用普通地图辅助实时构建高精道路拓扑，提升自动驾驶地图生成一致性。
Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps
- 基于标准地图先验，融合传感器数据预测车道与拓扑关系。
- 在公开数据集上车道段检测准确率显著优于已有方法。
- 适合需要低依赖高精地图的自动驾驶系统研发者。
多数自动驾驶汽车依赖高精地图。现有研究尝试通过车载传感器直接预测高精地图要素，并推理其与交通元素的关系。尽管有进展，但高精地图的协同在线构建仍具挑战，需统一建模道路拓扑的高复杂性。本文提出一种新方法，利用常见的标准定义（SD）地图先验信息，联合预测车道段及其拓扑、道路边界。设计了一种混合车道段编码网络架构，结合先验信息与去噪技术以增强训练稳定性和性能；同时引入历史帧实现时序一致性。实验表明，本方法显著优于先前方法，验证了建模方案的有效性。
原文摘要 · Abstract (English)
Most autonomous cars rely on the availability of high-definition (HD) maps. Current research aims to address this constraint by directly predicting HD map elements from onboard sensors and reasoning about the relationships between the predicted map and traffic elements. Despite recent advancements, the coherent online construction of HD maps remains a challenging endeavor, as it necessitates modeling the high complexity of road topologies in a unified and consistent manner. To address this challenge, we propose a coherent approach to predict lane segments and their corresponding topology, as well as road boundaries, all by leveraging prior map information represented by commonly available standard-definition (SD) maps. We propose a network architecture, which leverages hybrid lane segment encodings comprising prior information and denoising techniques to enhance training stability and performance. Furthermore, we facilitate past frames for temporal consistency. Our experimental evaluation demonstrates that our approach outperforms previous methods by a large margin, highlighting the benefits of our modeling scheme.
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