- Creating a data augmentation module for adverse weather conditions.
- Analyzing drawbacks of current adverse weather augmentation methods
- Data Augmentation through statistical analysis of actual precipitation and wet ground noise
- Validation of augmentation module through actual adverse weather data
- Development of network for noise point and object classification
- Development of deep learning-based semantic segmentation network which robust to adverse weather
- Developing a multi-head precipitation classifier using point features