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发明授权
CN105957342B 基于众包时空大数据的车道级道路测图方法及系统 有权

基于众包时空大数据的车道级道路测图方法及系统
摘要：
本发明提供一种基于众包时空大数据的车道级道路测图方法，包括建立轨迹向量的相似度评估模型，基于融合经验知识的生长聚类方法进行轨迹优选，构建高斯约束混合模型，并使用EM算法求解模型参数；探测车道信息，得到道路路段车道数量初次探测结果；基于道路建设规则，对初次探测结果进行修正；根据修正后的车道数量，根据相邻情况对车道中心线进行修正。本发明降低了获取城市精细道路信息的成本，且探测方法简单、容易实现。
摘要（英）：

The invention provides a lane-level road mapping method based on crowdsourcing space-time big data, and the method comprises the steps: building a similarity estimation model of a track vector, carrying out the track optimization based on a growth clustering method integrated with experiential knowledge, building a Gaussian constraint mixed model, and employing an EM algorithm to solve model parameters; detecting lane information, and obtaining an initial detection result of the number of road lanes; correcting the initial detection result based on a road construction rule; and correcting the central lines of the lanes according to the corrected number of lanes and the conditions of the surroundings. The method reduces the cost in obtaining the urban precise road information, is simple, and is easy to implement.

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