Radar Translation Network Between Sunny and Rainy Domains by Combination of KP-Convolution and CycleGAN

Abstract

Recently, research on autonomous driving has focused on the advent of various deep learning algorithms. The main sensors for autonomous driving include cameras, LiDAR, and radar, but these algorithms primarily focus on image and LiDAR data. This is because radar data is limited compared to image and LiDAR data. To address the lack of data problem, GAN-based translation methods have been proposed. However, these methods also focus only on image and LiDAR data, such as day-to-night translation or sunny-to-adverse weather translation. Since radar data differs depending on radar sensors and radar points are too sparse to learn patterns compared to LiDAR, translation with radar data is a challenging task. Radar is usually utilized as a sensor that is nearly unaffected by the weather. However, it has been confirmed through JARI data collected by us that rain has a negative effect. CycleGAN is useful for data translation in traffic scenes where pair data is difficult to acquire, since CycleGAN is a network specialized in style translation. KP-Convolution is a module specialized in feature extraction of points while maintaining location information. Therefore, we propose a radar translation network between sunny and rainy domains by combining KP-Convolution and CycleGAN. In this process, we address the adverse effects of radar data by rain, establishing the training format of radar data, KP-Convolution which can learn patterns despite a small number of points, and CycleGAN which is the basis of the translation method.

Keywords

• Radar translation
• autonomous driving
• driving simulator
• weather condition
• deep learning
• data-driven approach