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Gustavsson, Joakim
Publications (4 of 4) Show all publications
Harisubramanyabalaji, S. P., ur Réhman, S., Nyberg, M. & Gustavsson, J. (2018). Improving image classification robustness using predictive data augmentation. In: Workshops: ASSURE, DECSoS, SASSUR, STRIVE, and WAISE 2018 co-located with 37th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2018: . Paper presented at Workshops: ASSURE, DECSoS, SASSUR, STRIVE, and WAISE 2018 co-located with 37th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2018, Västerås, Sweden, 18 September 2018 through 21 September 2018 (pp. 548-561). Springer
Open this publication in new window or tab >>Improving image classification robustness using predictive data augmentation
2018 (English)In: Workshops: ASSURE, DECSoS, SASSUR, STRIVE, and WAISE 2018 co-located with 37th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2018, Springer, 2018, p. 548-561Conference paper, Published paper (Refereed)
Abstract [en]

Safer autonomous navigation might be challenging if there is a failure in sensing system. Robust classifier algorithm irrespective of camera position, view angles, and environmental condition of an autonomous vehicle including different size & type (Car, Bus, Truck, etc.) can safely regulate the vehicle control. As training data play a crucial role in robust classification of traffic signs, an effective augmentation technique enriching the model capacity to withstand variations in urban environment is required. In this paper, a framework to identify model weakness and targeted augmentation methodology is presented. Based on off-line behavior identification, exact limitation of a Convolutional Neural Network (CNN) model is estimated to augment only those challenge levels necessary for improved classifier robustness. Predictive Augmentation (PA) and Predictive Multiple Augmentation (PMA) methods are proposed to adapt the model based on acquired challenges with a high numerical value of confidence. We validated our framework on two different training datasets and with 5 generated test groups containing varying levels of challenge (simple to extreme). The results show impressive improvement by$$\approx $$ 5–20% in overall classification accuracy thereby keeping their high confidence.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11094
Keywords
Convolutional neural network, Predictive augmentation, Real-time challenges, Safety-risk assessment, Traffic sign classification
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-238414 (URN)10.1007/978-3-319-99229-7_49 (DOI)000458807000049 ()2-s2.0-85053899561 (Scopus ID)9783319992280 (ISBN)
Conference
Workshops: ASSURE, DECSoS, SASSUR, STRIVE, and WAISE 2018 co-located with 37th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2018, Västerås, Sweden, 18 September 2018 through 21 September 2018
Note

QC 20181106. QC 20191105

Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2024-03-18Bibliographically approved
Westman, J., Nyberg, M., Gustavsson, J. & Gurov, D. (2017). Formal Architecture Modeling of Sequential Non-Recursive C Programs. Science of Computer Programming, 146, 2-27
Open this publication in new window or tab >>Formal Architecture Modeling of Sequential Non-Recursive C Programs
2017 (English)In: Science of Computer Programming, ISSN 0167-6423, E-ISSN 1872-7964, Vol. 146, p. 2-27Article in journal (Refereed) Published
Abstract [en]

To manage the complexity of C programs, architecture models are used as high-level descriptions, allowing developers to understand, assess, and manage the C programs without having to understand the intricate complexity of the code implementations. However, for the architecture models to serve their purpose, they must be accurate representations of the C programs. In order to support creating accurate architecture models, the present paper presents a mapping from the domain of sequential non-recursive C programs to a domain of formal architecture models, each being a hierarchy of components with well-defined interfaces. The hierarchically organized components and their interfaces, which capture both data and function call dependencies, are shown to both enable high-level assessment and analysis of the C program and provide a foundation for organizing and expressing specifications for compositional verification.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
C program, Modeling, Architecture, Component, Interfaces
National Category
Mechanical Engineering
Research subject
Machine Design
Identifiers
urn:nbn:se:kth:diva-192375 (URN)10.1016/j.scico.2017.03.007 (DOI)000407402500002 ()2-s2.0-85017154649 (Scopus ID)
Projects
ESPRESSO
Funder
Vinnova, 2011-04446
Note

