Text

Statsvetenskap

Product and Production Development

Systemdesign i realtid

Robotics

Säkerhetskritisk teknik

Simulation and optimisation for future industrial applications (SOFIA)

Sustainable lifestyle and health from a public health perspective

Ubiquitous Computing

Algebra och Analysis with applications

Artificiell intelligens och intelligenta system

Komplexa inbyggda system i realtid

Cyber-Physical Systems Analysis

Tillförlitlig programvaruteknik

Energieffektivisering och minskning av utsläpp

Engineering Mathematics

Formell modellering och analys av inbäddade system

Heterogena system

Lärande och optimering

Modellbaserad konstruktion av inbäddade system

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

Planned completion

2022-12-31

Main financing

Vinnova

Collaboration partners

Saab AB, Avionics Systems

Project manager

Senior Lecturer

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.