AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin
For many people, driving a vehicle is an indispensable part of everyday life. However, sometimes everyday life does not go as expected, as a lot of accidents happen on the public roads. Most of the accidents are due to inattentive driver behavior. Modern driver monitoring systems evaluate driver beh...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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FRUCT
2020-09-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/fruct27/files/Lin.pdf |
| _version_ | 1818436911653453824 |
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| author | Friedrich Lindow Alexey Kashevnik Christian Kaiser Alexander Stocker |
| author_facet | Friedrich Lindow Alexey Kashevnik Christian Kaiser Alexander Stocker |
| author_sort | Friedrich Lindow |
| collection | DOAJ |
| description | For many people, driving a vehicle is an indispensable part of everyday life. However, sometimes everyday life does not go as expected, as a lot of accidents happen on the public roads. Most of the accidents are due to inattentive driver behavior. Modern driver monitoring systems evaluate driver behavior by means of distinctive sensor technology and, if necessary, indicate undesirable driving behavior. However, many roadworthy vehicles do not have the possibility to implement such systems. Therefore, it seems to be interesting to investigate the implementation of such systems on the basis of commodity hardware, e.g. smartphones, because nowadays almost every driver has a powerful smartphone equipped with many sensors. Furthermore, advances in machine learning made it possible to analyze large amounts of data and to generate new conclusions. This work is dedicated to the topic of how machine learning can be used for driver behavior recognition by improving an already existing driver monitoring system with machine learning techniques. We propose to use Microsoft Azure platform to analyze the data generated by a Driver Monitoring System. |
| first_indexed | 2024-12-14T17:16:18Z |
| format | Article |
| id | doaj.art-3d0e718dad8d4e26b49338637e2131e2 |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-14T17:16:18Z |
| publishDate | 2020-09-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-3d0e718dad8d4e26b49338637e2131e22022-12-21T22:53:25ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372020-09-0127111612510.23919/FRUCT49677.2020.9211020AI-Based Driving Data Analysis for Behavior Recognition in Vehicle CabinFriedrich Lindow0Alexey Kashevnik1Christian Kaiser2Alexander Stocker3Universitat Rostock, GermanySPIIRAS, RussiaVirtual Vehicle Research GmbH, GermanyVirtual Vehicle Research GmbH, GermanyFor many people, driving a vehicle is an indispensable part of everyday life. However, sometimes everyday life does not go as expected, as a lot of accidents happen on the public roads. Most of the accidents are due to inattentive driver behavior. Modern driver monitoring systems evaluate driver behavior by means of distinctive sensor technology and, if necessary, indicate undesirable driving behavior. However, many roadworthy vehicles do not have the possibility to implement such systems. Therefore, it seems to be interesting to investigate the implementation of such systems on the basis of commodity hardware, e.g. smartphones, because nowadays almost every driver has a powerful smartphone equipped with many sensors. Furthermore, advances in machine learning made it possible to analyze large amounts of data and to generate new conclusions. This work is dedicated to the topic of how machine learning can be used for driver behavior recognition by improving an already existing driver monitoring system with machine learning techniques. We propose to use Microsoft Azure platform to analyze the data generated by a Driver Monitoring System.https://www.fruct.org/publications/fruct27/files/Lin.pdfdriving data analysisdriver monitoringneural networkartificial intelligence |
| spellingShingle | Friedrich Lindow Alexey Kashevnik Christian Kaiser Alexander Stocker AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin Proceedings of the XXth Conference of Open Innovations Association FRUCT driving data analysis driver monitoring neural network artificial intelligence |
| title | AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin |
| title_full | AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin |
| title_fullStr | AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin |
| title_full_unstemmed | AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin |
| title_short | AI-Based Driving Data Analysis for Behavior Recognition in Vehicle Cabin |
| title_sort | ai based driving data analysis for behavior recognition in vehicle cabin |
| topic | driving data analysis driver monitoring neural network artificial intelligence |
| url | https://www.fruct.org/publications/fruct27/files/Lin.pdf |
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