用稀疏车辆数据自动构建高精地图车道模型，提升可扩展性。
LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations
- 基于车辆观测生成车道边界折线，通过变换器预测车道对与连接关系。
- 在多车观测数据上达到优于基线的精度，高速公路与普通道路均有效。
- 适合自动驾驶地图自动化更新，尤其适用于数据采集受限场景。
在自动驾驶中，高精地图提供不受传感器范围和遮挡限制的完整车道模型。然而，高精地图的生成与维护依赖周期性数据采集和人工标注，制约了其可扩展性。为此，本文探索利用稀疏车辆观测替代密集传感器数据，实现车道模型的自动化生成。首先通过配准与聚合观测到的车道边界生成折线；以行驶轨迹为起点，预测由左右边界点定义的车道对。提出车道模型变换器网络（LMT-Net），一种编码器-解码器结构，实现折线编码并预测车道对及其连通性。通过预测的车道对作为节点、连通性作为边，构建车道图。在包含多车观测及人工标注为真值（GT）的内部数据集上评估，结果表明性能优异，且在高速公路与非高速公路运行设计域（ODD）下均优于所实现基线。
原文摘要 · Abstract (English)
In autonomous driving, High Definition (HD) maps provide a complete lane model that is not limited by sensor range and occlusions. However, the generation and upkeep of HD maps involves periodic data collection and human annotations, limiting scalability. To address this, we investigate automating the lane model generation and the use of sparse vehicle observations instead of dense sensor measurements. For our approach, a pre-processing step generates polylines by aligning and aggregating observed lane boundaries. Aligned driven traces are used as starting points for predicting lane pairs defined by the left and right boundary points. We propose Lane Model Transformer Network (LMT-Net), an encoder-decoder neural network architecture that performs polyline encoding and predicts lane pairs and their connectivity. A lane graph is formed by using predicted lane pairs as nodes and predicted lane connectivity as edges. We evaluate the performance of LMT-Net on an internal dataset that consists of multiple vehicle observations as well as human annotations as Ground Truth (GT). The evaluation shows promising results and demonstrates superior performance compared to the implemented baseline on both highway and non-highway Operational Design Domain (ODD).
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