Abstract

In Intelligent Transportation Systems (ITS), the Vehicular Ad Hoc Networks (VANETs) paradigm based on the WAVE IEEE 802.11p standard is the main alternative for inter-vehicle communications. Recently, many protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Most of these services rely on location systems and require different levels of accuracy for their full operation. The Global Positioning System (GPS) is an off-the-shelf solution for localization in VANETs and ITS. However, GPS systems present problems regarding inaccuracy and unavailability in dense urban areas, multilevel roads, and tunnels, posing a challenge for protocols, applications, and services that rely on localization. With this motivation, we carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets, and regions around tunnels were selected. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capabilities, an integrated Dead Reckoning aided Geometric Dilution of Precision (GDOP)-based Cooperative Positioning solution was developed and evaluated. Leveraging the potential of vehicular sensors, such as odometers, gyroscopes, and digital compasses, vehicles share their positions and kinematics information using vehicular communication to improve their location estimations. With the assistance of a digital map, vehicles adjust the final estimated position using the road geometry. The situations of GPS unavailability characterized in the datasets were reproduced in a simulation environment to validate the proposed localization solution. The simulation results show average gains in Root Mean Square Error (RMSE) between 97% to 98% in comparison with the stand-alone GPS solution, and 83.00% to 88.00% against the GPS and Dead Reckoning (DR) only solution. The average absolute RMSE was reduced to the range of 3 to 5 m by vehicle. In addition, the proposed solution was shown to support 100% of the GPS unavailability zones on the evaluated scenarios.

Highlights

  • The advent of large urban centers and the evolution of traffic systems has improved the transportation of people, assets, and services

  • Green, magenta, and cyan represent, respectively, the trajectories and errors of the stand-alone Global Positioning System (GPS) solution (GPS), GPS integrated with Dead Reckoning (DR) and their sensors (GPS + DR), GPS

  • It is possible to observe that gyroscope random walk effect has a major contribution to the discrepancy in the DR estimative in the outage stage

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Summary

Introduction

The advent of large urban centers and the evolution of traffic systems has improved the transportation of people, assets, and services. Networks (VANETs) technology and the WAVE 802.11p standard are the main options regarding the capacity of real time processing and instantaneous inter-vehicle communication In this context, several applications, services, and protocols, ranging from comfort to security, rely on localization for complete functionality. GPS have the worst performance due to the non-line-of-sight (NLOS) with satellites, and the service becomes unavailable [2,4] These GPS features pose challenges for protocols, applications, and services that rely on localization. Sensors are already off-the-shelf devices in vehicles and will become too much more common in the following years, as shown in recent studies [8] In this regard, this study relies on low cost sensors (i.e., GPS, gyroscopes, digital compasses, and odometers) and the use of inter-vehicle communications to provide localization and improve accuracy in a cooperative way.

Related Work
Theoretical Foundations of Localization
Vehicle Positioning Sensors
Dead Reckoning
Multilateration
Integrated Dead Reckoning Aided Cooperative Positioning
Exchange Information Phase
GDOP-Based Positioning Estimation
Map Adjustment Using Road Geometry
Integrated Cooperative Positioning Algorithm
Applicability
Simulation Results
Characterization of GPS Problems from Real GPS Datasets
Simulation Setup
Positioning Results
Network Results
Conclusions and Future Work

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