Adversarial Robustness in Lane Detection For Autonomous Vehicles Using Generative Adversarial Networks
This research investigates the adversarial robustness of lane detection for Autonomous Vehicles (AVs) under challenging driving conditions using Generative Adversarial Networks (GANs). In this work, the term adversarial refers to the adversarial training mechanism of GANs and to robustness under naturally adverse driving conditions, particularly illumination variation, rather than to defence against deliberate pixel-level perturbation attacks such as FGSM or PGD. Lane detection is a crucial component for safe navigation, but it often fails under poor lighting or adverse weather. To solve this, a U-Net model is trained on the Berkeley DeepDrive (BDD100K) dataset as a baseline. Then, Conditional GAN (CGAN) is used with the Cityscapes dataset to learn the mapping between RGB images and lane masks, which improves structural consistency. To handle illumination changes, CycleGAN is used to simulate Day-to-Night and Night-to-Day translations using BDD100K datasets, creating a more diverse training set. Preprocessing involves resizing images to 512×512 to ensure training efficiency on limited GPU hardware. The experiments are conducted using TensorFlow in a GPU-accelerated environment. Results show that the U-Net + CycleGAN model achieves a Precision of 65.41% and an F1-Score of 63.79%, which outperforms previous studies. The CGAN model also shows high performance with 92.55% F1-Score. This research proves that using GANs for data augmentation and domain translation can enhance the adversarial robustness and reliability of lane detection systems in real-world scenarios.