Enhancing Anchor-Based Lane Detection with Auxiliary Semantic Segmentation Supervision

Engineering notes

Key topics: autonomous driving system, autonomous driving, lane detection, semantic segmentation. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Accurate and real-time lane detection is essential for autonomous driving systems. While anchor-based methods such as LaneATT have demonstrated strong performance in both speed and accuracy, they often lack spatial understanding that could be provided by dense semantic segmentation. Conversely, segmentation-based approaches struggle to capture instance-level lane structures. Notably, enhancing vectorized lane detection with auxiliary segmentation supervision has not been widely addressed. Therefore, in this work, we propose a dual-task extension to LaneATT by integrating an auxiliary segmentation head. Our architecture jointly learns vectorized lane representations and pixel-wise lane masks using a shared backbone. It is a hybrid vectorized lane detection model that incorporates auxiliary semantic segmentation to guide spatial reasoning in challenging road scenarios. Experiments on the TuSimple dataset demonstrate that our method improves lane detection accuracy and robustness while preserving real-time performance. Ablation studies further validate the effectiveness of different backbones, segmentation heads, and loss weights. Our proposed approach maintains real-time performance while offering improved spatial consistency.

Links and sources

• Official / arXiv page

Comments