车辆定位的地图匹配算法研究

研究了车辆定位系统中道路匹配问题,提出基于权重参考车辆历史轨迹的匹配算法。结合车辆行驶状态,引入位置信息和方向信息的可靠性参数,有效融合两者信息。仿真实验表明,该算法能够获得较高的匹配精度。     

基于GPS轨迹数据的地图匹配算法

针对GPS浮动车轨迹数据具有整体运动趋势的特点,结合城市路网行车限制的约束,提出一种GPS轨迹数据的全局地图匹配方法,综合考虑轨迹曲线与路网路径的曲线相似性、实际行车的路段几何拓扑和交通管制约束下的连通性,实现较好的地图匹配效果,并通过实验进行验证,为GPS浮动车数据的进一步分析应用打下基础。     

一种基于复杂网络的道路匹配方法

为了满足用户对数据现势性的要求,应当保证道路数据的及时准确更新,其关键是对同名道路数据进行匹配。鉴于对道路进行整体分析,道路的形状特征变得尤为重要,因此提出了基于复杂网络的道路匹配方法。该方法利用复杂网络理论分析每条道路的形状特性,得到相关的形状参数,再结合距离、方向等指标的参数计算得到总的相似度,从而选出最佳的匹配道路。这种方法计算简单,对道路的形状特征能很好地描述,并能很快地从候选匹配道路中识别出匹配道路。实验结果表明,将该方法用于道路匹配具有较高的准确率和可靠性。     

一种改进的轨迹地图匹配算法

GPS轨迹数据数目的急剧增长,对地图匹配算法提出了严峻的考验。目前已有的地图匹配算法对于较为复杂的路段和具有低频GPS的数据很难达到很高的准确率。本文提出了一种基于HMM的改进的地图匹配算法,在求解最短路径距离时进行了改进。经试验验证,本文提出的算法相比传统HMM算法具有更高的准确率。     

车辆导航系统的地图匹配技术研究

常规的地图匹配方法只能消除路段径向的偏差,在分析GPS误差模型的基础上,采用卡尔曼滤波技术对传感测量位置与数字道路地图之间的偏差进行估计,销减了路段平行方向的偏差,从而大大提高了系统的定位精度。仿真试验的结果证明了该算法对于提高车辆定位系统的性能是有效的。     

基于道路精简滤波原理的车辆导航系统地图匹配算法

针对交叉路口附近道路几何分布较为复杂的实际特点,提出基于道路精简滤波(RRF)原理的地图匹配算法。研究了基于D-S证据理论的多规则数据融合技术在二路段地图匹配中的应用。建立了最短欧氏距离和最小航向差两条判决规则。引入相关性模糊决策理论,将多路段匹配问题简化为二路段匹配问题,使D-S证据判决规则的概率分配函数能根据实际路网分布作适应性调整,提高了算法的鲁棒性。对实际跑车数据的仿真处理结果表明,应用该算法可以较好地解决城市交叉路口地图匹配问题。     

北京出租车GPS轨迹数据地图匹配算法研究

由于车辆位置数据匹配到电子地图时,会出现车辆轨迹偏离实际道路的情况,为了提高出租车GPS轨迹数据匹配到地图的准确率,提出一种出租车地图匹配算法:基于GPS定位精度的距离范围和车辆行驶方向与道路方向的夹角区间确定候选路段,依据车辆的速度确定方向权重,计算距离和方向的综合权重值进行轨迹点匹配,通过最短路径算法进行行驶轨迹的选择,并采用北京市西二环周围100辆出租车24860条GPS数据进行实验验证。实验表明该匹配算法的匹配正确率可达到96.72%。其具有地图匹配的准确性。     

轨迹分割与图层融合的车辆轨迹线构建道路地图方法

传统道路地图构建方法将轨迹点（线）同等对待提取道路数据,忽略车辆轨迹的空间差异性,制约其结果精度与应用范围。为此,本文根据轨迹速度将轨迹线集分割滤选为3个轨迹线子集,将轨迹方向与Delaunay三角网模型集成探测路网拓扑结构;顾及轨迹线子集的特征差异选取不同参数值和约束条件并分层提取道路几何、交通语义数据,分别构建3个道路图层;运用缓冲区方法并根据道路几何、语义特征将多个道路图层融合为单个完整道路地图。运用出租车轨迹数据进行试验分析,结果表明：该方法顾及轨迹分布差异性,能将道路几何、交通语义信息融合提取,更适于处理复杂道路结构下的轨迹线。     

一种路网拓扑约束下的增量型地图匹配算法

着眼于低频浮动车轨迹数据,对地图匹配问题进行了抽象,并分析了影响匹配结果的几何约束与拓扑约束。针对GPS采样的低频性和城市路网的复杂性,提出了一种路网拓扑约束下的增量型地图匹配算法（topology-constrained incremental matching algorithm,TIM）。选取北京市浮动车的GPS样例轨迹数据进行匹配,结果表明,该匹配算法在不同复杂程度的城市路网下均表现较好。     

浮动车轨迹点地图匹配及插值算法

针对带有定位误差和异常值的浮动车轨迹点数据,该文设计并实现了滑动窗口最优路径地图匹配算法,在综合考虑轨迹点的空间几何关系和路网拓扑关系基础上,为轨迹点匹配最优道路并纠正轨迹点误差。其次,针对稀疏且时间间隔不稳定的匹配后轨迹点,设计改进的Hermite插值法拟合车辆运动状态,并对稀疏轨迹点进行时序插值。利用南京市出租车轨迹点数据进行匹配算法与插值算法的验证,实验结果表明匹配算法具有较高准确性,插值算法能有效还原车辆行驶状态。     

