Abstract
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.
Abstract (translated)
基于视觉的自我车道推断在自动驾驶和高级驾驶辅助系统中至关重要。传统的解决方案需要经过良好校准的摄像头，但这会限制摄像头配置的变化，因为算法依赖于内生和外生标定。在本文中，我们提出了一种基于学习的自我车道推断方法，通过直接从单张图片中估计自车道的索引。为了提高稳健的性能，我们的模型在两个视角上同时引入了自车道的结构推断。此外，我们还使用基于消失点-线结构的注意力机制来适应视点的变化，而无需进行精确的标定。我们模型的适应性在不同的环境、设备和摄像头安装点得到了验证。
URL
https://arxiv.org/abs/2404.12770
