利用车辆轨迹提升高精地图车道线检测精度
MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling
- 用历史车辆轨迹作为道路结构先验，优化车道线检测
- 在NuScenes数据集上车道分隔线AP提升4.6，整体mAP提升2.6
- 适合自动驾驶中复杂场景下的高精地图构建需求
高精地图中的车道线检测与预测任务因视角遮挡、远距离可见性差及恶劣天气等非理想条件而极具挑战，常导致检测精度下降、系统可靠性降低。为应对这些挑战，本文提出MapATM——一种新型深度神经网络，通过有效利用历史车辆轨迹信息来提升车道线检测精度。其中，'演员'指移动车辆，其轨迹被用作道路几何的结构先验。结果显示，相较于强基线方法，MapATM在难测的NuScenes数据集上，车道分隔线的AP提升4.6（相对提升10.1%），mAP提升2.6（相对提升6.1%）。大量定性评估进一步表明，MapATM能在多样且复杂的驾驶场景中持续实现稳定可靠的地图重建，凸显其在自动驾驶应用中的实际价值。
原文摘要 · Abstract (English)
High-definition (HD) mapping tasks, which perform lane detections and predictions, are extremely challenging due to non-ideal conditions such as view occlusions, distant lane visibility, and adverse weather conditions. Those conditions often result in compromised lane detection accuracy and reduced reliability within autonomous driving systems. To address these challenges, we introduce MapATM, a novel deep neural network that effectively leverages historical actor trajectory information to improve lane detection accuracy, where actors refer to moving vehicles. By utilizing actor trajectories as structural priors for road geometry, MapATM achieves substantial performance enhancements, notably increasing AP by 4.6 for lane dividers and mAP by 2.6 on the challenging NuScenes dataset, representing relative improvements of 10.1% and 6.1%, respectively, compared to strong baseline methods. Extensive qualitative evaluations further demonstrate MapATM's capability to consistently maintain stable and robust map reconstruction across diverse and complex driving scenarios, underscoring its practical value for autonomous driving applications.
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