Lane Segmentation Data Augmentation for Heavy Rain Sensor Blockage using Realistically Translated Raindrop Images and CARLA Simulator

Abstract

Lane segmentation and Lane Keeping Assist System (LKAS) play a vital role in autonomous driving. While deep learning technology has significantly improved the accuracy of lane segmentation, real-world driving scenarios present various challenges. In particular, heavy rainfall not only obscures the road with sheets of rain and fog but also creates water droplets on the windshield or lens of the camera that affects the lane segmentation performance. There may even be a false positive problem in which the algorithm incorrectly recognizes a raindrop as a road lane. Collecting heavy rain data is challenging in real-world settings, and manual annotation of such data is expensive. In this research, we propose a realistic raindrop conversion process that employs a contrastive learning-based Generative Adversarial Network (GAN) model to transform raindrops randomly generated using Python libraries. In addition, we utilize the attention mask of the lane segmentation model to guide the placement of raindrops in training images from the translation target domain (real Rainy-Images). By training the ENet-SAD model using the realistically Translated-Raindrop images and lane ground truth automatically extracted from the CARLA Simulator, we observe an improvement in lane segmentation accuracy in Rainy-Images. This method enables training and testing of the perception model while adjusting the number, size, shape, and direction of raindrops, thereby contributing to future research on autonomous driving in adverse weather conditions. IEEE