用众包轨迹数据提升道路分割与拓扑推理精度
Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors
- 从众包轨迹中提取先验信息，转为热力图和向量令牌
- 在OpenLane-V2上显著超越当前最先进方法
- 设计置信度融合模块缓解先验与感知的错位问题
在自动驾驶中，车道段感知技术已能全面理解驾驶场景。引入先验信息可有效提升模型鲁棒性与准确性，但获取高质量先验、对齐先验与在线感知、高效融合仍面临三大挑战。本文提出从轨迹先验的新视角解决这些问题：首次从Argoverse2运动预测数据集中提取众包轨迹数据，并将其编码为栅格化热力图和向量化实例标记；通过多种方式将此类先验信息融入在线地图模型。为缓解先验与感知间的不一致，设计基于置信度的融合模块，在融合过程中显式考虑对齐。在OpenLane-V2数据集上进行大量实验，结果表明本方法性能显著优于现有最先进方法。代码已开源：https://github.com/wowlza/TrajTopo
原文摘要 · Abstract (English)
In autonomous driving, recent advances in lane segment perception provide autonomous vehicles with a comprehensive understanding of driving scenarios. Moreover, incorporating prior information input into such perception model represents an effective approach to ensure the robustness and accuracy. However, utilizing diverse sources of prior information still faces three key challenges: the acquisition of high-quality prior information, alignment between prior and online perception, efficient integration. To address these issues, we investigate prior augmentation from a novel perspective of trajectory priors. In this paper, we initially extract crowdsourcing trajectory data from Argoverse2 motion forecasting dataset and encode trajectory data into rasterized heatmap and vectorized instance tokens, then we incorporate such prior information into the online mapping model through different ways. Besides, with the purpose of mitigating the misalignment between prior and online perception, we design a confidence-based fusion module that takes alignment into account during the fusion process. We conduct extensive experiments on OpenLane-V2 dataset. The results indicate that our method's performance significantly outperforms the current state-of-the-art methods. Code is released is at https://github.com/wowlza/TrajTopo
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