A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data
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...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10024968/ |
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| author | Mussadiq Abdul Rahim Sultan Daud Khan Salabat Khan Muhammad Rashid Rafi Ullah Hanan Tariq Stanislaw Czapp |
| author_facet | Mussadiq Abdul Rahim Sultan Daud Khan Salabat Khan Muhammad Rashid Rafi Ullah Hanan Tariq Stanislaw Czapp |
| author_sort | Mussadiq Abdul Rahim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eaa220c72b6e453e93a6c4231a48aef9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eaa220c72b6e453e93a6c4231a48aef92025-02-06T00:00:14ZengIEEEIEEE Access2169-35362023-01-01118727873310.1109/ACCESS.2023.323931510024968A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only DataMussadiq Abdul Rahim0https://orcid.org/0000-0003-4329-3512Sultan Daud Khan1https://orcid.org/0000-0002-7406-8441Salabat Khan2https://orcid.org/0000-0003-1470-0529Muhammad Rashid3Rafi Ullah4https://orcid.org/0000-0002-3908-5043Hanan Tariq5Stanislaw Czapp6https://orcid.org/0000-0002-1341-8276Department of Computer Science, National University of Technology (NUTECH), Islamabad, PakistanDepartment of Computer Science, National University of Technology (NUTECH), Islamabad, PakistanCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaDepartment of Computer Science, National University of Technology (NUTECH), Islamabad, PakistanDepartment of Computer Science, National University of Technology (NUTECH), Islamabad, PakistanFaculty of Electrical and Control Engineering, Gdańsk University of Technology, Gdańsk, PolandFaculty of Electrical and Control Engineering, Gdańsk University of Technology, Gdańsk, PolandWhether 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.https://ieeexplore.ieee.org/document/10024968/Advance driver assistance systemsCNNdeep learningGPS datanaturalistic drivingspatio–temporal window analysis |
| spellingShingle | Mussadiq Abdul Rahim Sultan Daud Khan Salabat Khan Muhammad Rashid Rafi Ullah Hanan Tariq Stanislaw Czapp A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data IEEE Access Advance driver assistance systems CNN deep learning GPS data naturalistic driving spatio–temporal window analysis |
| title | A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data |
| title_full | A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data |
| title_fullStr | A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data |
| title_full_unstemmed | A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data |
| title_short | A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data |
| title_sort | novel spatio x2013 temporal deep learning vehicle turns detection scheme using gps only data |
| topic | Advance driver assistance systems CNN deep learning GPS data naturalistic driving spatio–temporal window analysis |
| url | https://ieeexplore.ieee.org/document/10024968/ |
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