用基础模型提升自动驾驶地图生成精度
MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
- 融合视觉基础模型增强图像特征表示
- 多任务学习提升语义分割与地图精度
- 适合做高精地图与端到端感知的研究者
在自动驾驶中，鸟瞰图（BEV）下的高精地图和语义地图对精准定位、路径规划与决策至关重要。本文提出一种改进的端到端模型 MapFM，用于在线生成矢量化高精地图。通过引入强大的视觉基础模型编码摄像头图像，显著提升了特征表示质量。为进一步丰富模型对环境的理解并提高预测性能，我们在 BEV 表示中集成辅助语义分割预测头。该多任务学习方法提供了更丰富的上下文监督，实现了更全面的场景表征，最终提升了预测矢量化高精地图的准确性和质量。源代码已公开于 https://github.com/LIvanoff/MapFM。
原文摘要 · Abstract (English)
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. The source code is available at https://github.com/LIvanoff/MapFM.
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