Vehicular cooperative map matching (original) (raw)
Related papers
In this paper a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step we introduce our cooperative map matching method. This map matching method uses the V2V communication in a VANET to exchange GPS information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that we have proposed the concept of a dynamic base station DGPS (DDGPS) which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections which can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method which aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated, based on the receiver’s belief on its positioning and its uncertainty, and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.
A Practical Map-matching Algorithm for GPS-based Vehicular Networks in Shanghai Urban Area
Currently, vehicular ad hoc networks (VANET) has been paid much attention. Also, we utilize 4000 taxis and 1000 buses equipped with GPS-based mobile sensors in Shanghai city, which constitute a GPS-based vehicular networks if we assume that vehicles can communicate with each other. Unfortunately, these sensors are set with long sampling interval by the taxi or bus companies, such as 1-2 minutes. In order to analyze the characteristics of this vehicular networks, such as connectivity, effective networking protocol and mobility model, first we need to get the real trace of vehicles based on sparse GPS data we received due to the long sampling interval. In this paper, we are interested in developing a practical map-matching algorithm because of the well-known error of GPS data. Two algorithms for mapmatching (NMA and EMA) are proposed based on the distance and angle factors. These algorithms are verified by the field test. The testing result shows that a practical mapmatching algorithm EMA can work fairly well based on these imperfect data made available by the GPS-based sensor in Shanghai urban area.
Vehicle localization in VANETs using data fusion and V2V communication
2012
In Vehicular Ad-hoc Networks (VANETs), one of the challenging issues is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-tovehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only can assure the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures.
Comparative Study and Application-Oriented Classification of Vehicular Map-Matching Methods
IEEE Intelligent Transportation Systems Magazine, 2018
Vehicular map-matching aims to identify the position of a vehicle in a road-network. Different map-matching applications make use of different formulations of the problem leading to some ambiguous usage. This work intends to make a step towards resolution of this issue. We cross-link mapmatching methods, classes and applications in order to provide insight into this area of study. A selection of current map-matching methods is reviewed, classified and appropriately linked with other methods. Conclusions on trends and influential ideas are drawn with respect to the specific flavor of individual applications. Finally, the trade-offs that must be considered when selecting a map-matching method are discussed and selection guidelines are provided. Positioning System Map Map-Matcher Path Planning Location Based Services Fleet Management FIg 1 Map-matching converts raw position to position on the map (originally printed in [35]).
Characterizing Cooperative Positioning in VANET
cse.unsw.edu.au
Cooperative positioning (CP), a localization means that has been widely used in wireless sensor networks, is emerging as one of the promising solutions for improving the vehicular positioning in VANET. CP allows each individual node in the network to calibrate its own position, by leveraging distance measurements between neighbor nodes with unknown or estimated (e.g., from GPS) positions. While concepts of applying CP in VANET have been introduced, the actual performance of CP in real-world vehicular communication scenarios is largely unknown. In this paper, we bridge this gap by conducting a comprehensive simulation study to characterize the performance of CP in realistic VANET environments. We investigate the efficacy of CP under the effects of various communication factors exhibiting in VANET. Further, we analyze the performance of CP with respect to the constraints from underlying non-cooperative vehicular localization techniques.
Localization in vehicular ad hoc networks using data fusion and V2V communication
Computer Communications, 2015
In Vehicular ad-hoc networks (VANETs), one of the challenging tasks is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-to-vehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only assures the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures. Moreover, further improvement has been achieved by estimating the vehicle prior (prior mean and covariance) using unscented transform (UT) together with sequential decentralized extended Kalman filtering.
2017
a Vehicular Ad-Hoc Network or VANET is a technology that has moving vehicles as nodes in a network for creating a mobile network. Vehicular networks are very fast emerging for deploying and developing new and traditional applications. It is characterized by rapidly changing topology, high mobility, and ephemeral, one-time interactions. Our goal is to provide the better algorithm for solution to the inaccurate positioning due to signal loss in GPS locators. We propose our novel work technique in the domain of Navigation in VANET. Recently, GPS handsets have been widely adopted by drivers for navigation. People rely more and more on GPS, and many of them stop checking and printing maps before they depart with a full confidence in GPS. However, due to the sensibility of GPS signals to terrain, vehicles cannot get their locations. To address the issue, we propose a novel Grid-based Onroad localization system, where vehicles with and without accurate GPS signals self-organize into a VANE...
A Study of Pre-Processing Technique for Map-Matching Schemes of GPS-Enabled Vehicles
International Journal of Computing, 2017
This study towards the Map-Matching process that is useful to align a location of Global Positioning System (GPS) of vehicles on the digital road networks. Today’s GPS-enabled vehicles in developed countries generate a big volume of GPS data. On the other hand, the development of new roads in the city enables the road network very complex and difficult to match the vehicles’ location. So therefore, different techniques (i.e., pre-processing techniques) may be applied before the map-matching process is a recent concern of the Intelligent Transport System (ITS) research community. In this paper, we introduce the pre-processing technique; splitting the road network graph and processing the Single Source Shortest Path (SSSP) in synchronize parallel processing in the Hadoop environment. The proposed technique enables the map-matching schemes efficient to align the GPS points on the digital road networks. In the experimental work, the results of the map-matching schemes (i.e., found in th...
2018 15th Workshop on Positioning, Navigation and Communications (WPNC), 2018
Future applications of Cooperative - Intelligent Transportation Systems (C-ITS) will require accurate and reliable localization capabilities in a variety of harsh operating contexts. In this paper, we account for proof-of-concept field validations of a cooperative localization approach suitable to GPS-enabled Vehicular Ad Hoc Networks (VANETs), which relies on Vehicle-to-Vehicle (V2V) communications (e.g., over ITS-G5) and Impulse Radio - Ultra Wideband (IR-UWB) V2V ranging measurements. First, we evaluate 1-D V2V ranging accuracy on a highway in real mobility conditions. Then, in the same environment, we evaluate the positive impact of cooperation on positioning (i.e., in comparison with standalone standard GPS) in the steady-state fusion regime. Finally, investigating the impact of erroneous initialization and full GPS denial conditions, we illustrate the resilience of the proposed solution, before discussing the limitations of the current evaluation setting.
Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks
Sensors
In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles’ position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in di...