用众包车辆数据端到端生成高精度城市地图，降低成本90%。
End-to-End Generation of City-Scale Vectorized Maps by Crowdsourced Vehicles
- 通过多车时空数据融合与新架构实现端到端建图
- 相比单车方法，地图精度和结构鲁棒性显著提升
- 适合自动驾驶高精地图快速构建与低成本部署
高精度矢量化地图对自动驾驶至关重要，但传统基于激光雷达的制图成本高、速度慢，而单车感知方法在恶劣条件下准确性与鲁棒性不足。本文提出 EGC-VMAP 框架，通过聚合众包车辆数据，实现端到端的城市级矢量化地图生成。不同于以往方法，EGC-VMAP 在统一学习流程中使用新型旅行感知变压器（Trip-Aware Transformer）直接融合多车、多时序感知的地图要素。结合分层匹配机制与多目标损失函数，该方法显著提升了地图精度与结构鲁棒性。在大规模真实多城数据集上验证，性能优于单车基线方法，并实现人工标注成本降低90%的可扩展、低成本城市级制图方案。
原文摘要 · Abstract (English)
High-precision vectorized maps are indispensable for autonomous driving, yet traditional LiDAR-based creation is costly and slow, while single-vehicle perception methods lack accuracy and robustness, particularly in adverse conditions. This paper introduces EGC-VMAP, an end-to-end framework that overcomes these limitations by generating accurate, city-scale vectorized maps through the aggregation of data from crowdsourced vehicles. Unlike prior approaches, EGC-VMAP directly fuses multi-vehicle, multi-temporal map elements perceived onboard vehicles using a novel Trip-Aware Transformer architecture within a unified learning process. Combined with hierarchical matching for efficient training and a multi-objective loss, our method significantly enhances map accuracy and structural robustness compared to single-vehicle baselines. Validated on a large-scale, multi-city real-world dataset, EGC-VMAP demonstrates superior performance, enabling a scalable, cost-effective solution for city-wide mapping with a reported 90\% reduction in manual annotation costs.
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