Automotive Radar Super-Resolution Sensing with Deep Camera Fusion

Abstract

We consider the problem of generating automotive radar super-resolution maps from low-resolution radar maps and camera images. This problem is relevant in automotive driving for synthetic sensor data generation to support improved environmental perception. We propose a radar super-resolution sensing approach based on multimodal data fusion between low-resolution radar range-azimuth (RA) maps and aligned camera images. Our method employs a U-Net-based autoencoder architecture enhanced with visual features extracted from a pre-trained ResNet50 encoder, enabling the model to generate high-resolution RA maps that approximate ground truth radar data. We evaluate the proposed method on the RADIal and RaDICaL datasets, which cover diverse driving environments and radar configurations. Quantitative and qualitative results demonstrate that our approach outperforms a baseline model and prior state-of-the-art methods, particularly in resolving fine spatial details in scenarios with closely-spaced vehicles and pedestrians.