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.
@article{arxiv.2404.12770,
title = {Camera Agnostic Two-Head Network for Ego-Lane Inference},
author = {Chaehyeon Song and Sungho Yoon and Minhyeok Heo and Ayoung Kim and Sujung Kim},
journal= {arXiv preprint arXiv:2404.12770},
year = {2025}
}