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

TL;DR

This paper introduces RAMP-CNN, a neural network that improves automotive radar object recognition by combining multiple lower-dimensional models, achieving high accuracy and robustness in various conditions including nighttime.

Contribution

The paper presents a novel multi-perspective CNN architecture that simplifies 4D radar data processing while maintaining high performance in object detection and recognition.

Findings

Outperforms prior methods in recall and precision across tests

Works robustly under nighttime and severe conditions

Achieves near upper-bound performance with lower complexity

Abstract

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.