Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| author | Khandakar, Amith Michelson, David G. Naznine, Mansura Salam, Abdus Nahiduzzaman, Md. Khan, Khaled M. Suganthan, Ponnuthurai Nagaratnam Ayari, Mohamed Arselene Menouar, Hamid Haider, Julfikar |
| author_facet | Khandakar, Amith Michelson, David G. Naznine, Mansura Salam, Abdus Nahiduzzaman, Md. Khan, Khaled M. Suganthan, Ponnuthurai Nagaratnam Ayari, Mohamed Arselene Menouar, Hamid Haider, Julfikar |
| contents | Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns. This study addresses this gap by introducing a comprehensive dataset derived from smartphone sensors, which surpasses existing datasets by incorporating a diverse range of sensors including accelerometer, gyroscope, magnetometer, GPS, gravity, orientation, and uncalibrated sensors. These sensors capture extensive parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed, providing a detailed understanding of road conditions and driving behaviors. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning. By making this dataset available to the community, the study aims to foster collaboration, inspire further research, and facilitate the development of innovative solutions in intelligent transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review Khandakar, Amith Michelson, David G. Naznine, Mansura Salam, Abdus Nahiduzzaman, Md. Khan, Khaled M. Suganthan, Ponnuthurai Nagaratnam Ayari, Mohamed Arselene Menouar, Hamid Haider, Julfikar Robotics Artificial Intelligence Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns. This study addresses this gap by introducing a comprehensive dataset derived from smartphone sensors, which surpasses existing datasets by incorporating a diverse range of sensors including accelerometer, gyroscope, magnetometer, GPS, gravity, orientation, and uncalibrated sensors. These sensors capture extensive parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed, providing a detailed understanding of road conditions and driving behaviors. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning. By making this dataset available to the community, the study aims to foster collaboration, inspire further research, and facilitate the development of innovative solutions in intelligent transportation systems. |
| title | Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2411.07315 |