用标准地图提升自动驾驶环境感知与拓扑推理能力
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving
- 融合栅格与矢量标准地图，增强鸟瞰特征表示
- 在OpenLane-V2上显著提升长距与遮挡场景表现
- 适合无图自动驾驶系统，尤其关注拓扑理解的场景
在线环境感知与拓扑推理对自动驾驶至关重要，尤其在减少对昂贵高精地图依赖的无图系统中。然而，现有方法在远距离或遮挡场景仍受限于车载传感器能力。为此，本文提出标准定义（SD）地图增强的场景感知与拓扑推理框架（SEPT），探索如何将SD地图作为先验知识融入现有感知与推理流程。我们设计了一种新颖的混合特征融合策略，结合栅格化与矢量化SD地图与鸟瞰（BEV）特征，同时缓解地图与特征空间间的潜在错位问题。此外，利用SD地图特性，引入辅助的交点感知关键点检测任务，进一步提升整体理解性能。在大规模OpenLane-V2数据集上的实验表明，有效整合SD地图先验后，本框架在场景感知与拓扑推理上均显著优于现有方法。
原文摘要 · Abstract (English)
Online scene perception and topology reasoning are critical for autonomous vehicles to understand their driving environments, particularly for mapless driving systems that endeavor to reduce reliance on costly High-Definition (HD) maps. However, recent advances in online scene understanding still face limitations, especially in long-range or occluded scenarios, due to the inherent constraints of onboard sensors. To address this challenge, we propose a Standard-Definition (SD) Map Enhanced scene Perception and Topology reasoning (SEPT) framework, which explores how to effectively incorporate the SD map as prior knowledge into existing perception and reasoning pipelines. Specifically, we introduce a novel hybrid feature fusion strategy that combines SD maps with Bird's-Eye-View (BEV) features, considering both rasterized and vectorized representations, while mitigating potential misalignment between SD maps and BEV feature spaces. Additionally, we leverage the SD map characteristics to design an auxiliary intersection-aware keypoint detection task, which further enhances the overall scene understanding performance. Experimental results on the large-scale OpenLane-V2 dataset demonstrate that by effectively integrating SD map priors, our framework significantly improves both scene perception and topology reasoning, outperforming existing methods by a substantial margin.
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