SMART: 運転トポロジー推論のためのスケーラブルな地図事前分布の進展
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
SD地図と衛星画像から地図事前分布モデルを学習し、センサー構成に依存せずスケーラブルな車線トポロジー推論を実現する手法を提案。OpenLane-V2で最大28%の改善。
著者: Junjie Ye, David Paz, Hengyuan Zhang, Yuliang Guo, Xinyu Huang, Henrik I. Christensen, Yue Wang, Liu Ren
分類: cs.CV, cs.RO
原文アブストラクト
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.
