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An innovative multi-sensor fusion algorithm to enhance positioning accuracy of an instrumented bicycle

An innovative multi-sensor fusion algorithm to enhance positioning accuracy of an instrumented bicycle
An innovative multi-sensor fusion algorithm to enhance positioning accuracy of an instrumented bicycle
Cycling is an increasingly popular mode of travel in cities, but its poor safety record currently acts as a hurdle to its wider adoption as a real alternative to the private car. A particular source of hazard appears to originate from the interaction of cyclists with motorized traffic at low speeds in urban areas. But while technological advances in recent years have resulted in numerous attempts at systems for preventing cyclist-vehicle collisions, these have generally encountered the challenge of accurate cyclist localization. This paper addresses this challenge by introducing an innovative bicycle localization algorithm, which is derived from the geometrical relationships and kinematics of bicycles. The algorithm relies on the measurement of a set of kinematic variables (such as yaw, roll, and steering angles) through low-cost on-board sensors. It then employs a set of Kalman filters to predict-correct the direction and position of the bicycle and fuse the measurements in order to improve positioning accuracy. The capabilities of the algorithm are then demonstrated through a real-world field experiment using an instrumented bicycle, called "iBike'', in an urban environment. The results show that the proposed fusion achieves considerably lower positioning errors than that would be achieved based on dead-reckoning alone, which makes the algorithm a credible basis for the development of future collision warning and avoidance systems.
1524-9050
1145-1153
Miah, S.
b62bf122-183e-4791-a6cb-fed954944c37
Milonidis, E.N.
8f7ef890-17f5-43b5-8ba4-f446ea715e5c
Kaparias, I.
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Karcanias, N.
33be7cfb-e08d-45d9-958d-71105028d1e1
Miah, S.
b62bf122-183e-4791-a6cb-fed954944c37
Milonidis, E.N.
8f7ef890-17f5-43b5-8ba4-f446ea715e5c
Kaparias, I.
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Karcanias, N.
33be7cfb-e08d-45d9-958d-71105028d1e1

Miah, S., Milonidis, E.N., Kaparias, I. and Karcanias, N. (2020) An innovative multi-sensor fusion algorithm to enhance positioning accuracy of an instrumented bicycle. IEEE Transactions on Intelligent Transportation Systems, 21 (3), 1145-1153. (doi:10.1109/TITS.2019.2902797).

Record type: Article

Abstract

Cycling is an increasingly popular mode of travel in cities, but its poor safety record currently acts as a hurdle to its wider adoption as a real alternative to the private car. A particular source of hazard appears to originate from the interaction of cyclists with motorized traffic at low speeds in urban areas. But while technological advances in recent years have resulted in numerous attempts at systems for preventing cyclist-vehicle collisions, these have generally encountered the challenge of accurate cyclist localization. This paper addresses this challenge by introducing an innovative bicycle localization algorithm, which is derived from the geometrical relationships and kinematics of bicycles. The algorithm relies on the measurement of a set of kinematic variables (such as yaw, roll, and steering angles) through low-cost on-board sensors. It then employs a set of Kalman filters to predict-correct the direction and position of the bicycle and fuse the measurements in order to improve positioning accuracy. The capabilities of the algorithm are then demonstrated through a real-world field experiment using an instrumented bicycle, called "iBike'', in an urban environment. The results show that the proposed fusion achieves considerably lower positioning errors than that would be achieved based on dead-reckoning alone, which makes the algorithm a credible basis for the development of future collision warning and avoidance systems.

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Miah et al - T-ITS paper - Accepted Manuscript
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More information

Accepted/In Press date: 21 February 2019
e-pub ahead of print date: 19 March 2019
Published date: 1 March 2020

Identifiers

Local EPrints ID: 429304
URI: http://eprints.soton.ac.uk/id/eprint/429304
ISSN: 1524-9050
PURE UUID: 93e48c98-e062-4b62-8407-c20165670495
ORCID for I. Kaparias: ORCID iD orcid.org/0000-0002-8857-1865

Catalogue record

Date deposited: 26 Mar 2019 17:30
Last modified: 07 Oct 2020 02:14

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