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Algebra and Analysis with applications

Artificial Intelligence och Intelligent Systems

Automated Software language and Software engineering

Complex Real-Time Embedded Systems

Engineering Mathematics

Heterogeneous systems - hardware software co-design

Real-Time Systems Design

Robotics

Software Testing Laboratory

Ubiquitous Computing

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 at MDH

Senior Lecturer

Håkan Forsberg

+4621101381

hakan.forsberg@mdh.se

Description of the project

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.

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