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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data

Abstrakt

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.

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Autorzy (7)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
IEEE Access nr 11, strony 8727 - 8733,
ISSN: 2169-3536
Język:
angielski
Rok wydania:
2023
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/access.2023.3239315
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

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