The Library
Classifying vehicle activity to improve point of interest extraction
Tools
Tools
Tools
Van Hinsbergh, James, Griffiths, Nathan, Taylor, Phillip M., Thomason, Alasdair, Xu, Z. and Mouzakitis, A. (2021) Classifying vehicle activity to improve point of interest extraction. Mobile Information Systems, 2021 . 9973681. doi:10.1155/2021/9973681 ISSN 1574-017X.
|
PDF
WRAP-Classifying-vehicle-activity-improve-point-of-interest-extraction-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1922Kb) | Preview |
|
![[img]](https://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
dcs-010921-wrap--vehiclepoi.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (548Kb) |
Official URL: https://doi.org/10.1155/2021/9973681
Abstract
Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
|||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Location-based services, Global Positioning System -- Data processing, Automobiles -- Navigation systems | |||||||||
| Journal or Publication Title: | Mobile Information Systems | |||||||||
| Publisher: | Hindawi Limited | |||||||||
| ISSN: | 1574-017X | |||||||||
| Official Date: | 3 September 2021 | |||||||||
| Dates: |
|
|||||||||
| Volume: | 2021 | |||||||||
| Article Number: | 9973681 | |||||||||
| DOI: | 10.1155/2021/9973681 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Copyright Holders: | Copyright © 2021 James Van Hinsbergh et al. | |||||||||
| Date of first compliant deposit: | 1 September 2021 | |||||||||
| Date of first compliant Open Access: | 13 September 2021 | |||||||||
| Funder: | This work was supported by Jaguar Land Rover and the UK-EPSRC grant EP/N012380/1 as part of the jointly funded Towards Autonomy: Smart and Connected Control (TASCC) Programme. | |||||||||
| RIOXX Funder/Project Grant: |
|
|||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
