GAN Based Method for Labeled Image Augmentation in Autonomous Driving Deep learning models in Autonomous Driving perception tasks commonly use supervised learning methods and thus highly depend on labeled data. Training with more labeled data tends to bring better results, which highlights the meaning of data augmentation. Currently there are two difficulties when doing data augmentation. Firstly, it is time consuming to manually label the collected raw data. The second issue is that the diversity of a dataset is limited by the collection environment and time. In this paper, we proposed to use the current state of the art Multimodal Unsupervised ImagetoImage Translation (MUNIT) to generate synthesized images from labeled data. One of the benefits is that the generated data are automated labeled since they share the same ground truth with the raw data. Then we used the augmentation dataset to do different tasks including drivable area detection and object detection to prove that the data could be used to improve the performance of convolution neural networks (CNNs). We also designed an auto labelling tool that people could do labelling with the help of the improved CNN. The whole process is like a close loop that finishes labelling tasks while making progresses by itself. Generally speaking, our approach introduces an auto labelling pipeline based on unsupervised imagetoimage translation to increase the amount and diversity of labeled data.

Unconstrained Road Marking Recognition with Generative Adversarial Networks Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often overdependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates highquality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a classconditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.