Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving

TL;DR

This paper introduces unsupervised domain adaptation techniques using adversarial methods and a new synthetic dataset for improving lane detection and classification in autonomous driving, reducing reliance on labeled real-world data.

Contribution

It presents novel UDA frameworks combining adversarial discriminative and generative approaches, along with the Simulanes synthetic dataset generator for lane detection tasks.

Findings

Proposed methods outperform baseline schemes in accuracy and consistency.

Synthetic dataset size significantly impacts classification performance.

Frameworks are validated on real-world and synthetic datasets.

Abstract

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