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Project: NXP: Design and automated optimization of DNNs for radar-based ADAS
Description
- Design and automated optimization of DNNs for radar-based ADAS (advanced driver assistance systems)
- Improving state of the art approaches on object detection, classification, and segmentation in radar spectrum and/or 'point cloud' data with neural network architectures;
- Leveraging radar-domain specifics to improve reliability or efficiency of the DNN;
- Leveraging ML and NN-design know-how from other domains (e.g., transformer models for computer vision) for Radar signal processing;
- Optimizing simultaneously the deployment on target hardware and the accuracy on task/dataset via e.g. Neural Architecture Search (NAS).
Contact the TU/e supervisor (Joaquin Vanschoren). Please note that final acceptance will depend on the availability of supervision bandwidth and daily supervision at TU/e, as well as NXP.
Details
- Supervisor
-
Joaquin Vanschoren
- External location
- NXP
- Interested?
- Get in contact