For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2020/07/certifying-neural-networks-for-autonomous-flight-a-presentation-from-daedalean-ai/ For more information about edge AI and vision, please visit: http://www.edge-ai-vision.com David Haber, Head of Deep Learning at Daedalean AI, delivers the presentation “Certifying Neural Networks for Autonomous Flight” at the Edge AI and Vision Alliance’s May 2020 Member Briefing meeting. Deep neural networks have demonstrated impressive performance on visual recognition tasks relevant to the operation of airborne vehicles such as autonomous drones and personal electric air-taxis. For this reason, their application to visual problems, including object detection and image segmentation, is promising (and even necessary) for autonomous flight. The downside of this increased model performance is higher complexity, which poses challenges related to interpretability, explainability and (eventually) the certification of safety-critical aviation applications. For example, how do you convince the regulators (and ultimately the public) that your model is robust to adversarial attacks? How do you prove that your training and testing datasets are exhaustive? How do you test edge cases when your input space is infinite and any mistake is potentially fatal? Over the last year, Daedalean AI has partnered with EASA (the European Union Aviation Safety Agency) to explore how existing regulations around safety-critical applications can be adapted to encompass modern machine-learning techniques. In this talk, Haber discusses the different stages of a typical machine learning pipeline as they relate to design choices for neural network architectures, desirable properties for training and test datasets, model generalizability, and how to protect against adversarial attacks. He also considers the opportunities, challenges and learning that may apply more generally when building AI for safety-critical applications in the future.

1. Certifying Neural Networks for
Autonomous Flight
Edge AI and Vision Alliance — 13 May 2020
David Haber
@davhab - dh@ddln.ai

3. Non-commercial Commercial
Pilot-related 75.3% 63.2%
Mechanical 15.0% 25.0%
Other/unknown 9.7% 11.8%
Fixed-Wing GA Aviation Accidents
Source: 26th Joseph T. Nall Report, GA Accidents in 2014, Aopa Air Safety

6. No autonomy without AI.
But we cannot compromise safety!

7. “Concepts of Design Assurance for Neural
Networks”
Propose a first set of guidelines for NN-based systems facilitating backward &
future compatibility with the existing regulatory framework.
+

8. Available at https://www.easa.europa.eu/ai.

9. Learning Process

10. 10-9
Your system cannot fail in 109 flight hours (on average).

12. Performance Guarantees

13. Performance Guarantees
One can mathematically prove that machine learning models generalize.

14. First Step
Identify input probability space :
“All 612 x 512 RGB images you could possibly
ever record over Switzerland”
Easy to talk about, hard to describe
mathematically. Hausen Airport (LSZN), Switzerland

15. Explicit Operating Parameters

16. Show Dtrain, Dval, Dtest are independently distributed in the operating space OS.
Data Distribution

17. ...not enough!
Show Dtrain, Dval, Dtest are independently sampled from the input space and independent from
each other.
Build an input distribution discriminator
Show Dtrain, Dval, Dtest are independently distributed in the operating space OS.
Data Distribution

18. Algorithm and Model Robustness

19. Our Team
Strong software/aerospace pedigree:
- Computer Vision, Machine Learning, Robotics, Control
- Experience with safety critical software development
Aviation experience:
- Multiple PPL(H)s
- Multiple PPL(A)s
- Gliding License
Focus on regulatory approval:
- Dedicated regulatory compliance team
- Aviation certification specialist on advisory board
We are based in Zürich

20. Thanks
easa.europa.eu/ai
dh@ddln.ai