QC 20170814

Available from: 2016-09-11 Created: 2016-09-11 Last updated: 2024-03-18Bibliographically approved
Mohan, N., Törngren, M., Izosimov, V., Kaznov, V., Roos, P., Svahn, J., . . . Nesic, D. (2016). Challenges in architecting fully automated driving; With an emphasis on heavy commercial vehicles. In: Proceedings - 2016 Workshop on Automotive Systems/Software Architectures, WASA 2016: . Paper presented at Workshop on Automotive Systems/Software Architectures, WASA 2016, Venice, Italy, 5 April 2016 through (pp. 2-9). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Challenges in architecting fully automated driving; With an emphasis on heavy commercial vehicles
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2016 (English)In: Proceedings - 2016 Workshop on Automotive Systems/Software Architectures, WASA 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 2-9Conference paper, Published paper (Refereed)
Abstract [en]

Fully automated vehicles will require new functionalities for perception, navigation and decision making - an Autonomous Driving Intelligence (ADI). We consider architectural cases for such functionalities and investigate how they integrate with legacy platforms. The cases range from a robot replacing the driver - with entire reuse of existing vehicle platforms, to a clean-slate design. Focusing on Heavy Commercial Vehicles (HCVs), we assess these cases from the perspectives of business, safety, dependability, verification, and realization. The original contributions of this paper are the classification of the architectural cases themselves and the analysis that follows. The analysis reveals that although full reuse of vehicle platforms is appealing, it will require explicitly dealing with the accidental complexity of the legacy platforms, including adding corresponding diagnostics and error handling to the ADI. The current fail-safe design of the platform will also tend to limit availability. Allowing changes to the platforms, will enable more optimized designs and fault-operational behaviour, but will require initial higher development cost and specific emphasis on partitioning and control to limit the influences of safety requirements. For all cases, the design and verification of the ADI will pose a grand challenge and relate to the evolution of the regulatory framework including safety standards.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
architecture, automotive, autonomy, commercial vehicles, dependability, full automation, functional safety, heavy vehicles, HGV, ISO 26262, modularity, platform migration, SAE L5, variability, verification
National Category
Embedded Systems
Identifiers
urn:nbn:se:kth:diva-194545 (URN)10.1109/WASA.2016.10 (DOI)000386759300002 ()2-s2.0-84978198875 (Scopus ID)978-150902571-8 (ISBN)
Conference
Workshop on Automotive Systems/Software Architectures, WASA 2016, Venice, Italy, 5 April 2016 through
Note

QC 20161031

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2024-03-15Bibliographically approved
Gustavsson, J. (2016). Verification Methodology for Fully Autonomous Heavy Vehicles. In: Proceedings - 2016 IEEE International Conference on Software Testing, Verification and Validation, ICST 2016: . Paper presented at 9th IEEE International Conference on Software Testing, Verification and Validation, ICST 2016, 10 April 2016 through 15 April 2016 (pp. 381-382). IEEE conference proceedings
Open this publication in new window or tab >>Verification Methodology for Fully Autonomous Heavy Vehicles
2016 (English)In: Proceedings - 2016 IEEE International Conference on Software Testing, Verification and Validation, ICST 2016, IEEE conference proceedings, 2016, p. 381-382Conference paper, Published paper (Refereed)
Abstract [en]

The introduction of fully autonomous vehicles posesa number of concerns regarding the safety and dependability ofvehicle operation. Best practice standards within the automotiveindustry rely on the driver operating the vehicle. With thetransition away from manual control, an increased emphasishas to be placed on verification during the vehicle developmentstages. The work presented within this paper aims to establisha framework for the various verification activities performedduring development, and their impact on the safety of the vehicle, as well as a set of guidelines for verification of the decision makingprocess of autonomous vehicles.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Keywords
Formal verification, Intelligent vehicles, Mathematical model, Road vehicles, System testing, Vehicle safety, Crashworthiness, Decision making, Intelligent vehicle highway systems, Mathematical models, Safety testing, Software testing, Vehicles, Autonomous Vehicles, Fully-autonomous vehicles, Safety and dependability, Verification activities, Verification methodology, Verification
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-197131 (URN)10.1109/ICST.2016.42 (DOI)000391252900038 ()2-s2.0-84983371297 (Scopus ID)9781509018260 (ISBN)
Conference
9th IEEE International Conference on Software Testing, Verification and Validation, ICST 2016, 10 April 2016 through 15 April 2016
Note

QC 20161214

Available from: 2016-12-14 Created: 2016-11-30 Last updated: 2022-06-27Bibliographically approved
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