# RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

arXiv: https://arxiv.org/abs/2011.08981 · HTML: https://mlsanity.com/paper/2011.08981/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 30 of 30)

- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals](https://mlsanity.com/paper/2303.16940/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [A recurrent CNN for online object detection on raw radar frames](https://mlsanity.com/paper/2212.11172/) (2022)
- [TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2310.02260/) (2023)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach](https://mlsanity.com/paper/2109.13666/) (2021)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data](https://mlsanity.com/paper/2406.10600/) (2024)
- [4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks](https://mlsanity.com/paper/2404.05307/) (2024)
- [SIRA: Scalable Inter-frame Relation and Association for Radar Perception](https://mlsanity.com/paper/2411.02220/) (2024)
- [Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection](https://mlsanity.com/paper/2512.22972/) (2025)
- [End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation](https://mlsanity.com/paper/2312.09790/) (2023)
- [RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather](https://mlsanity.com/paper/2602.19994/) (2026)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [REXO: Indoor Multi-View Radar Object Detection via 3D Bounding Box Diffusion](https://mlsanity.com/paper/2511.17806/) (2025)
- [4D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather](https://mlsanity.com/paper/2606.00416/) (2026)
- [Learning to Detect Open Carry and Concealed Object with 77GHz Radar](https://mlsanity.com/paper/2111.00551/) (2021)
- [Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels](https://mlsanity.com/paper/2311.18316/) (2023)
- [The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar](https://mlsanity.com/paper/2509.19644/) (2025)
- [Temporal Micro-Doppler Spectrogram-based ViT Multiclass Target Classification](https://mlsanity.com/paper/2511.11951/) (2025)
- [Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model](https://mlsanity.com/paper/2601.06228/) (2026)
- [Leveraging Transformer Decoder for Automotive Radar Object Detection](https://mlsanity.com/paper/2601.13386/) (2026)
- [mmAnomaly: Leveraging Visual Context for Robust Anomaly Detection in the Non-Visual World with mmWave Radar](https://mlsanity.com/paper/2604.00382/) (2026)
- [RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling](https://mlsanity.com/paper/2604.10183/) (2026)
- [Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2606.31609/) (2026)

## Abstract (arXiv)

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception. In this paper, we propose a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts the location and class of objects based on further processing of the range-velocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D convolutional neural networks (NN), we propose to combine several lower-dimension NN models within our RAMP-CNN model that nonetheless approaches the performance upper-bound with lower complexity. The extensive experiments show that the proposed RAMP-CNN model achieves better average recall and average precision than prior works in all testing scenarios. Besides, the RAMP-CNN model is validated to work robustly under nighttime, which enables low-cost radars as a potential substitute for pure optical sensing under severe conditions.