Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map

Inexpensive implementation of localization and environment mapping are critical issues for urban autonomous driving. We present a practical and low-cost navigation architecture to fuse different data from vehicle onboard sensors and estimate the vehicle state when individual observations such as GPS...

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Main Authors: Hossein Tehrani Niknejad, Mita Seiichi, Han Long, Huy Quoc
Format: Article
Language:English
Published: Society of Automotive Engineers of Japan, Inc. 2012-01-01
Series:International Journal of Automotive Engineering
Online Access:https://www.jstage.jst.go.jp/article/jsaeijae/3/1/3_20124024/_article/-char/en
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author Hossein Tehrani Niknejad
Mita Seiichi
Han Long
Huy Quoc
author_facet Hossein Tehrani Niknejad
Mita Seiichi
Han Long
Huy Quoc
author_sort Hossein Tehrani Niknejad
collection DOAJ
description Inexpensive implementation of localization and environment mapping are critical issues for urban autonomous driving. We present a practical and low-cost navigation architecture to fuse different data from vehicle onboard sensors and estimate the vehicle state when individual observations such as GPS are noisy. We are trying to compensate the GPS errors by data fusion from different sensors in a probabilistic way and a particle filter with joint observations model has been proposed. We have evaluated the feasibility of proposed localization and navigation architecture for fully autonomous driving by doing many experiments in our campus including up and down slopes.
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spelling doaj.art-7940c91087a4492bad5da27a8aa8c23d2023-12-28T02:01:30ZengSociety of Automotive Engineers of Japan, Inc.International Journal of Automotive Engineering2185-09922012-01-0131192510.20485/jsaeijae.3.1_19Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise MapHossein Tehrani Niknejad0Mita Seiichi1Han Long2Huy Quoc3Toyota Technological InstituteToyota Technological InstituteToyota Technological InstituteToyota Technological InstituteInexpensive implementation of localization and environment mapping are critical issues for urban autonomous driving. We present a practical and low-cost navigation architecture to fuse different data from vehicle onboard sensors and estimate the vehicle state when individual observations such as GPS are noisy. We are trying to compensate the GPS errors by data fusion from different sensors in a probabilistic way and a particle filter with joint observations model has been proposed. We have evaluated the feasibility of proposed localization and navigation architecture for fully autonomous driving by doing many experiments in our campus including up and down slopes.https://www.jstage.jst.go.jp/article/jsaeijae/3/1/3_20124024/_article/-char/en
spellingShingle Hossein Tehrani Niknejad
Mita Seiichi
Han Long
Huy Quoc
Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map
International Journal of Automotive Engineering
title Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map
title_full Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map
title_fullStr Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map
title_full_unstemmed Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map
title_short Multi-Sensor Data Fusion for Autonomous Vehicle Navigation and Localization through Precise Map
title_sort multi sensor data fusion for autonomous vehicle navigation and localization through precise map
url https://www.jstage.jst.go.jp/article/jsaeijae/3/1/3_20124024/_article/-char/en
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AT mitaseiichi multisensordatafusionforautonomousvehiclenavigationandlocalizationthroughprecisemap
AT hanlong multisensordatafusionforautonomousvehiclenavigationandlocalizationthroughprecisemap
AT huyquoc multisensordatafusionforautonomousvehiclenavigationandlocalizationthroughprecisemap