wing and helicopters and 15 hours on unmanned aircraft which has brought us to 600+ flight records in our database in total with more than 102 hours of flight data (both UAV and manned vehicles). We prepared the infrastructure to train and experiment with training neural networks giving us the scale and flexibility needed. The Spotter neural networks that had been trained on synthetic data generated in simulation environments were verified against real-life footage, and, additionally, in real-time and onboard an aircraft.

2) During the reported period, we have successfully specified and tested the various camera components, which comprise the camera design for Raven. We selected the necessary components (CPUs, GPUs, peripherals, etc.) to design and build a ruggedized system that can execute in real-time Raven neural networks. The selected components were integrated into a custom enclosure, connected to several cameras and several mounting options were prepared for the trial cases.

3) We had an opportunity to start one of the tasks regarding trials earlier hence we managed to complete a trial case with eVTOL aircraft
For future, higher-level of certification (DAL-C) we partnered with an established avionics manufacturer (outside of this project):
https://evtol.com/news/avidyne-daedalean-detect-avoid-system/

4) In the reported period, we have been ensuring the compliance of our system with the certification requirements. We have also collaborated with EASA (The European Union Aviation Safety Agency ) mainly investigating the existing regulations standards, and major reports on the use of machine learning-based systems in safety-critical systems.

5) During the reported period we have updated and further developed the communication strategy containing the objectives and action list specifically on publishing and promoting relevant project results, attracting the target audiences’ attention to Raven. We have successfully developed our social media accounts, launched a new website, and also participated in industry-related events. This part of the plan was, quite naturally, disrupted by the Covid-19 pandemic however, by May 2020 all the organizing committees adapted to the situation and converted their events into online versions. So, instead of speaking on real events, we presented on virtual ones.

6) We have been effectively managing the project and during the first year of the project, our team has concluded the following tasks:
- Communication with the EC Project Officer has been conducted via project dedicated email,
- Setting up and running project finances, including the financial accounting system to follow the planned budget
- Setting up and holding regular meetings with the work package leaders to follow up on the work progress in the project, preparing the deliverables to be submitted in the EC online platform and monitoring the achievement of the milestones,
- Project Management and Quality Assurance plan have been created in order to collect all relevant plans and activities regarding the Raven project.
- Risks were monitored on a regular, monthly basis and we have stayed vigilant to the inherent risks of the project and continuously worked on risk mitigation,
- Project documentation has been prepared and maintained in the project documentation repository especially related to deliverable, periodic reports, procedures, and resource allocation
- Ensuring gender balance in the project - as a company, we have 4 female engineers in senior positions. Our head of product management & lead pilot is also female.
We are developing Raven to augment pilots in aircraft cockpits, reduce their workload & further improve aircraft safety. We want the Raven components to extend pilot capabilities with an additional degree of flight automation & provide pilots with high-quality advice during critical & emergency situations. Raven is the next logical step in flight automation & reduction of labor in the cockpits. This will result in a fully autonomous passenger eVTOL. The need for reducing pilot workload & resulting fatigue will become even more apparent as new generation planes start flying ultra-long routes. We are developing a system that can show the same holistic awareness of all flight-related issues as a human pilot.
With Raven, we will be able to make the pilots even more efficient. With Raven executing flights autonomously, an experienced pilot can be remotely supervising dozens of flights at the same time, interfering only when required.
This news article on Daedalean summarises our progress, including project RAVEN, very well: https://evtol.news/news/swiss-startup-artificial-intelligence-uam