用道路手册和语言信息提升自动驾驶车道拓扑预测精度
Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
- 融合道路手册与地图元数据，增强车道拓扑预测模型
- 在复杂交叉口上提升车道与交通元素检测及关联性能
- 适合研究自动驾驶环境理解与地图构建的学者
车道拓扑预测是实现安全可靠自动驾驶的关键。准确理解道路环境有助于该任务。我们发现此类信息常以自然语言形式体现于设计规范中，反映道路结构与功能。本文通过轻量级方式将开放街道地图（OSM）中的结构化道路元数据和道路设计手册中的车道宽度先验，与道路中心线编码结合，增强基于地图先验的在线车道拓扑预测模型SMERF。我们在两个地理分布多样且复杂的交叉口场景中评估该方法，结果表明其在车道与交通元素检测及其关联性方面均有提升。采用四个拓扑感知指标全面评估模型性能，验证了该方法在不同拓扑结构与条件下具备良好的泛化与扩展能力。
原文摘要 · Abstract (English)
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality. We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model, by combining structured road metadata from OSM maps and lane-width priors from Road design manuals with the road centerline encodings. We evaluate our method on two geo-diverse complex intersection scenarios. Our method shows improvement in both lane and traffic element detection and their association. We report results using four topology-aware metrics to comprehensively assess the model performance. These results demonstrate the ability of our approach to generalize and scale to diverse topologies and conditions.
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