Camera Agnostic Two-Head Network for Ego-Lane Inference
<|reference_start|>Camera Agnostic Two-Head Network for Ego-Lane Inference: 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.<|reference_end|>
@article{song2024camera,
    title={Camera Agnostic Two-Head Network for Ego-Lane Inference},
    author={Chaehyeon Song, Sungho Yoon, Minhyeok Heo, Ayoung Kim, Sujung Kim},
    journal={arXiv preprint arXiv:2404.12770},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12770},
    primaryClass={cs.CV cs.LG cs.RO}
}