A Generative Adversarial Network-based Method for LiDAR-Assisted Radar Image Enhancement

TL;DR

This paper introduces a GAN-based method to enhance low-resolution radar images in autonomous vehicles by leveraging high-resolution LiDAR data, significantly improving environmental detail depiction under adverse weather conditions.

Contribution

It proposes a novel GAN framework that uses LiDAR data as ground truth to enhance radar images, enabling better object detection in autonomous vehicle applications.

Findings

Enhanced radar images with clearer object details

Effective under adverse weather conditions

Quantitative improvements over raw radar images

Abstract

This paper presents a generative adversarial network (GAN) based approach for radar image enhancement. Although radar sensors remain robust for operations under adverse weather conditions, their application in autonomous vehicles (AVs) is commonly limited by the low-resolution data they produce. The primary goal of this study is to enhance the radar images to better depict the details and features of the environment, thereby facilitating more accurate object identification in AVs. The proposed method utilizes high-resolution, two-dimensional (2D) projected light detection and ranging (LiDAR) point clouds as ground truth images and low-resolution radar images as inputs to train the GAN. The ground truth images were obtained through two main steps. First, a LiDAR point cloud map was generated by accumulating raw LiDAR scans. Then, a customized LiDAR point cloud cropping and projection method was employed to obtain 2D projected LiDAR point clouds. The inference process of the proposed method relies solely on radar images to generate an enhanced version of them. The effectiveness of the proposed method is demonstrated through both qualitative and quantitative results. These results show that the proposed method can generate enhanced images with clearer object representation compared to the input radar images, even under adverse weather conditions.