Inertial Measurement Units-Based Probe Vehicles: Automatic Calibration, Trajectory Estimation, and Context Detection

Mustafa Mousa; Kapil Sharma; Christian G. Claudel

doi:10.1109/TITS.2017.2772080

Inertial Measurement Units-Based Probe Vehicles: Automatic Calibration, Trajectory Estimation, and Context Detection

Mustafa Mousa, Kapil Sharma, Christian G. Claudel

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Most probe vehicle data is generated using satellite navigation systems, such as the Global Positioning System (GPS), Globalnaya navigatsionnaya sputnikovaya Sistema (GLONASS), or Galileo systems. However, because of their high cost, relatively high position uncertainty in cities, and low sampling rate, a large quantity of satellite positioning data is required to estimate traffic conditions accurately. To address this issue, we introduce a new type of traffic monitoring system based on inexpensive inertial measurement units (IMUs) as probe sensors. IMUs as traffic probes pose unique challenges in that they need to be precisely calibrated, do not generate absolute position measurements, and their position estimates are subject to accumulating errors. In this paper, we address each of these challenges and demonstrate that the IMUs can reliably be used as traffic probes. After discussing the sensing technique, we present an implementation of this system using a custom-designed hardware platform, and validate the system with experimental data.

Original language	English (US)
Pages (from-to)	3133-3143
Number of pages	11
Journal	IEEE Transactions on Intelligent Transportation Systems
Volume	19
Issue number	10
DOIs	https://doi.org/10.1109/TITS.2017.2772080
State	Published - Dec 6 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Texas Department of Transportation through the Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas Project, under Grant 0-6838. The Associate Editor for this paper was S. Kong.

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10.1109/TITS.2017.2772080

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title = "Inertial Measurement Units-Based Probe Vehicles: Automatic Calibration, Trajectory Estimation, and Context Detection",

abstract = "Most probe vehicle data is generated using satellite navigation systems, such as the Global Positioning System (GPS), Globalnaya navigatsionnaya sputnikovaya Sistema (GLONASS), or Galileo systems. However, because of their high cost, relatively high position uncertainty in cities, and low sampling rate, a large quantity of satellite positioning data is required to estimate traffic conditions accurately. To address this issue, we introduce a new type of traffic monitoring system based on inexpensive inertial measurement units (IMUs) as probe sensors. IMUs as traffic probes pose unique challenges in that they need to be precisely calibrated, do not generate absolute position measurements, and their position estimates are subject to accumulating errors. In this paper, we address each of these challenges and demonstrate that the IMUs can reliably be used as traffic probes. After discussing the sensing technique, we present an implementation of this system using a custom-designed hardware platform, and validate the system with experimental data.",

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AU - Claudel, Christian G.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported by the Texas Department of Transportation through the Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas Project, under Grant 0-6838. The Associate Editor for this paper was S. Kong.

PY - 2017/12/6

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N2 - Most probe vehicle data is generated using satellite navigation systems, such as the Global Positioning System (GPS), Globalnaya navigatsionnaya sputnikovaya Sistema (GLONASS), or Galileo systems. However, because of their high cost, relatively high position uncertainty in cities, and low sampling rate, a large quantity of satellite positioning data is required to estimate traffic conditions accurately. To address this issue, we introduce a new type of traffic monitoring system based on inexpensive inertial measurement units (IMUs) as probe sensors. IMUs as traffic probes pose unique challenges in that they need to be precisely calibrated, do not generate absolute position measurements, and their position estimates are subject to accumulating errors. In this paper, we address each of these challenges and demonstrate that the IMUs can reliably be used as traffic probes. After discussing the sensing technique, we present an implementation of this system using a custom-designed hardware platform, and validate the system with experimental data.

AB - Most probe vehicle data is generated using satellite navigation systems, such as the Global Positioning System (GPS), Globalnaya navigatsionnaya sputnikovaya Sistema (GLONASS), or Galileo systems. However, because of their high cost, relatively high position uncertainty in cities, and low sampling rate, a large quantity of satellite positioning data is required to estimate traffic conditions accurately. To address this issue, we introduce a new type of traffic monitoring system based on inexpensive inertial measurement units (IMUs) as probe sensors. IMUs as traffic probes pose unique challenges in that they need to be precisely calibrated, do not generate absolute position measurements, and their position estimates are subject to accumulating errors. In this paper, we address each of these challenges and demonstrate that the IMUs can reliably be used as traffic probes. After discussing the sensing technique, we present an implementation of this system using a custom-designed hardware platform, and validate the system with experimental data.

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DO - 10.1109/TITS.2017.2772080

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JF - IEEE Transactions on Intelligent Transportation Systems

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Inertial Measurement Units-Based Probe Vehicles: Automatic Calibration, Trajectory Estimation, and Context Detection

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