# DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification

arXiv: https://arxiv.org/abs/2202.08519 · HTML: https://mlsanity.com/paper/2202.08519/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles](https://mlsanity.com/paper/1905.11703/) (2019)
- [A Complete Derivation Of The Association Log-Likelihood Distance For Multi-Object Tracking](https://mlsanity.com/paper/1508.04124/) (2015)

## Built on by (most cited first, 7 of 7)

- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Radar Spectra-Language Model for Automotive Scene Parsing](https://mlsanity.com/paper/2406.02158/) (2024)
- [Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators](https://mlsanity.com/paper/2303.07586/) (2023)
- [Histogram-based Deep Learning for Automotive Radar](https://mlsanity.com/paper/2303.02975/) (2023)
- [Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search](https://mlsanity.com/paper/2506.21772/) (2025)

## Abstract (arXiv)

Automated vehicles need to detect and classify objects and traffic participants accurately. Reliable object classification using automotive radar sensors has proved to be challenging. We propose a method that combines classical radar signal processing and Deep Learning algorithms. The range-azimuth information on the radar reflection level is used to extract a sparse region of interest from the range-Doppler spectrum. This is used as input to a neural network (NN) that classifies different types of stationary and moving objects. We present a hybrid model (DeepHybrid) that receives both radar spectra and reflection attributes as inputs, e.g. radar cross-section. Experiments show that this improves the classification performance compared to models using only spectra. Moreover, a neural architecture search (NAS) algorithm is applied to find a resource-efficient and high-performing NN. NAS yields an almost one order of magnitude smaller NN than the manually-designed one while preserving the accuracy. The proposed method can be used for example to improve automatic emergency braking or collision avoidance systems.