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Abstract
In the next few years, modern vehicles will integrate the next level of Advanced Driver-Assistance Systems (ADAS) such as Light Detection and Ranging (LiDAR) which will be one of the key enabler for autonomous driving. Autonomous driving will be in charge for controlling the vehicle without any inputs of a passenger. This requires highly robust and reliable components and systems. In general, mechanical defects are detectable through vibrations or noise changes but for semiconductor components these capabilities are not available. Semiconductor components fail silently and abrupt without any prior information and this could lead to fatal accidents when systems fail during autonomous driving phases. In this publication, we are introducing a novel state-of-health monitoring system for automotive LiDAR system that is capable to economically record the component history and automatically processes these data to the statistical Failure-In-Time (FIT) Rate that is primarily used in the Automotive domain such as in the "ISO 26262 - Road Vehicle Safety" standard.
Originalsprache | englisch |
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Titel | 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings |
Seitenumfang | 6 |
ISBN (elektronisch) | 9781728148427 |
DOIs | |
Publikationsstatus | Veröffentlicht - März 2020 |
Veranstaltung | 15th IEEE Sensors Applications Symposium: SAS 2020 - Kuala Lumpur, Malaysia Dauer: 9 März 2020 → 11 März 2020 |
Publikationsreihe
Name | 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings |
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Konferenz
Konferenz | 15th IEEE Sensors Applications Symposium |
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Kurztitel | SAS 2020 |
Land/Gebiet | Malaysia |
Ort | Kuala Lumpur |
Zeitraum | 9/03/20 → 11/03/20 |
ASJC Scopus subject areas
- Instrumentierung
- Maschinelles Sehen und Mustererkennung
- Angewandte Informatik
Fingerprint
Untersuchen Sie die Forschungsthemen von „Enabling Live State-of-Health Monitoring for a Safety-Critical Automotive LiDAR System“. Zusammen bilden sie einen einzigartigen Fingerprint.-
Hardware/Software-Codesign
Steger, C., Seifert, C., Stelzer, P., Fiala, G. & Basic, F.
1/01/95 → …
Projekt: Arbeitsgebiet
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PRYSTINE - Programmierbare Systeme für die Intelligenz in Automobilen
Steger, C., Strasser, A. & Stelzer, P.
1/05/18 → 30/04/21
Projekt: Forschungsprojekt