用多车采集的矢量瓦片生成全局高精地图，准确率超前代方法5%以上。
Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles
- 通过多层注意力自编码器融合多轮采集的局部矢量瓦片
- 在真实数据集上F1得分超越现有最佳方法5%以上
- 已落地应用，适合自动驾驶高精地图自动化构建场景
高精地图是自动驾驶系统的基础，可提供驾驶场景的精确环境信息。现有矢量地图生成方法仅能在单次行驶中通过车载传感器生成约65%的车辆周边局部地图元素，难以在世界坐标系下构建高质量全局高精地图。为此，本文提出GNMap，一种端到端的生成式神经网络，可基于多辆自动驾驶汽车多次巡游产生的多个局部矢量瓦片，自动构建全局高精地图。该模型采用多层注意力自编码器作为共享网络，通过预训练与微调两个阶段学习参数，确保生成地图的完整性与要素类别的正确性。在真实世界数据集上进行了大量定性评估，实验结果表明，GNMap的F1分数超过当前最优方法5%以上，达到工业可用水平，仅需少量人工修正。目前已在导航信息有限公司（Navinfo Co., Ltd.）部署，作为自动驾驶高精地图自动构建的核心软件。
原文摘要 · Abstract (English)
High-definition (HD) map is a fundamental component of autonomous driving systems, as it can provide precise environmental information about driving scenes. Recent work on vectorized map generation could produce merely 65% local map elements around the ego-vehicle at runtime by one tour with onboard sensors, leaving a puzzle of how to construct a global HD map projected in the world coordinate system under high-quality standards. To address the issue, we present GNMap as an end-to-end generative neural network to automatically construct HD maps with multiple vectorized tiles which are locally produced by autonomous vehicles through several tours. It leverages a multi-layer and attention-based autoencoder as the shared network, of which parameters are learned from two different tasks (i.e., pretraining and finetuning, respectively) to ensure both the completeness of generated maps and the correctness of element categories. Abundant qualitative evaluations are conducted on a real-world dataset and experimental results show that GNMap can surpass the SOTA method by more than 5% F1 score, reaching the level of industrial usage with a small amount of manual modification. We have already deployed it at Navinfo Co., Ltd., serving as an indispensable software to automatically build HD maps for autonomous driving systems.
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