# Histogram-based Deep Learning for Automotive Radar

arXiv: https://arxiv.org/abs/2303.02975 · HTML: https://mlsanity.com/paper/2303.02975/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users](https://mlsanity.com/paper/2105.00363/) (2021)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar](https://mlsanity.com/paper/2205.02111/) (2022)
- [DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections](https://mlsanity.com/paper/2010.09273/) (2020)
- [DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification](https://mlsanity.com/paper/2202.08519/) (2022)

## Abstract (arXiv)

There are various automotive applications that rely on correctly interpreting point cloud data recorded with radar sensors. We present a deep learning approach for histogram-based processing of such point clouds. Compared to existing methods, the design of our approach is extremely simple: it boils down to computing a point cloud histogram and passing it through a multi-layer perceptron. Our approach matches and surpasses state-of-the-art approaches on the task of automotive radar object type classification. It is also robust to noise that often corrupts radar measurements, and can deal with missing features of single radar reflections. Finally, the design of our approach makes it more interpretable than existing methods, allowing insightful analysis of its decisions.