  While supervised detection and classification frameworks in autonomous
driving require large labelled datasets to converge, Unsupervised Domain
Adaptation (UDA) approaches, facilitated by synthetic data generated from
photo-real simulated environments, are considered low-cost and less
time-consuming solutions. In this paper, we propose UDA schemes using
adversarial discriminative and generative methods for lane detection and
classification applications in autonomous driving. We also present Simulanes
dataset generator to create a synthetic dataset that is naturalistic utilizing
CARLA's vast traffic scenarios and weather conditions. The proposed UDA
frameworks take the synthesized dataset with labels as the source domain,
whereas the target domain is the unlabelled real-world data. Using adversarial
generative and feature discriminators, the learnt models are tuned to predict
the lane location and class in the target domain. The proposed techniques are
evaluated using both real-world and our synthetic datasets. The results
manifest that the proposed methods have shown superiority over other baseline
schemes in terms of detection and classification accuracy and consistency. The
ablation study reveals that the size of the simulation dataset plays important
roles in the classification performance of the proposed methods. Our UDA
frameworks are available at https://github.com/anita-hu/sim2real-lane-detection
and our dataset generator is released at https://github.com/anita-hu/simulanes
