  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.
