Heatmap-based Vanishing Point boosts Lane Detection
This work addresses a key challenge in autonomous driving by improving lane detection accuracy, though it appears incremental as it builds on existing segmentation and fusion techniques.
The paper tackles lane detection for autonomous driving by integrating vanishing point prediction with lane segmentation, achieving higher accuracy than state-of-the-art methods on the CULane dataset.
Vision-based lane detection (LD) is a key part of autonomous driving technology, and it is also a challenging problem. As one of the important constraints of scene composition, vanishing point (VP) may provide a useful clue for lane detection. In this paper, we proposed a new multi-task fusion network architecture for high-precision lane detection. Firstly, the ERFNet was used as the backbone to extract the hierarchical features of the road image. Then, the lanes were detected using image segmentation. Finally, combining the output of lane detection and the hierarchical features extracted by the backbone, the lane VP was predicted using heatmap regression. The proposed fusion strategy was tested using the public CULane dataset. The experimental results suggest that the lane detection accuracy of our method outperforms those of state-of-the-art (SOTA) methods.