用普通地图训练模型，让自动驾驶更懂道路连接关系。
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
- 用标准地图和卫星图训练，摆脱对车载传感器的依赖。
- 仅用低精度输入，就能达到领先级的车道拓扑理解能力。
- 可通用集成到任何实时推理系统，提升效果最高达28%。
道路拓扑推理对自动驾驶至关重要，能全面理解车道与交通要素间的连通性与关系。尽管近期方法已通过车载传感器实现驾驶拓扑感知，但其可扩展性受限于对一致传感器配置训练数据的依赖。我们发现，实现可扩展车道感知与拓扑推理的关键在于消除这种传感器依赖特征。为此，提出SMART：利用易获取的标准清晰度（SD）地图与卫星图，基于大规模地理参考高清（HD）地图进行监督训练，构建与传感器设置无关的地图先验模型。得益于规模化训练，仅使用SD与卫星输入，SMART即可实现优越的离线车道拓扑理解。大量实验表明，SMART可无缝集成至任意在线拓扑推理方法中，在OpenLane-V2基准上带来最高达28%的性能提升。
原文摘要 · Abstract (English)
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent approaches have shown success in perceiving driving topology using vehicle-mounted sensors, their scalability is hindered by the reliance on training data captured by consistent sensor configurations. We identify that the key factor in scalable lane perception and topology reasoning is the elimination of this sensor-dependent feature. To address this, we propose SMART, a scalable solution that leverages easily available standard-definition (SD) and satellite maps to learn a map prior model, supervised by large-scale geo-referenced high-definition (HD) maps independent of sensor settings. Attributed to scaled training, SMART alone achieves superior offline lane topology understanding using only SD and satellite inputs. Extensive experiments further demonstrate that SMART can be seamlessly integrated into any online topology reasoning methods, yielding significant improvements of up to 28% on the OpenLane-V2 benchmark.
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