地图错误时的Map匹配：用于地图学习的高效道路和越野车辆跟踪
Map matching when the map is wrong: Efficient vehicle tracking on- and off-road for map learning
摘要：
给定一系列可能稀疏且有噪声的GPS轨迹和道路网络的地图，地图匹配算法可以推断出道路网络上最准确的轨迹。然而，如果道路网络是错误的（例如，由于丢失或不正确地映射的道路、丢失的停车场、错误定向的转弯限制或错误定向的单行道），则标准地图匹配算法无法重建正确的轨迹。本文提出了一种在已知道路网络上跟踪车辆的算法。它有效地统一了现有的隐马尔可夫模型（HMM）的地图匹配方法和标准的自由空间跟踪方法（如卡尔曼平滑）的原则。该算法是一种形式的交互式多模型（IMM）过滤器的一个额外的假设模型的相互作用的类型允许，这里称为半交互式多模型（sIMM）过滤器。描述了一种前向滤波器（适用于实时跟踪）和后向MAP采样步骤（适用于MAP轨迹推断和地图匹配）。这里列出的框架与所使用的特定跟踪模型无关，并清楚地说明了如何用类似类型的其他组件替换这些组件。除了避免生成误导性的地图匹配轨迹之外，该算法还可以通过检测未映射或未正确映射的道路和停车场、未正确映射的转弯限制和道路方向来学习地图特征。
英文摘要：
Given a sequence of possibly sparse and noisy GPS traces and a map of the road network, map matching algorithms can infer the most accurate trajectory on the road network. However, if the road network is wrong (for example due to missing or incorrectly mapped roads, missing parking lots, misdirected turn restrictions or misdirected one-way streets) standard map matching algorithms fail to reconstruct the correct trajectory. In this paper, an algorithm to tracking vehicles able to move both on and off the known road network is formulated. It efficiently unifies existing hidden Markov model (HMM) approaches for map matching and standard free-space tracking methods (e.g. Kalman smoothing) in a principled way. The algorithm is a form of interacting multiple model (IMM) filter subject to an additional assumption on the type of model interaction permitted, termed here as semi-interacting multiple model (sIMM) filter. A forward filter (suitable for real-time tracking) and backward MAP sampling step (suitable for MAP trajectory inference and map matching) are described. The framework set out here is agnostic to the specific tracking models used, and makes clear how to replace these components with others of a similar type. In addition to avoiding generating misleading map matching trajectories, this algorithm can be applied to learn map features by detecting unmapped or incorrectly mapped roads and parking lots, incorrectly mapped turn restrictions and road directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
