LLFormer4D: LiDAR-based lane detection method by temporal feature fusion and sparse transformer

Jun Hu, Zuotao Ning, Chaolu Feng, Haoxiang Jie, Xinyi Zuo, Wei Liu, Xiangyu Wei

Abstract: Lane detection is a fundamental problem in autonomous driving, which provides vehicles with essential road information. Despite the attention from scholars and engineers, lane detection based on LiDAR meets challenges such as unsatisfactory detection accuracy and significant computation overhead. In this paper, the authors propose LLFormer4D to overcome these technical challenges by leveraging the strengths of both Convolutional Neural Network and Transformer networks. Specifically, the Temporal Feature Fusion module is introduced to enhance accuracy and robustness by integrating features from multi-frame point clouds. In addition, a sparse Transformer decoder based on Lane Key-point Query is designed, which introduces key-point supervision for each lane line to streamline the post-processing. The authors conduct experiments and evaluate the proposed method on the K-Lane and nuScenes map datasets respectively. The results demonstrate the effectiveness of the presented method, achieving second place with an F1 score of 82.39 and a processing speed of 16.03 Frames Per Seconds on the K-Lane dataset. Furthermore, this algorithm attains the best mAP of 70.66 for lane detection on the nuScenes map dataset.