Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks
Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/8/2624 |
| _version_ | 1811263020741951488 |
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| author | Sara Hernández Sánchez Rubén Fernández Pozo Luis A. Hernández Gómez |
| author_facet | Sara Hernández Sánchez Rubén Fernández Pozo Luis A. Hernández Gómez |
| author_sort | Sara Hernández Sánchez |
| collection | DOAJ |
| description | Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%. |
| first_indexed | 2024-04-12T19:37:26Z |
| format | Article |
| id | doaj.art-35c28057ec8d4b2b95b8415fe73ebf72 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:37:26Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-35c28057ec8d4b2b95b8415fe73ebf722022-12-22T03:19:11ZengMDPI AGSensors1424-82202018-08-01188262410.3390/s18082624s18082624Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural NetworksSara Hernández Sánchez0Rubén Fernández Pozo1Luis A. Hernández Gómez2Grupo de Aplicaciones de Procesado de Señales (GAPS), Universidad Politécnica de Madrid, 28040 Madrid, SpainGrupo de Aplicaciones de Procesado de Señales (GAPS), Universidad Politécnica de Madrid, 28040 Madrid, SpainGrupo de Aplicaciones de Procesado de Señales (GAPS), Universidad Politécnica de Madrid, 28040 Madrid, SpainCharacterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.http://www.mdpi.com/1424-8220/18/8/2624driving characterizationvehicle movement direction (VMD)accelerometersDeep LearningCNNGRUt-SNEPCA |
| spellingShingle | Sara Hernández Sánchez Rubén Fernández Pozo Luis A. Hernández Gómez Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks Sensors driving characterization vehicle movement direction (VMD) accelerometers Deep Learning CNN GRU t-SNE PCA |
| title | Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks |
| title_full | Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks |
| title_fullStr | Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks |
| title_full_unstemmed | Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks |
| title_short | Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks |
| title_sort | estimating vehicle movement direction from smartphone accelerometers using deep neural networks |
| topic | driving characterization vehicle movement direction (VMD) accelerometers Deep Learning CNN GRU t-SNE PCA |
| url | http://www.mdpi.com/1424-8220/18/8/2624 |
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