稀疏Laneformer

Sparse Laneformer

April 11, 2024

作者: Ji Liu, Zifeng Zhang, Mingjie Lu, Hongyang Wei, Dong Li, Yile Xie, Jinzhang Peng, Lu Tian, Ashish Sirasao, Emad Barsoum

cs.AI

摘要

车道检测是自动驾驶中的基本任务，在深度学习兴起时取得了巨大进展。先前基于锚点的方法通常设计密集锚点，这些锚点高度依赖于训练数据集，并在推断过程中保持不变。我们分析得出密集锚点对于车道检测并非必要，提出了一种基于稀疏锚点机制的基于Transformer的车道检测框架。为此，我们使用位置感知车道查询和角度查询生成稀疏锚点，而非传统的显式锚点。我们采用水平感知注意力（HPA）沿水平方向聚合车道特征，并采用车道角度交叉注意力（LACA）在车道查询和角度查询之间执行交互。我们还提出了基于可变交叉注意力的车道感知注意力（LPA），以进一步优化车道预测。我们的方法名为Sparse Laneformer，易于实现且端到端可训练。大量实验证明Sparse Laneformer在CULane数据集上表现优异，例如在相同的ResNet-34骨干网络上，F1分数比Laneformer高出3.0%，比O2SFormer高出0.7%，并且MACs更少。

English

Lane detection is a fundamental task in autonomous driving, and has achieved great progress as deep learning emerges. Previous anchor-based methods often design dense anchors, which highly depend on the training dataset and remain fixed during inference. We analyze that dense anchors are not necessary for lane detection, and propose a transformer-based lane detection framework based on a sparse anchor mechanism. To this end, we generate sparse anchors with position-aware lane queries and angle queries instead of traditional explicit anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane features along the horizontal direction, and adopt Lane-Angle Cross Attention (LACA) to perform interactions between lane queries and angle queries. We also propose Lane Perceptual Attention (LPA) based on deformable cross attention to further refine the lane predictions. Our method, named Sparse Laneformer, is easy-to-implement and end-to-end trainable. Extensive experiments demonstrate that Sparse Laneformer performs favorably against the state-of-the-art methods, e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score with fewer MACs on CULane with the same ResNet-34 backbone.