# Deep Open Space Segmentation using Automotive Radar

arXiv: https://arxiv.org/abs/2004.03449 · HTML: https://mlsanity.com/paper/2004.03449/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2310.02260/) (2023)
- [Bootstrapping Autonomous Driving Radars with Self-Supervised Learning](https://mlsanity.com/paper/2312.04519/) (2023)
- [Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar](https://mlsanity.com/paper/2308.05478/) (2023)
- [PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain](https://mlsanity.com/paper/2103.03387/) (2021)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics](https://mlsanity.com/paper/2303.04302/) (2023)
- [L2RDaS: Synthesizing 4D Radar Tensors for Model Generalization via Dataset Expansion](https://mlsanity.com/paper/2503.03637/) (2025)
- [ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals](https://mlsanity.com/paper/2209.12940/) (2022)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation](https://mlsanity.com/paper/2604.04490/) (2026)
- [Revisiting Radar Perception With Spectral Point Clouds](https://mlsanity.com/paper/2604.08282/) (2026)

## Abstract (arXiv)

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.