キャリブレーション不要な自車線推定のためのカメラ非依存デュアルヘッドネットワーク
Camera Agnostic Two-Head Network for Ego-Lane Inference
単一画像から自車線インデックスを直接推定する学習ベース手法を提案し、消失点と線に導かれた注意機構と二頭構造でカメラ構成の変化に頑健に対応した。
著者: Chaehyeon Song, Sungho Yoon, Minhyeok Heo, Ayoung Kim, Sujung Kim
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
