AI 中文总结
研究针对自动驾驶中车道拓扑感知模型输出不稳定问题，提出WASABI实时后处理管道，通过联合跟踪车道段及其连通性，集成多种技术，在内部验证数据上提升了检测F1等指标，实现帧内和帧间车道拓扑稳定。
AI 中文摘要
自动驾驶需要将道路理解为可行驶车道及其连通性的图，而不仅仅是自身车道，以通过十字路口并推断交叉和合并交通情况。近期感知模型从车载传感器的360度鸟瞰视图推断车道拓扑。但由于神经感知的不完善，其输出存在结构不稳定问题。本文提出WASABI，一种实时后处理管道，在车载实时约束下，将车道段及其LCLC连通性视为联合跟踪目标，在帧内和帧间稳定车道拓扑输出。该管道集成了段跟踪与连通性、抗噪声拓扑感知细化和资源受限实时设计。在内部验证数据上，WASABI提高了LCLC检测F1，降低了中心线横向误差和检测误报率，还降低了时间稳定性指标。
英文摘要
Autonomous driving requires understanding the road as a graph of drivable lanes and their connectivity, beyond the ego lane alone, to follow routes through intersections and reason about cross- and merging-traffic. Recent perception models infer such lane topology, i.e., lane segments together with their inter-lane connectivity (LCLC), from onboard sensors over a 360-degree BEV view. Due to neural perception's imperfections, their outputs retain structural instabilities such as missed detections, lost or incorrect LCLC, over-detection, and label flicker. This paper presents WASABI, a real-time post-processing pipeline that stabilizes lane topology outputs both within and across frames by treating lane segments and their LCLC connectivity as joint tracking targets, under onboard real-time constraints (10 Hz / 20 ms / up to 200 input lanes). The pipeline integrates segment tracking with connectivity, noise-robust topology-aware refinement, and a resource-constrained real-time design. On internal validation data (16 sequences), WASABI improves LCLC detection F1 from 0.834 to 0.948 (+0.114, +13.6%) and reduces centerline lateral error from 2.50 m to 0.95 m, while reducing detection false-positives by 24.6%. Temporal-stability metrics on the same data show LCLC toggle rate reduced by 63.3% and boundary-label flicker rate by 30.2%, confirming across-frame stabilization beyond per-frame accuracy.
