High Definition (HD) maps are necessary for many applications of automated driving (AD), but their manual creation and maintenance is very costly. Vehicle fleet data from series production vehicles can be used to automatically generate HD maps, but the data is often incomplete and noisy. We propose a system for the generation of HD maps from vehicle fleet data, which is tolerant to missing or misclassified detections and can handle drives with multiple routes, generating a single complete map, model-free and without prior reference lines. Using randomly selected drives as pivot drives, a step-wise lateral sampling of detections is performed. These sampled points are then clustered and aligned using Expectation Maximization (EM), estimating a lateral offset for each drive to compensate localization errors. The clustered points are replaced with the maxima of their probability density function (PDF) and connected to form polylines using a modified rectangular linear assignment algorithm. The data from vehicles on varying routes is then fused into a hierarchical singular map graph. The proposed approach achieves an average accuracy below 0.5 meters compared to a hand annotated ground truth map, as well as correctly resolving lane splits and merges, proving the feasibility of the use of vehicle fleet data for the generation of highway HD maps.

翻译：高清（HD）地图是自动驾驶（AD）众多应用所必需的，但其人工创建和维护成本高昂。量产车辆的车队数据可用于自动生成高清地图，但此类数据往往不完整且包含噪声。本文提出一种基于车辆车队数据生成高清地图的系统，该系统能够容忍缺失或错误分类的检测结果，并可处理多路线驾驶场景，无需模型和预先参考线即可生成单一完整地图。通过随机选取驾驶数据作为枢轴驾驶，对检测结果进行逐级横向采样。随后利用期望最大化（EM）算法对这些采样点进行聚类与对齐，估计每条驾驶数据的横向偏移以补偿定位误差。聚类点被其概率密度函数（PDF）的最大值替代，并通过改进的矩形线性分配算法连接形成折线。来自不同路线车辆的数据随后融合为分层奇异地图图。与人工标注的真实地图相比，该方法实现了平均误差低于0.5米的精度，并正确解析了车道分流与汇合，验证了利用车辆车队数据生成高速公路高清地图的可行性。