在线融合时序信息构建高精度矢量地图，提升无图自动驾驶鲁棒性。
Online Temporal Fusion for Vectorized Map Construction in Mapless Autonomous Driving
- 通过哈希策略融合历史道路标记检测，构建语义体素图。
- 增量聚类生成可靠道路标记实例，几何拓扑结构更准确。
- 适合复杂城市场景的闭环自动驾驶系统使用。
为降低对高精地图的依赖，自动驾驶领域正兴起利用车载传感器在线生成矢量地图的趋势。然而现有方法多仅处理单帧输入，难以应对复杂场景。为此，本文提出一种在线地图构建系统，利用长期时序信息生成一致的矢量地图。首先，通过哈希策略高效融合来自现成网络的历史道路标记检测，构建语义体素图，以利用道路元素的稀疏性；其次，通过分析融合信息，增量聚类出可靠体素，形成道路标记的实例级表示；最后，引入领域知识估计道路的几何与拓扑结构，可直接供规划控制（PnC）模块使用。在复杂城市环境中的实验表明，该系统输出相比网络原始输出显著更一致、更准确，且可在闭环自动驾驶系统中有效应用。
原文摘要 · Abstract (English)
To reduce the reliance on high-definition (HD) maps, a growing trend in autonomous driving is leveraging onboard sensors to generate vectorized maps online. However, current methods are mostly constrained by processing only single-frame inputs, which hampers their robustness and effectiveness in complex scenarios. To overcome this problem, we propose an online map construction system that exploits the long-term temporal information to build a consistent vectorized map. First, the system efficiently fuses all historical road marking detections from an off-the-shelf network into a semantic voxel map, which is implemented using a hashing-based strategy to exploit the sparsity of road elements. Then reliable voxels are found by examining the fused information and incrementally clustered into an instance-level representation of road markings. Finally, the system incorporates domain knowledge to estimate the geometric and topological structures of roads, which can be directly consumed by the planning and control (PnC) module. Through experiments conducted in complicated urban environments, we have demonstrated that the output of our system is more consistent and accurate than the network output by a large margin and can be effectively used in a closed-loop autonomous driving system.
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