Text

Produkt- och produktionsutveckling

Robotik

Stokastiska processer, statistik och finansmatematik

Säkerhetskritisk teknik

Teknisk matematik

Algebra och Analys med tillämpningar

Artificiell intelligens och intelligenta system

Energieffektivisering och minskning av utsläpp

Heterogena system

Komplexa inbyggda system i realtid

Learning, Inclusive education, School transitions – for All (LISA)

Medicinsk teknik

M-TERM - Mälardalen University Team of Educational Researchers in Mathematics

SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems

This project addresses design methods for the use of DNNs in airborne safety-critical systems.

Start

2019-09-01

Planerat avslut

2022-12-31

Huvudfinansiering

Vinnova

Samarbetspartners

Saab AB, Avionics Systems

Projektansvarig vid MDH

Universitetslektor

Håkan Forsberg

+4621101381

hakan.forsberg@mdh.se

Deep neural networks (DNNs) have shown to be very successful in several areas, e.g. for object detection in autonomous cars. DNNs may also be successful in airborne systems. One such possible application is guided landing. The enabling of safe landing in adverse weather conditions without full ground support from the instrument landing system, decreases aerospace greenhouse gas emissions as multiple landing attempts and aerospace congestion are mitigated. To land autonomously without support from ground infrastructure requires advanced airborne systems including algorithms for detecting the runway. These systems are safety-critical.

This project addresses design methods for the use of DNNs in airborne safety-critical systems. DNNs cannot rely on traditional design assurance techniques described in documents from certification authorities or standardization bodies. In this project, the research focus is on mitigation techniques for design errors in both hardware and software and for adversarial effects which can lead to system failures. The expected results are design methodologies and fault tolerant architectures for airborne safety-critical applications using neural networks.