TY - JOUR A1 - Saad, Kmeid A1 - Schneider, Stefan-Alexander T1 - Virtual Integration Platforms and Sensor Models In the Context of ADAS Functions Performance Testing & Validation T2 - Transportation Research Procedia N2 - 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 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. Y1 - 2019 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/739 SN - 2352-1465 VL - 41. IS - 41 SP - 799 EP - 801 PB - Elsevier CY - Amsterdam ER -