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Abstract
Whether the computer is driving your car or you are, advanced driver assistance systems (ADAS) come into play on all levels, from weather monitoring to safety. These modern-day ADASs use various assisting tools for drivers to keep the journey safe; these sophisticated tools provide early signals of numerous events, such as road conditions, emerging traffic scenarios, and weather warnings. Many urban applications, such as car-sharing and logistics, rely on accurate and up-to-date road map data. Map generation methods use a variety of data sources, including but not limited to global positioning systems (GPS). In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. It outperforms the existing turn detection schemes on two major frontiers, the required data and the accuracy achieved in detecting different driving behaviors.
Citations
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2
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0
Web of Science
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4
Scopus
Authors (7)
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Mussadiq Abdul Rahim
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Sultan Daud Khan
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Salabat Khan
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Muhammad Rashid
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Rafi Ullah
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/ACCESS.2023.3239315
- License
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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IEEE Access
no. 11,
pages 8727 - 8733,
ISSN: 2169-3536 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Rahim M. A., Khan S. D., Khan S., Rashid M., Ullah R., Tariq H., Czapp S.: A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data// IEEE Access -Vol. 11, (2023), s.8727-8733
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/access.2023.3239315
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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