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ThinkMind // ICN 2020, The Nineteenth International Conference on Networks // View article icn_2020_1_20_30035


A Vehicle Position Estimation Method Combining Roadside Vehicle Detector and In-Vehicle Sensors

Authors:
Shunya Yamada
Yousuke Watanabe
Hiroaki Takada

Keywords: Sensor fusion, Position estimation, Communication delays, DSRC, Intelligent transportation system

Abstract:
To improve highway traffic safety and traffic flow, it is important to properly manage merging at junctions. Accurate vehicle positions and velocities are necessary to achieve this, but existing sensors have both advantages and disadvantages. Road-side vehicle detectors are very accurate, but only available at fixed points. By contrast, in-vehicle Global Navigation Satellite System (GNSS) sensors can be used anywhere except in tunnels, but are less accurate. However these sensors can compensate for each other’s weak points. In this paper, we proposed a vehicle position estimation method that combines roadside vehicle detector and in-vehicle sensors. This gathers data from roadside vehicle detector and in-vehicle sensors via different wireless networks, applies Kalman filters to calculate accurate position and velocity. When exchanging information over wireless networks, communication delays occur and data arrival sequence is not guaranteed. Our method can retroactively calculate vehicle position in the presence of delays below a maximum acceptable threshold. The results of simulation experiments show that our method can estimate vehicle positions more accurately than using data from either sensor alone.

Pages: 7 to 14

Copyright: Copyright (c) IARIA, 2020

Publication date: February 23, 2020

Published in: conference

ISSN: 2308-4413

ISBN: 978-1-61208-770-2

Location: Lisbon, Portugal

Dates: from February 23, 2020 to February 27, 2020

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