Deep Open Space Segmentation using Automotive Radar
This work addresses parking automation for automotive systems, but it is incremental as it applies existing deep segmentation models to a new radar dataset.
The paper tackled open space segmentation in parking scenarios using radar data and deep learning, achieving low memory usage and real-time processing speeds suitable for embedded deployment.
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations called SCORP was collected. Deep models are evaluated with various radar input representations. Our proposed approach achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment.