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 photoreal 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 github.com/anita-hu/sim2real-lane-detection and our dataset generator is released at github.com/anita-hu/simulanes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2022-06-05


    Size :

    3387930 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Synthetic-to-Real Domain Adaptation for Lane Detection

    Garnett, Noa / Uziel, Roy / Efrat, Netalee et al. | British Library Conference Proceedings | 2021


    Real-Time Lane Detection for Autonomous Navigation

    Jeong, S. G. / Kim, C. S. / Yoon, K. S. et al. | British Library Conference Proceedings | 2001


    Detection and Classification of Lanes Using Reannotated Lane Dataset to Aid Autonomous Driving

    TS, Rajalakshmi / R, Senthilnathan | Transportation Research Record | 2024


    Real-time lane detection for autonomous navigation

    Seung Gweon Jeong, / Chang Sup Kim, / Kang Sup Yoon, et al. | IEEE | 2001


    / V2X Real-time driving lane detection method and system for autonomous/remote driving and V2X service

    KIM JIN UK / LEE HWANG HYUN | European Patent Office | 2022

    Free access