Camera Agnostic Two-Head Network for Ego-Lane Inference
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908415230476288 |
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| author | Song, Chaehyeon Yoon, Sungho Heo, Minhyeok Kim, Ayoung Kim, Sujung |
| author_facet | Song, Chaehyeon Yoon, Sungho Heo, Minhyeok Kim, Ayoung Kim, Sujung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Camera Agnostic Two-Head Network for Ego-Lane Inference Song, Chaehyeon Yoon, Sungho Heo, Minhyeok Kim, Ayoung Kim, Sujung Computer Vision and Pattern Recognition Machine Learning Robotics 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. |
| title | Camera Agnostic Two-Head Network for Ego-Lane Inference |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2404.12770 |