车辆轨迹数据提取道路交叉口特征的决策树模型

众源车辆轨迹数据隐含最新的道路分布信息,研究利用轨迹数据提取道路特征有益于基础路网数据的快速建库与更新。道路网由交叉口和连接交叉口的道路线构成,其中交叉口特征识别是整个道路网生成的关键。由于缺乏精细的交叉口识别模型,轨迹数据生成的道路网容易出现路口遗漏、结构失真等现象。针对这一问题,本文提出一种利用轨迹数据提取道路交叉口的方法。首先,分析车辆在交叉口与非交叉口区域移动轨迹几何形态及隐含动力学特征的变化情形;然后,利用决策树方法构建轨迹片段分类模型,并结合移动开窗式的轨迹线剖分模型建立交叉口区域变道轨迹片段提取方法;最后,依据Hausdorff距离对交叉口区域轨迹片段进行聚类,并提取中心线获得完整的道路交叉口结构。采用真实的车辆轨迹线作为测试数据,验证了本文提出方法的有效性。     

An adaptive Markov chain algorithm applied over map-matching of vehicle trip GPS data

Markov chains have frequently been applied to match the probable routes with a set of GPS trip data that a pilot vehicle is emitting over a specific graph road network. This class of map- matching (MM) algorithms presently demonstrates and involve statistical and ad-hoc measures to drive the Markov chain transitional probabilities in picking the best route combinations constrained over the graph road network. In this study, we have devised an adaptive scheme to modify the Markov Chain (MC) kernel window as we move along the GPS samples to reduce the mistakes that can happen by the use of narrower MC widths. The measure for temporarily increasing the MC window width is chosen to be the ratio between the geodesic distance of current route to the actual geodesic distance between each pair of GPS samples. This adaptive use of MC has shown to have hardened the results significantly with tolerable computational cost increase. The details of the overall algorithm are depicted by the example routes extracted from various vehicle trips and the results are shown to validate the usefulness of the algorithm in practice.     

车辆轨迹信息的道路属性粗糙集定权模型

针对利用道路自身属性来计算重要度,并进行道路选取时产生的路网不连通、道路悬挂等问题,该文提出一种新的道路属性定权模型。结合轨迹大数据与道路自身属性,以路段为基本单位,选取道路长度、连通度、网络密度、车流量、平均车速、偏好系数6项属性作为道路重要性的衡量指标,采用基于牵引程度的离散化方法,实现指标数据的离散化,利用粗糙集确定属性权重。基于武汉市车辆的轨迹数据和道路数据的实验结果表明,该文模型计算得到的重要性值更符合实际情况,以该重要性进行道路选取时,有效地解决了路网中一些道路由于自身属性较小,使得重要度计算较小,导致道路选取时被舍弃,产生的路网不连通、道路悬挂等问题。     

A map-matching algorithm dealing with sparse cellular fingerprint observations

ABSTRACT

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.     

基于多准则融合的信任理论地图匹配算法

本文提出了一种基于多准则融合的地图匹配算法,该算法运用信任理论融合了多种评价准则并对匹配过程中的道路选择进行了综合评价。首先,利用车辆定位的当前和历史信息建立了邻近准则、夹角准则和历史轨迹邻近准则。然后,运用D-S证据组合规则,将三种评价准则进行融合应用于道路选择过程中。最后,借助连通性准则对融合的最终结果进行修正,提高了算法的精度。仿真结果表明,对于简单和复杂路网,该算法都能提供较高的匹配精度,识别出车辆行驶的道路。     

出租车轨迹数据快速提取道路骨架线方法

文章就如何利用出租车GPS轨迹数据提取道路信息展开研究:首先运用角平分线算法生成轨迹线的缓冲区,再利用点水平切割中点线法从缓冲区中提取道路骨架线的方法。该方法快速、简单、实用,对大多数道路的结构情况都能够很好地进行描述。最后利用昆明市出租车数据进行实验,并将实验结果与影像数据对比,证明提取的道路骨架线与影像吻合较好。     

机载LiDAR点云提取城市道路网的半自动方法

针对从卫星和航空正射影像中手工提取道路的方法速度慢的问题,该文基于机载LiDAR点云的高程和反射强度信息,手工选取一定量的种子点,采用区域生长法分割出初始道路区域;在填充空洞和平滑预处理后,利用细化算法提取出道路中线;根据长度阈值剔除毛刺短分支,使用自动算法和手工结合的方法消除多余通路和环形通路;并跟踪连接成矢量道路线,根据抽稀算法移除大量的小弯曲拐点,最后平滑矢量道路线成道路网。在测试复杂场景下的城市LiDAR点云后,发现提取的道路网的完整率和正确率较高。     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.     

面向低成本车载IMU的安装姿态估计

车载IMU相对于车体的安装姿态信息是应用车辆非完整约束的必需条件,而车辆非完整约束可以有效解决GNSS信号长时间中断的情形下低成本INS+GNSS组合导航系统精度降低的问题。本文针对车载场景下的低成本消费级IMU,基于卡尔曼滤波和粒子滤波提出了一种估计IMU安装姿态的算法。该算法无需限制IMU相对于车体的姿态为小角度;随后,基于仿真平台对低成本消费级IMU进行建模,利用生成的若干组不同安装姿态的IMU数据对算法进行验证;最后进行车载测试。仿真结果和车载测试结果都表明,该算法可以准确地估计IMU相对于车体的安装姿态,对于低成本INS+GNSS组合导航系统精度的提高具有实际意义。