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Master Thesis Development of an ML-Based Post-Processing Scene Classification for ADAS Development Using Continuously Evaluated Data
Leonberg
Aktualität: 07.11.2024
Anzeigeninhalt:
07.11.2024, Bosch-Gruppe
Leonberg
Master Thesis Development of an ML-Based Post-Processing Scene Classification for ADAS Development Using Continuously Evaluated Data
Aufgaben:
In recent years, Advanced Driver Assistance Systems (ADAS) such as Adaptive Cruise Control (ACC) and lane departure warning have become increasingly popular. However, these systems still have room for improvement in certain scenarios, which requires a systematic search for such scenes during the development phase. Unfortunately, this process requires expensive and time-consuming manual expert labelling to cluster and label these effects. To address this problem, in your Master thesis you will design a machine learning algorithm using continuous-valued input data from radar and camera to classify faulty scenarios in post-processing. Furthermore, you will test and optimize the algorithm using real data. Last but not least, you will integrate the algorithm into an existing validation framework.
Qualifikationen:
Education: Master studies in the field of Engineering or comparable Experience and Knowledge: in Python; skills in Deep Learning (Pytorch), driver assistance systems and sensors (radar, camera) are an advantage Personality and Working Practice: an independent, motivated and reliable person Languages: very good in English and basic in German