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Virtual Integration Platforms and Sensor Models In the Context of ADAS Functions Performance Testing & Validation

  • In the Automotive industry and especially in the ADAS domain, functions like “Vehicle Detection”, “Lane Detection” undergo a very costly and time-consuming validation process before their final deployment in the vehicle. After the completion of the development process, image based detection algorithms usually rely on huge data sets of previously recorded data for performance testing and validation. Though vision data sets like “KITTI Vision Benchmark Dataset” and others are currently available for public use, there still lies numerous requirements that need to be satisfied and steps that need to be followed in order to pave the way for a proper and meaningful use of the recorded data sets in the scope of image based function testing and validation. Using the publicly available recorded data may be in some cases a good starting point but as we all know sooner or later we will need a more customized/personalized recorded data sets that capture more precise and detailed specifications like the camera’s technical specifications or evenIn the Automotive industry and especially in the ADAS domain, functions like “Vehicle Detection”, “Lane Detection” undergo a very costly and time-consuming validation process before their final deployment in the vehicle. After the completion of the development process, image based detection algorithms usually rely on huge data sets of previously recorded data for performance testing and validation. Though vision data sets like “KITTI Vision Benchmark Dataset” and others are currently available for public use, there still lies numerous requirements that need to be satisfied and steps that need to be followed in order to pave the way for a proper and meaningful use of the recorded data sets in the scope of image based function testing and validation. Using the publicly available recorded data may be in some cases a good starting point but as we all know sooner or later we will need a more customized/personalized recorded data sets that capture more precise and detailed specifications like the camera’s technical specifications or even its mounting position in the car. Furthermore, depending on the image based function under investigation, recorded data should also reflect certain driving scenarios in specific environmental conditions (rain, snow, fog, at sun rise, at daytime, at night …) or specific driving parameters like, speed,acceleration, grip, car orientation, position in lane, etc. that that may be too hard to safety due to safety, financial restrictions or even time limitations.show moreshow less

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Metadaten
Author:Kmeid Saad, Stefan-Alexander SchneiderORCiDGND
DOI:https://doi.org/10.1016/j.trpro.2019.09.127
Identifier:2352-1465 OPAC HS OPAC extern
Parent Title (English):Transportation Research Procedia
Subtitle (English):International scientific conference on mobility and transport urban mobility : mobilTUM 2018 ; 13-14 June 2018, Munich, Germany
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Conference:mobilTUM 2018 ; 13-14 June 2018, Munich, Germany
Language:English
Date of Publication (online):2019/01/01
Year of first Publication:2019
Volume:41.
Issue:41
Number of pages:3 Seiten
First Page:799
Last Page:801
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP2: Mobilität
Publication Lists:Schneider, Stefan-Alexander
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Release Date:2021/04/07
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