DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification
One of the key functions of driver monitoring systems is the evaluation of the driver’s state, which is a key factor in improving driving safety. Currently, such systems heavily rely on the technology of deep learning, that in turn requires corresponding high-quality datasets to achieve the required...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/7/12/181 |
| _version_ | 1797460709386223616 |
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| author | Walaa Othman Alexey Kashevnik Batol Hamoud Nikolay Shilov |
| author_facet | Walaa Othman Alexey Kashevnik Batol Hamoud Nikolay Shilov |
| author_sort | Walaa Othman |
| collection | DOAJ |
| description | One of the key functions of driver monitoring systems is the evaluation of the driver’s state, which is a key factor in improving driving safety. Currently, such systems heavily rely on the technology of deep learning, that in turn requires corresponding high-quality datasets to achieve the required level of accuracy. In this paper, we introduce a dataset that includes information about the driver’s state synchronized with the vehicle telemetry data. The dataset contains more than 17.56 million entries obtained from 633 drivers with the following data: the driver drowsiness and distraction states, smartphone-measured vehicle speed and acceleration, data from magnetometer and gyroscope sensors, g-force, lighting level, and smartphone battery level. The proposed dataset can be used for analyzing driver behavior and detecting aggressive driving styles, which can help to reduce accidents and increase safety on the roads. In addition, we applied the K-means clustering algorithm based on the 11 least-correlated features to label the data. The elbow method showed that the optimal number of clusters could be either two or three clusters. We chose to proceed with the three clusters to label the data into three main scenarios: parking and starting driving, driving in the city, and driving on highways. The result of the clustering was then analyzed to see what the most frequent critical actions inside the cabin in each scenario were. According to our analysis, an unfastened seat belt was the most frequent critical case in driving in the city scenario, while drowsiness was more frequent when driving on the highway. |
| first_indexed | 2024-03-09T17:09:54Z |
| format | Article |
| id | doaj.art-fd4e0fec57a041208a2643355d298038 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T17:09:54Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-fd4e0fec57a041208a2643355d2980382023-11-24T14:13:59ZengMDPI AGData2306-57292022-12-0171218110.3390/data7120181DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State IdentificationWalaa Othman0Alexey Kashevnik1Batol Hamoud2Nikolay Shilov3Information Technology and Programming Faculty, ITMO University, St. Petersburg 197101, RussiaInstitute of Mathematics and Information Technologies, Perozavodsk State University (PetrSU), Petrozavodsk 185035, RussiaInformation Technology and Programming Faculty, ITMO University, St. Petersburg 197101, RussiaSt. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, RussiaOne of the key functions of driver monitoring systems is the evaluation of the driver’s state, which is a key factor in improving driving safety. Currently, such systems heavily rely on the technology of deep learning, that in turn requires corresponding high-quality datasets to achieve the required level of accuracy. In this paper, we introduce a dataset that includes information about the driver’s state synchronized with the vehicle telemetry data. The dataset contains more than 17.56 million entries obtained from 633 drivers with the following data: the driver drowsiness and distraction states, smartphone-measured vehicle speed and acceleration, data from magnetometer and gyroscope sensors, g-force, lighting level, and smartphone battery level. The proposed dataset can be used for analyzing driver behavior and detecting aggressive driving styles, which can help to reduce accidents and increase safety on the roads. In addition, we applied the K-means clustering algorithm based on the 11 least-correlated features to label the data. The elbow method showed that the optimal number of clusters could be either two or three clusters. We chose to proceed with the three clusters to label the data into three main scenarios: parking and starting driving, driving in the city, and driving on highways. The result of the clustering was then analyzed to see what the most frequent critical actions inside the cabin in each scenario were. According to our analysis, an unfastened seat belt was the most frequent critical case in driving in the city scenario, while drowsiness was more frequent when driving on the highway.https://www.mdpi.com/2306-5729/7/12/181driver statedriving datasmartphone datavehicle telemetry |
| spellingShingle | Walaa Othman Alexey Kashevnik Batol Hamoud Nikolay Shilov DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification Data driver state driving data smartphone data vehicle telemetry |
| title | DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification |
| title_full | DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification |
| title_fullStr | DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification |
| title_full_unstemmed | DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification |
| title_short | DriverSVT: Smartphone-Measured Vehicle Telemetry Data for Driver State Identification |
| title_sort | driversvt smartphone measured vehicle telemetry data for driver state identification |
| topic | driver state driving data smartphone data vehicle telemetry |
| url | https://www.mdpi.com/2306-5729/7/12/181 |
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