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.
|First Name||Last Name||Title|
|Joakim||Lindén||Industrial Doctoral Student|
A novel method for detecting UAVs using parallel neural networks with re-inference (Sep 2022) Hubert Stepien , Martin Bilger , Håkan Forsberg, Billy Lindgren , Johan Hjorth 33rd Congress of the International Council of the Aeronautical Sciences (ICAS 2022)
Curating Datasets for Visual Runway Detection (Oct 2021) Joakim Lindén, Håkan Forsberg, Josef Haddad , Emil Tagebrand , Erasmus Cedernaes , Emil Gustafsson Ek , Masoud Daneshtalab The 40th Digital Avionics Systems Conference (DASC'2021)
Challenges in Using Neural Networks in Safety-Critical Applications (Oct 2020) Håkan Forsberg, Johan Hjorth, Masoud Daneshtalab, Joakim Lindén, Torbjörn Månefjord The 39th Digital Avionics Systems Conference (DASC'2020)
A software implemented comprehensive soft error detection method for embedded systems (Sep 2020) Seyyed Amir Asghari , Mohammadreza Binesh Marvasti , Masoud Daneshtalab Elsevier journal of Microprocessors and Microsystems (MICPRO)
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators Mahdi Taheri , Mohammad Riazati, Mohammad Ahmadilivani , Maksim Jenihhin , Masoud Daneshtalab, Jaan Raik , Mikael Sjödin, Björn Lisper International Symposium on Quality Electronic Design (ISQED 2023)
DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin European Test Symposium 2023 (IEEE ETS)
|Saab AB, Avionics Systems||Industrial|