Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems. The traditional approach necessitates well-calibrated cameras, which confines variation of camera configuration, as the algorithm relies on intrinsic and extrinsic calibration. In this paper, we propose a learning-based ego-lane inference by directly estimating the ego-lane index from a single image. To enhance robust performance, our model incorporates the two-head structure inferring ego-lane in two perspectives simultaneously. Furthermore, we utilize an attention mechanism guided by vanishing point-and-line to adapt to changes in viewpoint without requiring accurate calibration. The high adaptability of our model was validated in diverse environments, devices, and camera mounting points and orientations.


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    Titel :

    Camera Agnostic Two-Head Network for Ego-Lane Inference


    Beteiligte:
    Song, Chaehyeon (Autor:in) / Yoon, Sungho (Autor:in) / Heo, Minhyeok (Autor:in) / Kim, Ayoung (Autor:in) / Kim, Sujung (Autor:in)


    Erscheinungsdatum :

    02.06.2024


    Format / Umfang :

    1480953 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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