Adaptive System for Autonomous Driving

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Avoiding faults in systems is of uttermost importance during system development. In case of autonomous systems this requirement becomes more important because of the fact that there is no human in the loop that can take over control after a fault. In this paper, we discuss a methodology allowing to implement a system that reacts on faults in a smart way using rules specifying possible sequences of actions a system can take for reaching a goal. In the methodology the selection of actions is done automatically aiming at reaching the goal state. The methodology allows a system to adapt in cases where action execution fails. Besides the underlying foundations, we show the applicability of the methodology using an example from the autonomous driving domain considering different sensors for obstacle detection. For this case study the methodology leads to a substantial improvement of availability compared to a random selection approach.

Originalspracheenglisch
TitelProceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten519-525
Seitenumfang7
ISBN (Print)9781538678398
DOIs
PublikationsstatusVeröffentlicht - 9 Aug 2018
Veranstaltung18th IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018 - Lisbon, Portugal
Dauer: 16 Jul 201820 Jul 2018

Konferenz

Konferenz18th IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018
LandPortugal
OrtLisbon
Zeitraum16/07/1820/07/18

Fingerprint

Adaptive systems
Availability
Sensors

Schlagwörter

    ASJC Scopus subject areas

    • Software
    • !!Safety, Risk, Reliability and Quality

    Dies zitieren

    Wotawa, F., & Zimmermann, M. (2018). Adaptive System for Autonomous Driving. in Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018 (S. 519-525). [8432021] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/QRS-C.2018.00093

    Adaptive System for Autonomous Driving. / Wotawa, Franz; Zimmermann, Martin.

    Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018. Institute of Electrical and Electronics Engineers, 2018. S. 519-525 8432021.

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Wotawa, F & Zimmermann, M 2018, Adaptive System for Autonomous Driving. in Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018., 8432021, Institute of Electrical and Electronics Engineers, S. 519-525, Lisbon, Portugal, 16/07/18. https://doi.org/10.1109/QRS-C.2018.00093
    Wotawa F, Zimmermann M. Adaptive System for Autonomous Driving. in Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018. Institute of Electrical and Electronics Engineers. 2018. S. 519-525. 8432021 https://doi.org/10.1109/QRS-C.2018.00093
    Wotawa, Franz ; Zimmermann, Martin. / Adaptive System for Autonomous Driving. Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018. Institute of Electrical and Electronics Engineers, 2018. S. 519-525
    @inproceedings{8016975176574e01bdf16b0b3b3de4f6,
    title = "Adaptive System for Autonomous Driving",
    abstract = "Avoiding faults in systems is of uttermost importance during system development. In case of autonomous systems this requirement becomes more important because of the fact that there is no human in the loop that can take over control after a fault. In this paper, we discuss a methodology allowing to implement a system that reacts on faults in a smart way using rules specifying possible sequences of actions a system can take for reaching a goal. In the methodology the selection of actions is done automatically aiming at reaching the goal state. The methodology allows a system to adapt in cases where action execution fails. Besides the underlying foundations, we show the applicability of the methodology using an example from the autonomous driving domain considering different sensors for obstacle detection. For this case study the methodology leads to a substantial improvement of availability compared to a random selection approach.",
    keywords = "Reliability, Self-adaptation, Self-healing",
    author = "Franz Wotawa and Martin Zimmermann",
    year = "2018",
    month = "8",
    day = "9",
    doi = "10.1109/QRS-C.2018.00093",
    language = "English",
    isbn = "9781538678398",
    pages = "519--525",
    booktitle = "Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018",
    publisher = "Institute of Electrical and Electronics Engineers",
    address = "United States",

    }

    TY - GEN

    T1 - Adaptive System for Autonomous Driving

    AU - Wotawa, Franz

    AU - Zimmermann, Martin

    PY - 2018/8/9

    Y1 - 2018/8/9

    N2 - Avoiding faults in systems is of uttermost importance during system development. In case of autonomous systems this requirement becomes more important because of the fact that there is no human in the loop that can take over control after a fault. In this paper, we discuss a methodology allowing to implement a system that reacts on faults in a smart way using rules specifying possible sequences of actions a system can take for reaching a goal. In the methodology the selection of actions is done automatically aiming at reaching the goal state. The methodology allows a system to adapt in cases where action execution fails. Besides the underlying foundations, we show the applicability of the methodology using an example from the autonomous driving domain considering different sensors for obstacle detection. For this case study the methodology leads to a substantial improvement of availability compared to a random selection approach.

    AB - Avoiding faults in systems is of uttermost importance during system development. In case of autonomous systems this requirement becomes more important because of the fact that there is no human in the loop that can take over control after a fault. In this paper, we discuss a methodology allowing to implement a system that reacts on faults in a smart way using rules specifying possible sequences of actions a system can take for reaching a goal. In the methodology the selection of actions is done automatically aiming at reaching the goal state. The methodology allows a system to adapt in cases where action execution fails. Besides the underlying foundations, we show the applicability of the methodology using an example from the autonomous driving domain considering different sensors for obstacle detection. For this case study the methodology leads to a substantial improvement of availability compared to a random selection approach.

    KW - Reliability

    KW - Self-adaptation

    KW - Self-healing

    UR - http://www.scopus.com/inward/record.url?scp=85052489707&partnerID=8YFLogxK

    U2 - 10.1109/QRS-C.2018.00093

    DO - 10.1109/QRS-C.2018.00093

    M3 - Conference contribution

    SN - 9781538678398

    SP - 519

    EP - 525

    BT - Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018

    PB - Institute of Electrical and Electronics Engineers

    ER -