A Data Correction Algorithm for Low-Frequency Floating Car Data.
TL;DR: A data correction algorithm for low-frequency floating car data is proposed using an adaptive density optimizing method to remove the noise points with large mistakes and an efficient hierarchical map matching algorithm is used.
Abstract: The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.
Citations
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TL;DR: The authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis (DST-MM), which outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.
Abstract: Global positioning system (GPS) trajectory map matching projects GPS coordinates to the road network. Most existing algorithms focus on the geometric and topological relationships of the road network, while did not make full use of the historical road network information and floating car data. In this study, the authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis (DST-MM). The algorithm mainly focused on the following three aspects: (i) analyse the spatial relevancy from the prospective of geometric analysis, topology analysis and intersection analysis; (ii) to make full use of the historical and real-time data, a deep learning model was conducted to extract the road network and vehicle trajectory features and (iii) establish a speed prediction model and nest it in the temporal analysis structure. It narrows down the path search range through establishing the dynamic candidate graph. Experimental results show that the proposed DST-MM algorithm outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.
11 citations
TL;DR: New algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts using internal information and a correction algorithm based on signals from precession angle sensors are presented.
Abstract: This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.
7 citations
Cites background from "A Data Correction Algorithm for Low..."
...During the operation time of autonomous INS with sufficiently long intervals, errors can reach unacceptably large values [10]....
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DOI•
20 Oct 2015
Abstract: The paper presents a map-matching method which mainly considers the curvature integral value of the curve as a map-matching characteristic for constraining the associated matching between two adjacent GPS track points.Through the implementation of map matching experiments for floating car data on the different conditions of both route categories and sampling intervals,the proposed curvature integration constrained mapmatching method could be superior to the classic floating car map matching method when evaluating them by the matching accuracy and stability.
3 citations
01 Jan 2021
TL;DR: In this article, the state-of-the-art molybdenum disulphide (MoS2)-based nanostructures have been investigated for energy and biomedical applications.
Abstract: Owing to the fascinating structural, optical, electrical, chemical properties, graphene has created new paradigm in the field of nanoscience and the common crystalline structures that can be exfoliated include the layered van der Waals (vdW) solids such as boron nitride, transition metal dichalcogenides (TMDCs), black phosphorus, and the layered ionic solids. Here, we bring forth the state-of-art-of materials dominated by their two-dimensional (2D) geometry beyond graphene. Being one of the most well-studied families of vdW layered materials, molybdenum disulphide (MoS2) belonging to TMDC family has gained considerable research interest. The present work is focused on attempts to optimize and characterize this material with unique properties for a host of applications. The work resolves the hydrothermal growth of hexagonal MoS2 nanoflakes with attracting optical and magnetic properties providing strong evidence for the spin orbit split valence bands of these nanostructures. The enhanced electrocatalytic activity, excitation wavelength dependent down-conversion and up-conversion photoluminescence, growth of structural polymorphs using simple hydrothermal method, and the efficient anticancer properties of MoS2 nanostructures providing greater insight into energy and biomedical applications are also discussed. The improved catalytic activity of MoS2-based nanostructures reveals the increasing number of accessible active sites, formation of large surface area and is greatly beneficial for accomplishing a clean, environmental-friendly, inexpensive hydrogen mission for energy storage and conversion applications. The synergistic effect of the MoS2 nanocomposites was able to impede angiogenesis, tumor growth, and epithelial to mesenchymal transition, elucidating the anticancer efficacy. Understanding and exploiting such unique properties of these 2D materials paves new horizons toward novel technological advances in electronic and medical field.
3 citations
09 Dec 2020
1 citations
References
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01 Jan 2001
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Abstract: Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.
6,574 citations
11 Jun 2007
TL;DR: A new partition-and-group framework for clustering trajectories is proposed, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster, and a trajectory clustering algorithm TRACLUS is developed, which discovers common sub-trajectories from real trajectory data.
Abstract: Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.
1,387 citations
01 Jan 2001
TL;DR: In this article, Rao-Blackwellised particle filters (RBPFs) were proposed to increase the efficiency of particle filtering, using a technique known as Rao-blackwellisation.
Abstract: Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.
1,231 citations
04 Nov 2009
TL;DR: The results show that the ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories and when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.
Abstract: Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.
817 citations
"A Data Correction Algorithm for Low..." refers methods in this paper
...Map matching algorithms for low-frequency data include both local and global algorithms [17]....
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...We choose an ST-matchi g algorithm to match these points [17,28]....
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...We choose an ST-matching algorithm to match these points [17,28]....
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...In this section, we propose hierarchical map matching (HST-Matching) method, by improving the ST-matching algorithm [17], to match low-frequency floating car data with the OSM map....
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...Compared to the synthetic trajectory data used in reference [17], it is more reliable....
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TL;DR: A statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process by bounding the approximation error introduced by the sample-based representation of the particle filter.
Abstract: Over the past few years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process. The key idea of the KLD-sampling method is to bound the approximation error introduced by the sample-based representation of the particle filter. The name KLD-sampling is due to the fact that we measure the approximation error using the Kullback-Leibler distance. Our adaptation approach chooses a small number of samples if the density is focused on a small part of the state space, and it chooses a large number of samples if the state uncertainty is high. Both the implementation and computation overhead of this approach are small. Extensive experiments using mobile robot localization as a test application show that our approach yields drastic improvements over particle filters with fixed sample set sizes and over a previously...
731 citations