Abstract
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 distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm.
Highlights
Vehicular ad hoc networks (VANets) require precise localization information, mainly in critical safety-based applications, such as driverless vehicles and blind crossing [1]
In [18] the authors show a proof of concept study that uses vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication as well as an extended Kalman filter (EKF) to perform the fusion of time of arrival (TOA) measurements, inertial measurement unit (IMU), and map information to localize a vehicle in global positioning system (GPS)-denied environments
We have proposed an improvement to the BOuND algorithm, named cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS position of nearby vehicles and minimize their errors through an extended
Summary
Vehicular ad hoc networks (VANets) require precise localization information, mainly in critical safety-based applications, such as driverless vehicles and blind crossing [1]. The accuracy of GPS information can be affected by dense urban areas, such as urban street canyons and indoor parking lots, because of the absence of direct satellite visibility, which turns the GPS into an inaccurate instrument to provide precise location information [5] To tackle this drawback, there are some solutions proposed in the literature that use anchor nodes [5]. These approaches benefit from using vehicle-to-vehicle communication (V2V), in which nearby nodes exchange information about their positions and the relative distance between them and their neighbors [7,8] Another known technique used to decrease localization error is data fusion [9], which combines location information from different sources to generate a more precise result.
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