摘要：

自动驾驶场景理解是自动驾驶技术的关键环节之一, 地图感知和拓扑关系推理是场景理解的重要组成部分。地图感知任务主要包括道路元素感知和交通元素感知, 拓扑关系推理在地图感知的基础上构建感知结果间的拓扑关系。然而, 在传感器受到遮挡或感知范围超过传感器范围时, 传统方法的地图感知性能会受到影响。同时, 由于拓扑关系推理依赖于地图感知结果, 地图感知误差会进一步影响拓扑关系推理的准确性。为此, 提出了一种融合标精地图先验的地图感知不确定性建模方法, 并基于地图感知不确定性实现鲁棒的拓扑关系推理。首先, 通过引入标精地图先验信息, 有效提升了遮挡场景下的地图感知性能。随后, 使用Laplace分布建模地图感知结果, 实现了对地图感知不确定性的建模。最后, 基于地图感知结果及其不确定性, 提出了一种基于概率的拓扑关系推理方法, 有效提升了拓扑关系构建精度。在公开数据集OpenLaneV2上进行大量实验, 结果表明, 所提方法在地图感知和拓扑关系推理任务上性能均优于对比方法。

关键词: 自动驾驶, 地图感知, 不确定性建模, 拓扑关系推理, 特征融合

Abstract:

Autonomous driving scene understanding is one of the critical components of autonomous driving technology. Map perception and topological relationship inference are essential parts of scene understanding. Map perception tasks mainly include road element perception and traffic element perception. Topological relationship reasoning builds topological relationships between perception results based on map perception. However, traditional methods face challenges in map perception performance when sensors are occluded, or the perception range exceeds the sensor's limit. Additionally, since topological relationship inference in driving scenes relies on map perception results, errors in map perception can further impact the accuracy of topological relationship inference. To address this, a map perception uncertainty modeling method incorporating standard map priors is proposed, along with robust topological relationship inference based on map perception uncertainty. First, by introducing high-precision map prior information, the method effectively enhances map perception performance in occluded scenarios. Next, the map perception results are modeled using the Laplace distribution to achieve uncertainty modeling of map perception. Finally, a probabilistic topological relationship inference method is proposed based on the map perception results and their uncertainties, which effectively improves the accuracy of topological relationship construction. Extensive experiments conducted on public datasets demonstrate that the proposed method outperforms comparative methods in both map perception and topological relationship inference tasks.

Key words: autonomous driving, map perception, uncertainty modeling, topological relationship reasoning, feature fusion